social media influencers dissertation

2023 Theses Doctoral

A Relational View of Social Media Influencers and Audience Evaluations in Cultural Markets

Song, Youjin Jenna

Social media influencers are online content creators who seek audience support, form relationships with their audiences, and shape audience interests, opinions, and behavior. Not only is the influencer economy a cultural market in its own right, but it is also increasingly shaping other cultural industries as influencers serve as cultural gatekeepers who filter cultural products for their audiences (Hirsch, 1972). As social media influencers become powerful gatekeepers and producers, the relational nature of their activities and performance is reshaping norms in cultural industries overall. Lay audiences gain more influence and agency as their evaluations and support are critical for influencer survival and success. Then how has the evaluative landscape in cultural industries changed, and what are the broader social implications of these changes? In this dissertation, I outline the rise of social media influencers both as gatekeepers and producers in cultural markets and argue that scholarship in management and sociology must examine the unique ways in which these influencers’ activities differ from their predecessors’. Chapter 1 defines social media influencers and describes their activities and market dynamics by comparing and contrasting them to their traditional counterparts. Chapter 2 examines the relationship between influencer-audience relational interactions and performance outcomes, focusing on influencers’ role as cultural gatekeepers. Chapter 3 looks at the effects of race and race-based social movements on influencer-audience interactions, highlighting influencers’ role as cultural producers. In all three chapters, the focus is on the sociological theory of relational work, which is the process through which economic actors balance the transactional and social components of their relationships (Zelizer, 2012; Bandelj, 2020). Social media influencers serve as an ideal type to demonstrate how relational work contributes to market value and performance, given the tight balance between their relational and transactional activities. I use mixed quantitative and qualitative methods to analyze channel-, video-, and comment-level data on 1,167 BookTubers (YouTube influencers who evaluate books). I find that social media influencers indeed serve as trusted gatekeepers for their audiences, and that audiences demand not only evaluative content, but also intimate relationships with influencers. Influencers use audience interactions as relational work efforts to build trusting relationships with audiences and gain their support, but this effect is moderated by the perceived trustworthiness of the influencer. The effectiveness of influencer relational work also varies by race, and relational work thus reproduces racialized logics that underlie cultural markets. In sum, this dissertation not only highlights the rise of an important new market actor, but also contributes to theories on how relational work shapes and is shaped by inequality.

• Social media--Influence
• Internet personalities
• Internet marketing
• Economics--Sociological aspects
• Popular culture--Economic aspects

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Social Media Influencers: Exploratory Analysis on the Relationships Between Female Millennials and Influencers

Masters Thesis

https://repository.uel.ac.uk/item/87220

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How to quantify social media influencers: An empirical application at the Teatro alla Scala

Agostino deborah.

a Department of Management, Economics and Industrial Engineering, Politecnico di Milano Italy

Arnaboldi Michela

Calissano anna.

b MOX - Department of Mathematics, Politecnico di Milano Italy

A topic of primary importance for organizations is the ability to identify and appraise Social Media Influencers (SMIs), given their key role in affecting conversations and interactions on social media. According to the current research in this area, influencers make up a single category of social media users, but only limited attention has been paid concerning the extent to which they can exert their influence. In this study, the quantification and classification of SMIs is addressed by proposing an advanced methodology based on social network analysis - K-shell decomposition - together with a discussion on the relationship between the different SMI categories and the effect of each type of influencer on the public relation activity of an organization. The developed methodology was tested through an action research project conducted at the Teatro alla Scala of Milan, and the results were then discussed with the management of the opera house. The main finding of this work is that SMIs can be split into writers, authorities or spreaders on the basis of the kind of influence they exert, thereby delivering a precisely focused typology of SMIs. These findings enhance our academic knowledge on analytics applied to social science, while also providing a real case situation where managers make practical use of analytics.

1. Introduction

Social Media Influencers (SMIs) play a key role in affecting the way users interact on social media, and organizations have learnt to leverage on this group when they prepare their communication and public relations plans ( Freberg et al., 2011 ; Moreno et al., 2015 ; Li, 2016 ; Ge and Gretzel, 2018 ; Ong and Ito, 2019 ). SMIs represent “a new type of independent third party endorser who shape audience attitudes through blogs, tweets, and the use of other social media” ( Freberg et al., 2011 ).

With their audience looking up at them as credible sources of information, SMIs can provide valuable support to organizations, while equally being a potential menace ( Li, 2016 ; Ramadan, 2018 ; Ong and Ito, 2019 ). Influencers can promote a brand, enhancing an organization's popularity and becoming, in this way, part of the enterprise's social media strategy ( Booth and Matic, 2011 ; Ge and Gretzel, 2018 ). A number of companies already promote their brand through blogs posted by famous bloggers or artists who mention a particular product or label on social media, often in connection with experiences in their daily life. On the flip side, influencers can also represent a hazard for organizations, when they point out bad results or negative situations involving the enterprise or, even worse, when they pass on false information, which can often originate from fake social media accounts, or when they actually write bogus material themselves ( Freberg, 2012 ; Wan et al., 2015 ; Jahnke and Kroll, 2018 ).

It follows that organizations must engage proactively with social media influencers and plan specific communication strategies around them. In the available studies, an influencer is defined as someone who has been empowered by their network, is extremely active on social media and so makes a significant impact ( Li, 2016 ). These studies, however, have not been concerned with classifying SMIs or examining how they connect with the personal relations strategies set in place within organizations. This implies that the overarching term of SMI brings together users who exert several kinds of influence. Influencers are, in some cases, those with very many followers, other times, they have a high number of connections or they may be extremely prolific bloggers ( Himelboim et al., 2014 ; De Veirman et al., 2017 ; Djafarova and Rushworth, 2017 ). If influencers are taken as a single category of social media users, companies will find it difficult to set in place a public relations strategy customized to the kind of influence being exerted.

This study addresses the problem of how to evaluate SMIs, by focusing on the kind of influence that the SMIs hold. The following research questions are addressed in the paper:

• • What are the indicators for qualifying and quantifying Social Media Influencers (SMIs)?
• • What is the methodological approach for applying these measures?
• • How can SMIs be classified and what are the associated public relations strategies?

Theoretically, the development of indicators for the quantification of SMIs is grounded in Social Network Analysis ( Wasserman and Faust, 1994 ), which recognises a different level of importance for nodes inside a network. Empirically, the indicators identified and related methodological approach were applied through an action research project conducted at the Teatro alla Scala, one of the major opera houses in Europe (and here also referred to as La Scala). Operationally, the authors conducted an empirical analysis of a Twitter-based dataset consisting of tweets posted about the Teatro Alla Scala in 2016, alongside interviews and meetings held with the opera house's marketing and communication offices.

As the main outcome of our study, we found that there are different types of SMIs, depending on the kind of influence they exert. This led us to come up with a precisely focused classification of SMIs into writers, authorities and spreaders . The activity and interaction of these influencers were captured and monitored through a set of indicators that have been tested at a technical and management level through the action research project.

The rest of the paper is structured as follows. First, the literature background consists of reviewing extant studies on influencers, with particular reference to the available measures for detecting SMIs. The research methodology will then be presented, followed by the results, where the first part covers the proposed indicators and the network-building methodology, and the second part deals with how these measures can be applied empirically to the Teatro alla Scala Twitter dataset. The final section contains the discussion and conclusions, expanding on the potential uses of these measures in management studies.

2. Background literature

There is the widespread recognition that social media users play different roles within social media platforms based upon their level of engagement with the production and consumption of information ( Shao, 2009 ; Muntinga et al., 2011 ; Austin et al., 2012 ; Li, 2016 ; Ge and Gretzel, 2018 ). Independently of the various classifications of social media users, users are generally split into two main categories, active social media users and passive social media users ( Li, 2016 ). While passive social media users are spectators watching from inside social media platforms, “active social media users are the creators, critics, collectors, and joiners” ( Li, 2016 : 51).

Influencers are considered to be a particular type of active social media user, and are defined as “opinion leaders who can use their online platforms to diffuse information and affect the attitudes and behaviours of their audiences” ( Moreno et al., 2015 ).

The available management studies on SMIs address three main research streams (see Table 1 ).

Table 1

Current literature on Social Media Influencers (SMIs).

The first research stream is focused on the relationship between SMIs and other social media users , with studies exploring how the general public perceive information posted on social media by SMIs, often comparing this information with that provided through traditional media ( Hayes and Carr, 2015 ; Johnson and Kaye, 2015 ; Djafarova and Rushworth, 2017 ; Ge and Gretzel, 2018 ). The second research stream explores the relationship between SMIs and the organization , placing particular attention on how organizations manage their interactions with influencers ( Jin and Liu, 2010 ; Pang et al., 2016 Ong and Ito, 2019 ). For example, Pang et al. (2016) developed a conceptual model to frame a strategy for cultivating effective relationships with SMIs, while Jin and Liu (2010) explored how organizations can interact with influencers to manage potential crises. A third recent research stream is concerned with the personal characteristics of SMIs . Concerning this last point, Freberg et al. (2011) adopted a California Q-sort method to provide a qualitative description of the salient personality traits of SMIs. The practices of self-branding and “micro-celebrity” used by social media influencers have been investigated in other studies, where the focus was on identifying and analyzing the behaviour of SMIs ( Wiedmann et al., 2010 ; Khamis et al., 2017 ).

Despite this growing literature on SMIs, management scholars have, to date, given little attention to how influencers can be detected or appraised once identified. A proxy for quantifying influencers is sometimes obtained by counting the number of posts they publish, their followers, the hits they receive on social media channels or their connections ( Himelboim et al., 2014 ; Agostino and Arnaboldi, 2017 ; Djafarova and Rushworth, 2017 ). The main disadvantage of these approaches is that the different kinds of influence exerted by SMIs, whether determined by the number of connections, posts from followers or something else, collapse into a single pigeonhole labelled influencer.

A small number of studies propose more sophisticated approaches to detect SMIs. For example, Booth and Matic (2011) developed a valuation algorithm, also called influencer index, that it is based on a weighted average of different parameters (such as posting frequency, citations, views per month and level of engagement). While this approach brings a number of different influencer-related characteristics into the elaboration, each of these characteristics on entering the index as variables must be classified manually - and so subjectively - on a scale from 1 to 5. The process of identifying influencers is, as a result, biased according to the analysts’ individual perceptions. More recently, De Veirman et al. (2017) suggested that the ratio between the number of followers and of followees should be taken into consideration, since they found “that a high number of followers may negatively impact influencer likeability for influencers who are following few accounts themselves” (p. 813).

All these studies have increased our understanding of the role played by SMIs and their impact, but somewhat limited attention has been paid to the methodologies for measuring these SMIs. SMIs are seen as a single category of users, without looking at the different kinds of influence they can exert within their network. This study addresses this issue by proposing a methodology and a set of indicators theoretically grounded on social network analysis to quantify and classify SMIs.

3. Methodology

The study relies on action research. The distinctive feature of this approach is that the researchers and the organization under examination work together within a joint research framework, allowing them to address a matter that can be both a practical concern and an academic problem. In this study, the organization in question is the world-famous Teatro alla Scala. La Scala was established in 1778 as an independent opera house, eventually becoming a foundation in 1997. It started operating on social media in 2009 and is now active on five social media platforms, Facebook, Twitter, YouTube, Instagram and Pinterest.

A work group was set up at the beginning of the project consisting of four researchers and five opera house employees, with the theatre being represented by the head of marketing and that of communications, the social media manager and two social media and communication specialists. The research group was made up of two senior researchers with expertise in management and analytics, one researcher with specific expertise in social media management and one with statistical expertise. The project lasted twelve months in total, with a monthly strategic meeting between the senior researchers and the heads of marketing and communication, and a weekly operational meeting between the junior researchers and the social media manager and experts. One researcher spent three days at La Scala as a participant observer, taking note of the daily dynamics of their social media management and analysis.

The entire project can be discussed following the five main phases of action research ( Table 2 ), expressed as diagnosing, action planning, action taking, evaluating and specifying learning ( Susman and Evered, 1978 ).

Table 2

Phases of the action research project.

The diagnosis phase consisted of setting out clearly the problem of analyzing SMIs. In this phase, the literature review played a key role in clarifying our understanding of current public relations research on SMIs. In particular, a gap emerged between quantifying the SMIs and their different levels of influence. This academic problem was aligned to the practical objective of being able to gain a better understanding of the social media users who talk about the opera house, an aspect that would allow La Scala to adjust their marketing and communications strategies accordingly. The theatre's management explained that they were interested in detecting the most influential bloggers engaged in talking about La Scala, their function or role (whether they are competitors, journalists or other) and their power within their social media network.

The action planning phase consisted of setting out the approach for the analysis, in terms of identifying the social media to be analyzed, defining the time horizon for collecting data and the keywords for extracting data, the techniques for data cleaning and the approach for building the network of social media users. At the end of this phase, the decision was to concentrate on the social media Twitter, since data can be downloaded for free ( https://developer.twitter.com/ ), which is not the case for Facebook or Instagram. A further decision was made to gather data from social media conversations by keywords rather than by account, allowing the group to detect conversations about the opera house more broadly, rather than only the threads emanating from tweets posted by La Scala.

The action taking phase consisted of putting previous decisions into practice. Data were downloaded from Twitter from March to May 2016 using its public API (Application Programming Interface), the network of social media users was constructed and the indicators for detecting influencers elaborated following a data-driven approach.

The evaluation phase played a crucial role in terms of revising the completed analysis, a process that involved both the research team and the opera house management represented by the heads of communication and of marketing. The indicators for detecting the influencers were refined during this phase, and the empirical analysis amended accordingly.

Finally, the specific learning phase took place after the analysis was fine-tuned. This involved holding a final meeting to consolidate the empirical analysis as well as the general methodology for detecting influencers.

The results presented were derived from a data-driven analysis of the Twitter dataset, backed by extant theories on SMIs and discussions with the management of the opera house.

Two main areas of results can be identified. A first area concerns the theoretical development of indicators to detect SMIs, together with the methodology used to construct the network. The second area of results applied the indicators previously discussed to the Twitter dataset concerning La Scala.

4.1. Detecting differences between SMIs: hub index, authority index, and centrality index

Our argument states that SMI is a generic term that refers to an active and empowered social media user who is listened to and seen as a trusted source by other social media users. They central point of our study is that there are different classes of SMIs, depending on the specific kind of influence that the SMI is able to exert. This premise is fundamental for establishing that there is not one but several types of SMIs, and that these different kinds vary one from another. As previous literature suggests ( Booth and Matic, 2011 ; De Veirman et al., 2017 ; Djafarova and Rushworth, 2017 ), a SMI can be actively engaged in posting on social media, or can have scores of followers or connections. In turn, these different levels of activity call for a change in how SMIs are measured. Different metrics are required to detect the heterogeneity in SMIs. In order to develop ad hoc measures for quantifying SMIs, we endorsed advanced social network analysis techniques basing the classification of SMIs upon the role they play in the network.

Since social network analysis focuses on patterns of relationships between nodes, this method is particularly useful for investigating the structure of a network in terms of the relative importance of the nodes and the strength of their ties ( Wasserman and Faust, 1994 ). Social network analysis techniques are increasingly being adopted to investigate social media networks, especially the connections and interactions between social media users or heterogeneous roles inside a network ( Sedereviciute and Valentini, 2011 ; Himelboim et al., 2014 ; Ramos et al., 2019 ). In studies endorsing the social network analysis perspective, this approach is recognized to be “a natural form of understanding and evaluating public relations as it focuses on patterns of relationships among social entities” ( Himelboim et al., 2014 : 363–364). Other authors have argued that “in a social media context, the network perspective becomes vital to tackle since social media are also about networks and stakeholders who create and share information online” ( Sedereviciute and Valentini, 2011 : 227).

In accordance with social network analysis, the position of a node in the network can affect its power and influence ( Granovetter, 1973 ; Wasserman and Faust, 1994 ; Coombs, 1998 ). The relative importance of a network node is usually measured through a set of indicators that can include centrality, degree and hubs, which determine the number of connections, the strength of the connections and the relative position of a node with respect to other nodes ( Freeman, 1979 ; Wasserman and Faust, 1994 ).

In this study, we propose a novel application of the social network analysis to quantify social media influencers and identify the different kinds of influence they can exert ( Alp and Öğüdücü, 2019 ; Oro et al., 2018 ). Moreover, without the adoption of powerful and advanced analytical tools of network analysis, a deep understanding of users roles and behaves would be more difficult. To do this, it is necessary to build a network of social media users, where the nodes and connections start from social media conversations.

More specifically, the network considered is what is known as a “citation network”. Given a set of posts relating to the Teatro alla Scala (based upon certain keywords and written within a certain interval of time), each post or user mentioned is a node in the network and every link between two nodes represents a citation. For example, looking at the following post:

“‘Due Foscari’ with great @user2 at @teatroallascala –amazing weekend in Milan #travel #milano”

written by an author going under the name of “@user1”, this gives rise to three nodes (corresponding to “@user1” and the two users named in the post, “@user2” and “@teatroallascala”, and to two directed links, the one from “@user1” to “@user2” and the other from “@user1” to “@teatroallascala”. Using the same approach for all the posts downloaded, the result is a weighted and directed network where the nodes represent the users posting online or being cited in posts, and the interactions are given by the messages exchanged between users.

After building the network of social media users, the indicators used to quantify SMIs can then be derived. We adopted already developed indicators in social network analysis dealing with the importance of nodes within a network ( Granovetter, 1973 ; Freeman, 1979 ; Wasserman and Faust, 1994 ), but applying them in a novel context of SMIs and completing this with more advanced statistical methods for investigating how information propagates through networks. This resulted into the development of three main indicators, hub index, authority index and centrality index ( Table 3 ). These three indicators show three different perspective of the user's activity, that could be interpret as three different category of SMI.

Table 3

Categorization of SMIs and relative indicators.

The hub and authority indexes come from the web search engine sector and were initially introduced to find the best way to organize relevant information. The hub index describes how active a user is in writing to important users in the network, and ranges from 0 to 1 (these being the lowest and highest level of hubness, respectively). Users with a high hub index are here called “ writers” as they send the most messages to important users in the network. The authority index describes how widely cited a user is by users with a high hub index, that is, by top writers, and it also goes from 0 to 1 (the lowest and highest level of authority, respectively). Values close to 1 detect what we called here “ authority ”. Operationally, given a graph G=(N,V) with N nodes and V links, if A is its adjacency matrix, the hub index is computed as the eigenvector of the matrix AA T , while the authority index is computed as the eigenvector of the matrix A T A.

Hub and authority indexes are strongly related to the concept of activeness and passiveness of a user within a network. User activeness (out-degree) and passiveness (in-degree) are computed by counting the number of messages sent and received ( Wasserman and Faust, 1994 ). However, hub and authority extend the concept of degree, because they take in the mutual relationships between highly passive and highly active users. The two indexes, hub and authority, present what is known as a “mutually reinforcing relationship”, meaning that a user with a high hub index, otherwise known as a “good hub”, is a user who points to many “good authorities”, where a “good authority” is a webpage with pointers from many “good hubs”.

The centrality index help to identify the SMIs who are mentioned often, have numerous connections and, most importantly, occupy a central position in the network, as they are closest to the focal organizations. The idea of centrality in social network studies is that a node with high centrality has higher access to information and has greater power than others ( Granovetter, 1973 ). SMIs with these features here have been called spreaders given their high potential for propagating information throughout the network. Extant literature on social network proposes a variety of indicators to quantify the level of centrality for a single node, such as betweenness centrality or closeness centrality ( Freeman, 1977 ; Granovetter, 1973 ). For the purpose of detecting SMIs, here we have adopted a more advanced social network technique, called the K-core or K-shell decomposition algorithm, using it to detect the level of centrality and, therefore, identify the spreaders. This approach is widely adopted when investigating the spreading of certain phenomena, such as infections, epidemics and other diseases (e.g. Pastor-Satorras and Vespignani, 2001 ), and is therefore aligned to detecting who, inside the social media network, has most spreading power.

The basic idea behind applying K-shell decomposition to the task of identifying SMIs is that network users positioned in central core layers can spread messages more widely than users located in peripheral areas. Hence, the users positioned closed to the centre (where the focal organization is located) are those influencing the network and they can easily spread messages throughout the network.

Operationally, the K-shell decomposition algorithm works in the way described below ( Kitsak et al., 2010 ):

“nodes are assigned a k shell according to their remaining degree, which is obtained by successive pruning on nodes with degree smaller than the k-s value of the current layer. We start by removing all nodes with degree k = 1. After removing all the nodes with k = 1, some nodes may be left with one link, so we continue pruning the system iteratively until there is no node left with K = 1 in the network”.

This approach is performed iteratively until no more nodes are left. In our empirical application, we concentrated on the in-degree of each node. The idea is to prune the network by peeling away the outer layers as if it were an “onion”, until reaching the core. Three layers were identified in our application, with users in layer K = 3 representing the central nodes of the network (those nearest to the organization) and they are considered as the spreaders. These are users cited the most on the organization's social media profile, and are, therefore, users that the network as a whole associates most closely with the organization. Because of the number of citations they receive and their many connection, spreaders are strategically important for an organization.

4.2. Detection of SMIs in the twitter network of the Teatro alla Scala

This section discusses the empirical application of the proposed indicators for detecting SMIs on the Twitter dataset relating to the Teatro alla Scala.

In line with the methodology proposed, the social network was constructed from the downloaded dataset (see Fig. 1 ). The Twitter network held 3,080 social media users and 13,318 posts written over the period of the analysis (March-May 2016). The Teatro alla Scala, being the focal organization, is positioned at the centre, while each user is indicated as a green node, the size of which is proportional to its degree.

Social Network of Twitter users for Teatro alla Scala.

The three indicators were then applied to detect the three different categories of influencers.

On elaborating the hub index, the influencers were ranked on the basis of their sent messages. We took the top ten users and analyzed them further. From the accounts and features of these users, we found that there are many kinds of writers in the theatre's Twitter network. Most are members of the general public, but some are artists, employees or companies. All are influencers, citing La Scala very often in their social media conversations. At this point, it is clearly important to understand whether they talk about La Scala in a positive or negative manner and what their level of authority is.

Users with a higher authority index were identified as a group of influencers whom we have called authorities. The first authority in the network was inevitably the opera house itself, given that the network was built around the focal organization placed at its centre. Behind the theatre, the next authorities were artists at La Scala and companies. The artists were mainly dancers, given their popularity among the general public, while companies are often the opera house's main sponsors.

Finally, the k-shell method was used to detect the spreaders . Only 15 users were found in the inner layers, corresponding to the key network spreaders for La Scala. Once again, artists and companies appeared to be the mainstay of the category ( Table 4 ).

Table 4

Detection of SMIs of Teatro alla Scala.

The results of this analysis were discussed with the management of the opera house. This phase was important in terms of interpreting the results of the three indexes, both from their viewpoint and from a management perspective in general. The values for quantifying the SMIs presented in Table 4 , alongside with the discussion of these results with the theatre's management, supported a broader discussion on how these measures affect the public relations actions of an organization. More specifically, per each type of influencer, an ad hoc public relations action was identified (see Table 5 ).

Table 5

Public relation activity for the each category of SMI.

Spreaders are a typology of SMIs particularly adept at conveying messages to the broader network, and as such they can act as strategic actors for organizations, helping to spread information and/or promotions. An organization should be proactive and contact these individuals, engaging them (and their services) within its communication plans and for other public relations activities. The discussion with the management of Teatro Alla Scala supported this view, with a reflection on the possibility to establish communication agreement with social media account detected as “spreaders”. Accordingly, the detection of spreaders within the network of social media users pushes the organization to adopt an “active” public relation strategy characterized by the active involvement of the spreaders themselves in conveying social media messages.

Authorities represent a typology of SMIs that receive a vast number of messages and, for this reason, are considered to be important in the eyes of other users in the network. Often, they are also social media accounts with a high number of followers. Their role is crucial inside the network of social media users, having the power to boost positively or negatively the opinion of other users. If “authorities” post messages expressing a positive sentiment, then this can strengthen the brand's reputation they refer to. If, on the contrary, they post negative messages, this can cause damage to a brand. The detection of “authorities” within the network has the main effect to push organizations adopting a “monitoring” public relation action on these social media account. A monitoring action means the introduction of “alert” to signal the publication of a post by “authorities”; the subsequent action depends on the sentiment of the message itself: positive messages can be forwarded (recalling the active action), while negative messages require an immediate intervention to avoid the diffusion of a negative mood among the other social media users. Moreover, the strategy towards authorities can also be proactive, by involving them in the communication activity by the organization and leveraging on their wide network to boost the virality of the messages.

Finally, “writers” are the third categories of social media influencers, quantified by the hub index as those social media users very active in writing messages. The discussion of the list of “writers” with the management of Teatro Alla Scala, lead to the identification of several private individuals and a few organizations and companies. The connected public relations activity was dual: for writers with a high amount of followers, they were included in the monitoring activity previously described. Yet writers with a limited amount of followers were mainly considered as spammer. The action implemented was that of ignoring them. This implies the adoption of an inactive action only for those writers with a limited amount of followers.

In summary, advanced social network analysis techniques were specifically applied to the novel context of detecting heterogeneous SMIs. Indicators were proposed theoretically, but also applied empirically with the identification of three main public relation actions (in connection with each typology of SMI identified) derived from the discussion of results with the management of Teatro Alla Scala. We are aware that other effects can exist, such as the level of engagement connected with the messages post by influencers or the sentiment of the messages. However, the analysis of the effects of SMIs action is out of scope of this investigation.

5. Discussion and conclusions

Given the widespread recognition about the importance of SMIs in management studies ( Freberg et al., 2011 ; Moreno et al., 2015 ; Li, 2016 ), this study addressed the question of quantifying SMIs, examining how they can be identified and how to classify them on the basis of the influence they exert.

Our argument here is that SMIs are not a homogenous category of users, and their influence on social media varies. This implies that there are different categories of SMIs. Previous studies were valuable in suggesting ways to detect SMIs and investigate their personal features and the impact of their activities on other social media users and the organization ( Booth and Matic, 2011 ; Himelboim et al., 2014 ; De Veirman et al., 2017 ; Djafarova and Rushworth, 2017 ). Adding to previous research, this study also addresses how SMIs can be classified by proposing an advanced methodology based on social network analysis, and then examines the relationship between the different SMI categories and the personal relations strategies set out in an organization.

These results have been achieved through an action research project, which started from a literature review, proceeded with the empirical analysis on a Twitter dataset and concluded with a critical discussion of the results achieved with the management at La Scala. Moreover, once these measures were applied, they supported our initial argument that SMIs are heterogeneous, since the writers, authorities and spreaders all differ from one another.

The theoretical developments and empirical application enhanced previous research at two levels. First, we developed and tested the classification of SMIs, meaning that the associated methodology can be replicated in other studies. SMIs are grouped into three categories, revealed by the quantitative analysis of the network. The first category is that of the writers , detected through a hub index, and they are influencers in terms of the number of posts they write.

The second category is that of the authorities , that is, the influencers who are mentioned extensively within the network, and they were detected through an authority index. The third category is that of the spreaders , who were detected by adopting a K-shell decomposition algorithm. These are the influencers who are mentioned very often and have many connections and, therefore, are very likely to convey information throughout the network.

This classification and methodology add to our current understanding about the importance of SMIs and how they can be detected, by underlying the importance of classifying influencers on the basis of the kind of influence they exert within the network.

The second area of results was obtained by applying the methodology empirically to La Scala, taking in insights from the participant observation process, and then validating the results. We are well-aware that our findings are specific to their context and time, however, this action research study allows us to draw more general considerations on public relations strategies in connection with the detailed analysis of SMIs. In particular, the empirical analysis confirmed that there are differences between the three categories of influencers, setting out a personal relations strategy for each group. The strategies can be split into “active” strategies, applied to spreaders, because of their central role in conveying messages through their network, “monitoring” strategies, applied to authorities, because of their numerous connections and their ability to deeply affect, positively or negatively, the reputation of organizations, and “inactive” strategies, applied to writers with only a few followers.

These results not only enhance our academic knowledge about SMIs, how they can be detected and the need for an ad hoc strategy for each type. The proposed methodology can also help public relations practitioners by providing them with a practical toolkit to detect SMIs and suggesting appropriate management strategies for each category.

This work can lead to further studies, where the same measures can be applied to other social media and used to explore the connection between SMIs and the opinions expressed in their posts.

Declarations

Author contribution statement.

Agostino Deborah, Arnaboldi Michela, Calissano Anna: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.

Funding statement

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Competing interest statement

The authors declare no conflict of interest.

Additional information

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A Survey on Social Media Influence Environment and Influencers Identification

• Review Paper
• Published: 03 October 2022
• Volume 12 , article number  145 , ( 2022 )

Cite this article

• Feriel Gammoudi   ORCID: orcid.org/0000-0002-4715-0028 1 , 2 ,
• Mondher Sendi 2 &
• Mohamed Nazih Omri 2  

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Social media users can be influenced directly by their close relationships, such as their friends, family, and colleagues. They can also be influenced by those who follow them through shared information, goals, news, and opinions. Generally, an influencer is someone who entices an influence to do the same action, make the same decision, or change their behavior. He can also communicate information, ideas, and thoughts to multiple users. There are many tools to identify influencers. It can not be found simply through their big follower number or their shared media number. Thus, influencer identification is one of the essential tasks in social media research. Several approaches and metrics have been proposed in the literature to identify influencers. In this article, we explored the issue of identifying social media influencers while providing a generic view of social media influence. First, we presented a literature synthesis on the influence of social media. Then, we categorized the works and illustrated the leading solutions in literature to identify influencers in social media. A discussion and suggestions for potential future directions in this area accompanied this presentation. We believe these briefings are critical to resolving the issue discussed in this article.

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1 Introduction

1.1 context.

Digital networks have a huge impact on people’s daily lives, leading dozens of new researchers to study this rapidly growing issue that extends throughout multiple sub-domains. As social networks become more prominent, a researcher’s first logical thought upon analyzing these networks is how to get specific information from the data. That makes marketing applications explore social media to understand user trends and offer new products. Military applications have analyzed them to detect terrorist groups and their further socialization Cialdini and Goldstein ( 2004 ). The health and biomedical fields Sendi and Omri ( 2015 ) have also benefited from social media analysis by exploring discussions on health topics and analyzing patient behaviors. Analysis can also predict relationship quality, event detection and characterization as good or bad Iraklis and Williams ( 2020 ) anticipating the number of followers they might have Imamori and Tajima ( 2016 ), enhancing interaction activity and user similarities Xiang et al. ( 2010 ). And by combining temporal information with a social network structure, user interests Bao et al. ( 2013 ) are predicted.

As the researchers’ results found, the network structure proves to be of utmost importance. A graph with nodes and edges, where each edge symbolizes the connection between two users, whereas each node represents one user, can be used to model a social network. Users have a personal web page and connect with different friends to share content and interact with each other, and build social relationships by following others or adding a theme as a friend.

In associated with social influence Peng et al. ( 2018 ) an influencer entices an influence to do the same action or make the same decision, and there can also be no uncertainty Peng et al. ( 2017 ). Users might well be instantly influenced by their true and intimate relationships (friends, family, classmates, coworkers, etc.) while they spend more time with them, and by famous content creators that they follow, by consulting their shares daily: actuality, goals, news, and opinion. Thus, users are sharing influence while sharing media and posts. The information and knowledge management community has recently attached importance to the topic of identifying significant users from social media as “the source of influence”. Although studies have concentrated on measuring levels of ability to identify knowledgeable users and determining levels of influence to identify influential users. The process starts by analyzing the influence of the nodes and then progressively maximizes the influence of an initial set of nodes to obtain the influential nodes. It is a challenging task that has the potential for considerable usefulness in many applications.

Several important works in the literature deal with influence and many new works have recently been attracted by identifying important and significant influencer nodes. In this paper, we will deal with the social media influence environment, influencer identification, and important nodes, and we will classify important existing works.

The following section discusses the main related surveys cited in the literature.

1.2 Related surveys

Many important works dealing with social media analysis have been proposed in the literature. We are going to classify them according to their state of the art and their metrics. The first social media analysis technique Adedoyin-Olowe et al. ( 2014 ), Tabassum et al. ( 2018 ) was classified as an unsupervised, semi-supervised, or supervised learning method. The SM community detection Azaouzi et al. ( 2019 ) and social media structure discovery have two types of structure networks: static or dynamic according to space and time. Some others worked to drown the social media users’ relationships as a graph with nodes and edges. Authors Bian et al. ( 2019 ) reviewed, analyzed, and classified some main literature works into two main categories: the most influential nodes in the graph and the most significant nodes. Works dealing with the inferring of user interest profiles have been classified in Piao and Breslin ( 2018 ). Another target user personality has been detailed by its authors in Andreassen et al. ( 2016 ). Depending on the sort of machine learning utilized in each approach, the information transmitted could be rumors Alzanin and M.Azmi ( 2018 ). The authors proceeded by dividing the problem of rumor detection into three different categories: hybrid approaches; supervised approaches; and unsupervised approaches. There are also important works dealing with the analysis of social media influence, such as the works detailed in Peng et al. ( 2018 ); Sun and Tang ( 2011 ); Peng et al. ( 2017 ). The authors of the contribution presented in Peng et al. ( 2018 ) classified several notable works using switch algorithms and models. In Sun and Tang ( 2011 ), the authors also described various methods and algorithms for calculating measures related to social influence. An important new work Azaouzi et al. ( 2021 ) deals with the problem of influence maximization under privacy protection. They divide models into two categories: group node-based models and individual models. And, by proposing different algorithms, different methodologies, and also diverse frameworks, they demonstrated that one key solves the problem of maximizing influence at the individual level Kempe et al. ( 2003 ). The perspectives of influencer marketing, their content strategies, and the attractiveness of their sponsored recommendations are reviewed to promote the marketing tactic. The three research factors used by authors to categorize papers were the source, message, and audience Hudders et al. ( 2021 ). Social network applications are becoming highly interested in the topic of community detection Plantie and Crampes ( 2013 ).

There is much work dealing with the influence of others on social media. We aimed to classify some of them subjectively according to their aims and the taxonomy used in classification. Table  1 below summarizes the related surveys.

To the best of our knowledge and after these related surveys studies, no one proposed a classification of influencer node. Moreover, no work has been found to deal with the global problem of social media influencer identification. By comparison with the main related surveys, such as Bian et al. ( 2019 ); Adedoyin-Olowe et al. ( 2014 ); Tabassum et al. ( 2018 ); Piao and Breslin ( 2018 ); Alzanin and M.Azmi ( 2018 ); Hudders et al. ( 2021 ); Andreassen et al. ( 2016 ); Peng et al. ( 2018 ); Plantie and Crampes ( 2013 ) presented previously, our study presents the following major differences:

We are not only dealing with influence maximization or significant node identification. Our aim is the identification of many types of social network influencers.

Unlike some existing surveys, dealing with only one type of social network, that of Twitter, our study exploits different types of social networks, such as micro-blogging, egocentric networks, Google+ dataset, Facebook, Amazon, DBLP, and Wiki.

Our study is characterized by two important points: exhaustiveness and recent bibliographical references. Indeed, our study represents coverage of different fields of application.

1.3 Motivation and contribution

1.3.1 motivation.

Numerous important works in the literature deal with user interests and influence, which have changed over time and have played a vital role in all analysis processes. Thus, we have dealt with social media strategy, the first user influencer, to simulate the real environment of users.

The social media strategy makes users more dependent: content is organized and categorized, and switching algorithms, user activity, and past choices increase the usage rate. One of its strategies is that publication occurrence depends on users’ last choices and their degree of importance, the rate of publication consulted, and users’ litigation. These networks use cookies and algorithms to collect information about them and their behavior. Other websites consult, search, and try to identify the users’ interests. When the latter is looking for something, it starts appearing as a suggestion on social media homepages. This further influences brands to consume their offer. User interest was also guided by photos, videos, and information inspired by his friends. On the homepage are their trending topics, most viewed videos, and their latest picks; similar and international trending topics; international action or famous events; and personal activity.

Social media allows users to follow each other and share details about their activities and postings. They can like each other’s publications, photos, videos, opinions, and disputes. It allows the user to see similar consulted publications, and it offers the possibility to chat and create direct communication and influence. Their friend or an influencer can be inspired by seeing the publication. That is why we thought that social media users were sharing influence.

All of those influence the user’s choice, use, ideas, focus, and personality. Users prefer personalized and guided services that are matched by their choice of searches and hope. So we are dealing with the influence and influencers on social media networks.

1.3.2 Contributions

The main contributions of this article can be summarized in the following points:

First, we dealt with the social media influence environment to study the general concept of our aims.

Secondly, our principal contribution was influencer identification and important nodes, followed by classifying important works dealing with them and reviewing them.

Finally, in a third step, new research directions concerning our survey review are discussed.

1.4 Paper Organization

The rest of this work is organized as follows: In Section  2 , we presented the review methodology adopted in this work by providing the information sources and the research questions. In section  3 , the social media influence background is presented and discussed. Section  4 is devoted to detailing an overview of social media influence. Then, section  5 contained a literature synthesis on the social media influence that we have treated in this work. Section  6 presented the discussions and research challenges that are synthesized at the end of this research work. We concluded the study by proposing several research challenges in Section  7 .

2 Review methodology, sources of information and research questions

2.1 review methodology.

We have assumed a systematic methodology to elaborate on this current work on social media influence. The following is a description of the review approach used, inspired by Kitchenham and Charters ( 2007 ); Helali and Omri ( 2021 ); Souiden et al. ( 2022 ). The review procedure is a series of processes that make up the review protocol. We begin by providing the various information sources and then the search and selection criteria for the main bibliographical references that we used to undertake this study. Next, we present the questions section that we have to provide answers to throughout this literary work.

According to our objective, we do not aim to create a proper list dealing with social media influence but to introduce some important work. We focused on works from reputable publications. The initial research was based on keywords from recent works. After that, we filtered our search results by choosing according to a few norms, such as the publication date and/or writer competence. If the same, we find a similar approach or idea. Then, based on a full-text screening, title, and abstract, we obtain our primary studies list. Finally, to ensure the quality of the assessment form, we choose several articles arbitrarily based on the previous stage’s selected results.

2.2 Sources of information considered

Articles from different types of sources

Concerning the bibliography used, we proceed to search various information sources to lead to the realization of this study. To choose sources related to our focus problem, we selected research articles from journals, conference proceedings, books, and magazines. Thus, we considered the following databases in our research: IEEE Xplore Springer Footnote 1 , ScienceDirect Footnote 2 , Scopus Footnote 3 , ACM Digital Library Footnote 4 ,Taylor Footnote 5 , Francis Footnote 6 , Google Scholar Footnote 7 .

We also screened the majority of related high-profile conferences, such as SIGKDD, ICDT, SIGMOD, ICML, WWW, VLDB, ICDE, and EDBT, to find out about recent work. Figure  1 shows that papers reviewed percentages from different types of resources.

2.3 Research questions

In our research, we are going to answer some proposed questions that are identified in this current section. Therefore, this part focuses on question determination, as below. Then, we will answer those questions through the research review.

RQ1 : Who can influence social media users?

RQ2 : Which is the way to detect influencers?

RQ3 : How do they influence social media users?

3 Social Media Influence : background

3.1 social networks representation.

Social media networks can be represented as a set of profile sites Aggarwal and Subbian ( 2014 ) or virtual communities. Their structure, by default, is considered static, with a static topology and information. After that, it can be transformed into dynamic networks at a specific time t if new nodes and edges are added. Networks have a specific topology Peng et al. ( 2018 ): internet sites where users connect and communicate with others; and mobile social networks. According to the contribution Bian et al. ( 2019 ), the authors show that we can model a social network by a graph G = {V, E} where V = { \(v_1\) , \(v_2\) , . . . , \(v_i\) } represents a set of nodes and E = { \(e_1\) , \(e_2\) ,..., \(e_j\) } represents a set of edges. In the network graph, actors are denoted by V , and social interactions and relations between them are denoted by E . Edge measures relay the influence built on the concepts of a pair of nodes. Each node \(v_i\) can be active or inactive, and the diffusion of influence can propagate from the active node to its neighbor, and the links correspond to social relations.

3.2 Influence on Social Media

Social influence is a concept that includes a wide range of phenomena, such as:

Socialization is the process through which individuals acquire the knowledge, skills, attitudes, values, norms, and appropriate actions of their community Shahr et al. ( 2019 ), which powerfully directs behavior in private settings as well Cialdini and Goldstein ( 2004 ) .

Obedience and authority Individuals are frequently rewarded for behaving under the opinions, advice, and directives of authority figures Cialdini and Goldstein ( 2004 ).

Compliance professionals are forever attempting to establish that they are working for the same goals as they are, in essence, their teammates Cialdini ( 2001 ).

Conformity refers to the act of changing one’s behavior to match the responses of others Cialdini and Goldstein ( 2004 ).

Persuasion, the ability to use its effect, is frequently a crucial element of success Cialdini and NJ. ( 2002 ).

Social networking users tend to act like their “friends” or “neighbors” Singlaand and Richardson ( 2008 ); Sun and Tang ( 2011 ). This phenomenon can be summarized in the following points:

Social Influence: Social media users are more likely to imitate their friends’ actions. Thus, social influence causes people to endorse the behaviors of others.

Selection: People who are characterized as close to them are more likely to bond with each other.

Variables that may be confusing: Other unidentified variables exist, and people may act similarly to each other.

Social influence Cercel and Trausan-Matu ( 2014 ) can be defined as the influence of a person or a group of people on each other. This influence is manifested through the imposition of a set of attitudes and behaviors. Considering two users, \(U_1\) and \(U_2\) , from the social network, \(U_1\) has a direct influence on \(U_2\) or indirectly influences the point of view of \(U_2\) , and transmits influence to \(U_2\) . According to Peng et al. ( 2018 ), social influence is defined as a level of uncertainty or a binary relationship between one (the influencer) and the other (the influenced) to do the same action or choice. The level of social influence can be quantified by a continuous real number or by a degree of uncertainty (e.g., weaker, weak, strong, stronger, etc.) and can have different values Peng et al. ( 2017 ). The fact that \(U_1\) influences \(U_2\) , does not imply that \(U_2\) necessarily affects \(U_1\) .

3.3 Two-step and multi-step communication flow theory

Since relationships are organized around a network of peers, with no centralized entities of control in social media, users are thrown into a global stream of communication Elanor ( 2013 ). Social media networks have reshaped communication at different levels, such as mass communication’s two-step flow of communication theory Oren ( 2019 ) and multi-step communication. The two-step flow communication theory can be used to describe how influence works on social media. According to Ognyanova ( 2017 ) in the two-step model, the theory’s concept of personalized content through opinion leaders Oren ( 2019 ) and direct media effects are hampered by social interactions and audience selectivity in exposure, perception, and retention. The two-step flow theory was expanded upon by the multi-step paradigm Ognyanova ( 2017 ). The multi-step flow paradigm describes the way media and interpersonal influence shape public opinion. Understanding whether someone’s opinion was influenced by their social links or if they simply chose social ties that shared that opinion, to begin with, is important in the context of the multi-step flow of communication Ognyanova ( 2017 ).

3.4 Influencers identification

Nowadays, social media homepages are limited to showing their users priority on the activities of their friends who are used to communicating with or reacting to their statuses, pics, media, or stories. This task can be studied by utilizing the rich text and user interactions on social media Zheng et al. ( 2020 ). Some research deals with the person who influences others, each defines a type and researches its impact on others. Nowadays, social media homepages are limited to showing their users the activities of their friends, who are used to communicating with or reacting to their statuses, pics, media, or stories. Some research deals with the person who influences others, each defines one influencer type and researches its impact on others. In the book, Shah et al. ( 2018 ) and according to its authors, influential people play an important role in information sharing. Being the most popular or the first to affirm a new concept are two examples of being influential. They also provide a strategy for identifying network infrastructure pioneers based on a specific topic of interest. People who adopt and introduce new ideas before they are known, influencing others, are known as trendsetters. They are not always famous or popular. In the same way, not all the latest inventors are trendsetters since only a few of them can spread their ideas in their social circles. They also suggest a reliable way to describe the spread of creativity by presenting each issue as a collection of trends that can be applied to many different scenarios. Results from algorithm simulations on a large Twitter dataset show an ability to quantify indirect and direct influence, and early adoption is also an important trait for influencing others.

All this is to distinguish between the trendsetters and the others, who, although having a high degree, only accept trends when they have become well known. Other specified innovation hubs score a lower degree of acceptance of a new idea than follower hubs. Followers Innovation Hubs are trendsetters, while hubs are influencers. In the dissemination of information, influential people have a crucial effect. Having more Wang et al. ( 2020 ) followers than others does not mean more interaction. There are several ways to be influential, such as being the most famous or being the first to implement a new idea. The most influential users Erlandsson et al. ( 2016 ); Zareie et al. ( 2019 ); Zhao et al. ( 2019 ); Sun and Ng ( 2012 ) played an important role in spreading the information. Others aim to find influencers Rodríguez-Vidal et al. ( 2019 ); Subbian et al. ( 2014 ); Harrigan et al. ( 2021 ); Tsugawa and Kimura ( 2018 ); Kaple et al. ( 2017 ); Chia et al. ( 2021 ); Harrigan et al. ( 2021 ) in different ways. The authors of the works presented in Cervellini et al. ( 2016 ); Saez-Trumper et al. ( 2012 ) deal with a trendsetter who is not automatically popular or famous but whose thoughts have been broadcast on the network. As a result, they serve the same purpose with a different name Zhou et al. ( 2019 ): influence nodes. Identifying influential users has important implications in e-commerce and media Jain and Sinha ( 2020 ); Sun et al. ( 2016 ); Yang et al. ( 2019 ); Sheikhahmadi et al. ( 2017 ). Another example is influential actors Qasem et al. ( 2015 , 2017 ), who have followers Probst ( 2013 ), are famous on Instagram Jin et al. ( 2019 ), and have popular content Ding et al. ( 2015 ). This phenomenon causes a significant increase in the size of the social network. In addition, news influencers Alp and Ögüdücü ( 2018 ) have been identified as specialists on a specific topic. Table  2 summarizes this work and gives further details.

Trendsetters are people who support and propagate innovative thoughts and have an influence on others before they become famous. Cervellini et al. ( 2016 ) propose a methodology for identifying themes in network infrastructure based on a certain topic of interest. And Saez-Trumper et al. ( 2012 ) used different ranking algorithms to identify themes. Popular content is by and about famous people on social media. A random walk model was presented by Ding Ding et al. ( 2015 ) to quantify the influence of users and the popularity of tweets. Another influencer type is called the prophet, who are knowledgeable bloggers and have a strong capacity to predict the future. Zhang et al. ( 2015 ) propose a method for identifying influential blogs that can predict trending hashtags. It is based on keywords or phrases that describe topics or events that are well-known in the community. Influencers are people who have the power to influence someone or something’s personality, evolution, or attitude. Authors like Rodríguez-Vidal et al. ( 2019 ) used a topic modeling approach to manage the textual signal in tweets after experimenting with numerous signals and machine learning algorithms. Harrigan et al. ( 2021 )are using freely available data from social media APIs, and influential mavens on social media may be discovered. Chia et al. ( 2021 ) integrated the social capital and social exchange theories, as well as the social learning theory, to identify ideal Kaple et al. ( 2017 ) proposed a method to maximize public participation and build smart cities through the use of social networks. To detect influencers, Pudjajana et al. ( 2018 ) employ SNA metrics (DC, CC, BC) and weight on the SNA measurement. A method for identifying network influencers based on their social capital value was proposed by Subbian et al. ( 2014 ). Switch Sunil and Lingam ( 2019 ) most of the currently used algorithms created to address these issues have the greedy algorithm as their foundation. And the two-level approach was proposed (SI) for an epidemic model for maximizing the influence spread and a multithreading approach for implementation. Tsugawa and Kimura ( 2018 ) used degree, betweenness, proximity, PageRank, and the k-core index, and used the common sample methodologies SEC, BFS, DFS, and random sampling to find influencers in different social networks. Switch topic Alp and Ögüdücü ( 2018 ) defined the topical influencers as experts on a given topic. And to identify the theme, an approach that combines related features and network feature information was used. It is widely acknowledged that the distribution of influence varies over topics. The work of Zheng et al. ( 2020 ) is based on the language attention network and influence convolution network to detect on-demand topic-specific influencers. Another work dealing with Topic-Sensitive Influencer Fang et al. ( 2014 ). They used a unified hypergraph to model users, images, and various types of relations. Influencers of airline services Izdihardian and Ruldeviyani ( 2021 ) are also detected on Twitter and the results showed their higher influence. Influential nodes in complex networks are often those that increase an interest propagation process’s asymptotic reach. Zhou et al. ( 2019 ) a thorough evaluation of centrality metrics based on their capacity to detect early and late influencer nodes. Lead or follow the influence of mobilizers and propagators in networks and society funded by Probst ( 2013 ) using a systematic literature review to find relevant publications. Influential users are authoritative actors and are a term used to describe influential users. The work of Jain and Sinha ( 2020 ) used WCI (Weighted Correlated Influence), a new concept that integrates the relative influence of 10 different elements into two different feature sets: timeline and trend specific. Yang et al. ( 2019 ) developed the component study of the characteristics of Houston’s top 1% of Twitter users in terms of the following growth. Another important work by Sheikhahmadi et al. ( 2017 ) where weights are allocated to neighbors based on their adoption delays and the spreading influence of nodes is identified. Via a multi-features model, Sun et al. ( 2016 ) and based on a user identification solution with a lot of clouts. The model investigates if a user’s attributes, such as follower count, can be used to infer whether he or she is an influential user, and then ranks influential individuals based on their impact using the Page-Rank concept. Others aim to find the most influential users, or “top k” users, who are considered the most influential. Erlandsson et al. ( 2016 ) found the Degree Centrality and Page Rank Centrality and compared the theme to the outcomes of association rule learning. Zareie et al. ( 2019 ) proposed the idea of a user behavior analysis-based high-influence user discovery algorithm (HIUD). Also, influential actors are individuals whose tweets generate an enormous number of retweets, resulting in a larger social network, proposed in 2015 by Qasem et al. ( 2015 ) find via a method for detecting influential people utilizing a novel influence degree. Then, Qasem et al. ( 2017 ), an extended method for identifying important actors, was formed based on the attractiveness model defined with the T measure.

Other works have defined special influencers that can be an inspiration for many important other works by trying to identify and find those influencer nodes on social media networks. MARWICK ( 2013 ) wrote a book that describes content creators who enjoy fame and influence others by using social media to maintain an active communication network. Other works could use the larger social graph or friends list and broadcast audience recordings to Marwick, who proved its importance. In the work Enke and Borchers ( 2019 ), the authors described three types of influencers: digital opinion leaders, those who are likely to have an impact on others in their immediate area, and content creators, who are people who develop content for social media using their talents to create social media content. They also defined moderators, who initiate interaction procedures and utilize their position to try to lead and continue conversations, and they proved the importance and strategy of influencer communication, in which social media influencers are performed or addressed within tasks that are strategically important to the objectives of business goals. Campbell and Grimm ( 2019 ); Campbell and Farrell ( 2020 ) aimed at users with large communities of followers. Like celebrity influencers and mega-influencers, last but not least, are individuals who have already earned 1 million or more followers on social media, have seen considerable follower growth, and have gained celebrity status through a well-established skill. Recording Campbell and Grimm ( 2019 ) authors’ social media influencers’ posts on their social media channels in exchange for compensation. In their next work, they affirmed that influencers can be simultaneously social media personae, celebrities, experts in particular fields, and peer consumers. They add another definition of influencers and micro-influencers, whose ads are indistinguishable from other consumer-generated content. And also, they defined influencers as individuals who post to their social media accounts in exchange for compensation and as consumers paid to endorse products and promote them to their followers. They affirmed that micro-influencers, Campbell and Grimm ( 2019 ); Campbell and Farrell ( 2020 ); Leonardi and Monti ( 2020 ) or influencers with relatively small online followings, are more effective, often implying enjoyment or simply usage than other influencers and celebrities.

Using social media to identify influential users is a complex process, but one that is extremely important for several Zhang et al. ( 2015 ) applications. Previous research has determined skill levels for detecting knowledgeable users and influence degrees for recognizing influential users. Generally, they mainly depend on the analysis of textual content but also on links to the structure of social media networks. Bloggers use the blogosphere as a platform to post articles, communicate ideas and discuss views. Blogosphere data is dynamic and changes with information updates over time. In the work presented in Zhang et al. ( 2015 ), the authors attempted to find bloggers who anticipate future popular trends, known as prophetic bloggers. In this work, the authors examined the chronological characteristics and content of blog data and proposed a method to analyze the ability of bloggers to predict buzzwords. Bloggers are judged on the types of words used in their blogs. For example, four recent, related, frequent, and/or thorough factors are considered for this goal: content similarity, post-precocity, buzzword coverage, and frequency of entry. In the work of Zhang et al. ( 2015 ), the authors stated that by inferring buzzwords, they detected buzzwords, which allows the assessment of bloggers’ buzzword prediction ability. The contribution proposed by Bian et al. ( 2014a ), was interested in the detection of epidemics. To discover the spread of a virus as soon as possible, they have selected many social network nodes that they consider most relevant. To maximize the benefits of companies’ products and services, consumer profiles are detected and analyzed Hernandez et al. ( 2013 ). By analyzing a dataset from Twitter, the linguistic indicators of partisan conflict in mainstream and social media during political upheaval are explored Karamshuk et al. ( 2016 ). Others deal with pathogenic social media accounts, such as terrorist supporters exploiting large communities Alvari et al. ( 2018 ) and critical nodes, Alorainy et al. ( 2022 ) and a high detection of top-k central nodes Mahyar et al. ( 2018 ). Identifying the users who are most influential in spreading information can aid in the creation of effective tactics Adnan et al. ( 2022 ).

4 Overview of Social Media Influence

4.1 users’ interest.

The degree of influence a social media user has on their friends, as well as their desire to spread additional information, can be detected from interest similarity. Authors have submitted that people users are more affected and influenced by their friends’ opinions than by other influencers Domingos and Richardson ( 2001 ); Staab et al. ( 2005 ). In Xie et al. ( 2014 ), the measure of similarity between two users is based on a random walk distance based on tags and image correlation and was examined in folksonomy data. It outperforms proof in terms of user profile enrichment. Generally, social media users demonstrate Bian et al. ( 2014b ) different interests and hold different expertise levels for numerous kinds of interests, such as sports, music, history, and so on. Authors Sendi et al. ( 2017 ) assumed that users’ interests were discovered from uncertain information. Another way to categorize users’ profiles Mabrouk et al. ( 2020 , 2018 ) as an outcome, the culture of the user’s interest profile will be critical for a full analysis of their influence on their friends, diffusion actions, and future action prediction.

4.2 User’s relationship

The main reason that social networks influence users’ behavior changes is their relationship. As users’ relationship strengths are not equal, social media users do not have the same degree of friendship and intimacy. Relationship strength can be detected by interaction comments, likes, chats, etc. Previously, social network studies centered on binary friendship relationships. Switch authors Xiang et al. ( 2010 ) can lead to a wide variety of relationship strengths (for example, mixing acquaintances and best friends) to estimate relationship strength. They create an unsupervised model based on user similarities and interaction activity, weak and strong relationships. Their model represents the full spectrum and infers a continuous-valued relationship strength for links. For the inference, they used a coordinate ascent optimization approach to create their link-based latent variable model. They have proved and evaluated it on Facebook and LinkedIn. The authors Chader et al. ( 2017 ) did not accept the binary relation. They had related weights that recorded the strength of people’s relationships; they tested the effectiveness of the CoBSP process. The motive comes from the belief that those with the closest relationships with users can give more information about them. In the work of Zarrinkalam et al. ( 2017 , 2019 ), they detect user interests by focusing on social relationships, user-generated content, and temporal factors.

4.3 Interests change

After taking into account the users’ utterances, gender Ouni et al. ( 2022b ), their activeness, and vulnerability to their friends’ influence, the work of Budak et al. ( 2014 ) proved that interests vary during life. They established an inference algorithm that strikes a balance between the latest estimates of interests and the old estimate of interests, and using Twitter data, they funded a precision of 0.9 for the top-5 interests. Users have a variety of interests, such as music, history, sports, and so on Bian et al. ( 2014a ). The degree of influence he has on his friends is determined by his interest in distributing new information and must be varied for different categories. The interest profile learning is decisive for a complete analysis of a user’s impact on his contacts, spread actions, and future action prediction. Another idea Wang et al. ( 2018 ) is to use the UNITE systemic framework with interesting extraction, which considers both text and information. Their approach to experimental results on Sina Weibo. An important work Sendi et al. ( 2019 ) aims to derive and monitor on Twitter the temporal interest topics and also handle the user’s interest, social various dimensions, and dynamical characteristics. Moreover, over time on social media, through user activities’ frequent tags, users, and organizations from their social media posts Shah et al. ( 2018 ), the evolution of one’s attitude through time is observed. The suggested approach also incorporates the use of the semantic web, particularly the DBpedia ontology, which analyzes the content of tweets in the user’s Twitter feed.

4.4 Infer interests

To deduce users’ interests, authors have taken into account their communication, their activity state, and their sensitivity according to the influence of their friends. A probabilistic model of social data has been proposed by Budak et al. ( 2014 ). This model is unsupervised and well-scalable with many users and interests. The authors of Han et al. ( 2015 ), were interested in determining the similarities of interest given to two users, without having any idea about their interests. They showed that similarities between people depend on similar demographic information and having more common friends. Attribute inference can be performed by mixing social graphs and Jia et al. ( 2017 ) behaviors, by first collecting public data and then using machine learning techniques to estimate the private attributes of certain target users. They analyzed user behavior to determine the probability that all users have a given property. Many applications, such as targeted advertising and news suggestions, rely on user locations, which can help solve the problem of profiling users’ Ouni et al. ( 2022c ) home locations Li et al. ( 2012 ). The authors propose a unified discriminatory influence model UDI and overcome the challenge of rare signals in a unified probabilistic framework, combining user-centric and social media data. They produce local and global locations with their prediction methods. Important work has also been proposed in Utz ( 2010 ), their authors found that the target’s acknowledged popularity, community orientation, target’s self-generated data, the affectivity of the target’s contacts, and the volume of friends all have an impact on social attractiveness. So users can create accounts with photos and detailed descriptions to use as a starting point for building an impression. In addition to self-provided data, social media offers two other types of data: the target’s friend data and system data, such as the number of friends. Another interesting work has been proposed in Xie et al. ( 2014 ) where the authors aimed to improve various user tags for a simple image by establishing a collaborative description and making them more meaningful and useful to users.

4.5 Emerging and trending topic

The work proposed in Bao et al. ( 2013 ) was occupied by trending topics. It could be related to important news or subjects being discussed by a large number of microbloggers. The authors supposed that publishing posts on a popular topic proved the user’s interest. The latter are replicated in posts published on the same trending topics. In the work presented in Dang et al. ( 2016 ), the authors aim at emerging topics and are concerned with the substance of important emerging events such as regulatory enforcement, election campaigns, natural disasters, and traffic accidents. Dynamic Bayesian networks are used to uncover developing topics within their methodology. For this, they started the topic diffusion characteristics in the early phase, as well as non-emerging topics with several topology features, and they compared emerging and then, in a given time interval, generated an emerging keyword candidate term list based on term frequency. Next, create a DBN-based model for each candidate to calculate the probability of the candidate emerging as a keyword based on the joint conditional probabilities of the specified features.

5 Social Media influence and literature synthesis

In this section, we proceed to an illustration and a classification of the main relevant works in the literature that are related to the problem treated in this work. The Table  3 presents a summary of the works covered and discussed in this survey. We can see that the majority of previous studies have dealt with the goals and interests of users. Many other studies have addressed the influencers, reasons for interest, and change of all new users.

This Table  3 also presents the approaches used, the experimental results, as well as the years of publication, the authors, and the methodologies adopted. The different approaches to this survey are listed with their concepts. The table also shows the average scores and proof.

For example, in 2013, Bao et al. ( 2013 ) propose a PMF-based method to predict user interests by combining temporal information and social network structure. In this work, the authors confirmed the accuracy of user interest predictions. Then, in 2014, Bian et al. ( 2014a ) focused on the prediction of trending messages and diffusion participants. Three types of influence have been determined: epidemic influence, interest influence, and social influence, which has proven its superiority. In 2015, Han et al. ( 2015 ) were interested in inferring similarities in interests using user demographic information, friendships, and interests. The proposed model was tested on a Facebook dataset. The authors were able to conclude that people with similar demographic information (e.g., age, location) or more mutual friends tend to have more similar tastes. Another interesting work is that of Xiang et al. ( 2010 ). This work consists of modeling the strength or weight of the relationship using a latent variable model on Facebook and LinkedIn. The simulation results of this model showed better autocorrelation and classification performance compared to the results obtained using different raw data elements. The contribution proposed by Zarrinkalam et al. ( 2017 , 2019 )leveraged influencers’ interests in active social media users. By using a graphical description model with three types of information, this model showed its performance. Important work was also proposed by Shah et al. ( 2018 ), where the authors focused on modeling user interests through the social media network graph and the semantic web. This model has also proven its effectiveness. In the same year, i.e., 2018, Wang et al. ( 2018 ) used both content and network structure to extract interest from microblogging and significantly outperform basic methods. Another unsupervised approach that allows inferring user interests from microblogs has been proposed by Budak et al. ( 2014 ). The most direct measure of their inference technique is quality. For the top five interests, this model yielded an accuracy of 0,9. Zhang et al. ( 2015 ) targeted the chronological and content aspects underlying blog data, and a method of analyzing the power of bloggers to predict hashtags has been implemented and tested. This technique has also shown its performance. In 2017, Chader et al. ( 2017 ) show that targeted friendships are not created equal. A methodology for detecting emerging trends has been proposed by Dang Dang et al. ( 2016 ). Their DBN model is based on DBN and uses an early detection strategy based on a dynamic Bayesian network for emerging topics in microblogging networks. They choose features among the topological attributes of the subject distribution and use dynamic changes in the conditional probabilities computed by DBNs to recognize new trends. Jia et al. ( 2017 ) proposed an AttriInfer model to infer user attributes in online social networks using Markov random fields. They incorporated behaviors and social graphics, benefited from both positive people and people with negative training experience, and scaled across large online communities. They proved that the optimized version of AttriInfer is much more flexible than the fundamental version. Li et al. ( 2012 ) proposed their model called “Home Locations Using a Universal and Discriminative Influence Model,” which proved its effectiveness. Saez-Trumper et al. ( 2012 ) tried to find trendsetters in news networks by proposing a robust method. They showed their ability to locate a large portion of trendsetters, regardless of the top 10% of trend adopters, in authentic situations. In their contribution, when nodes with high degree trends are late, the temporal corrosion function diminishes their impact on outcome ranking and helps to underlie variations in the behavior of TS and PR outcomes over time. Rather than using the tags or just the photos, they used both user-generated tags and image correlation. The Multifaceted Folksonomy Graph (MFG) was proposed by Xie et al. ( 2014 ). The Multifaceted Folksonomy Graph (MFG) was proposed. Recently, Wang et al. ( 2020 ), examined the impact of influential leaders on the spread of famous games: Sina Weibo’s Travel Frog and assessed the spread trends, as well as the growth of KOL group networks and keywords UGC. They show that the information propagation event continues via periods of robust and slow forward growth, reaching its peak. In the same year, Mabrouk et al. ( 2020 ) proposed two approaches: the first is a hybridization of ontology and linear SVM, while the second is a hybridization of ontology and FSVM, where they proved that the SVM fuzzy semantic classifier works exceptionally well Mabrouk et al. ( 2018 ). A unique weighted correlated influence metric for Covid-19 is used to identify influential users on Twitter and was also proposed Jain and Sinha ( 2020 ) In this work, the authors verified that the individual with the most followers or the greatest number of tweets is not always the most impactful. Trend-specific influence metrics are insufficient to identify influential users. Harrigan et al. ( 2021 ) attempted to identify influencers on social networks using data widely accessible from the APIs of these networks, and influential mavens have been identified. They found that big social data can be used by decision-makers to identify influential customers. Also, Chia et al. ( 2021 ) proposed searching for the best influencer by combining theories of social capital and socialization with social theories of learning. A complete framework for identifying ideal SMIs has been developed. They proved that the effects of trust and social identification on PMIs’ desire to share unboxing journals in the cognitive dimension of the social capital theory were found to be insignificant. The cognitive factor did not influence the propensity of PMIs to post unboxing notices in the community, unlike the structural and relational dimensions. Finding the most influential entities within the network has proved to be NP-hard. The proposed approach More and Lingam ( 2019 ) provides a balance between influence spread and execution time. Others Yang et al. ( 2014 ) deal with uncovering social network sybils in the wild and terrorist community evolution detection Chaabani and Akaichi ( 2022 ) proposing an artificial bee colony optimization, and then applying the BCTTC to track terrorist evolution. In the work of , Hodas et al. ( 2016 ), an experiment exploring social media usage during disaster scenarios, combining electroencephalograms, personality surveys, and prompts, is proposed. To detect spammer Aswani et al. ( 2018 ) using bio-inspired computing and compromised accounts, the authors Bohacik et al. ( 2017 ); Ouni et al. ( 2022a ) used an anomaly model trained on the previous login data of users. Another important work Arora et al. ( 2019 ) proposes a mechanism for measuring the influencer index across popular social media platforms to identify buzz in social media Aswani et al. ( 2017 ). This is also detected via a hybrid artificial bee colony approach integrated with k-nearest neighbors to identify and segregate buzz and a proposed hybrid bio-inspired approach.

6 Discussion and research challenges

One could wonder if we have adequately addressed the majority of the important issues underlying the identification of influencers, considering the volume of works that have been published in this area, such that the solutions suggested are sufficient for the majority of social network analysis tasks. Yet, in our opinion, a large variety of critical research issues should be solved until we can regard influencer identification as a problem-solving technique. We conclude that other significant fields have not yet been fully explored or studied, such as social network graphs and topology, and the relationship between influencers’ identification and community detection, their networked audience switch MARWICK ( 2013 ) instead of studied fields, of which we have shown some relevant examples. Thus, we ponder unless we have successfully handled almost all of the critical problems related to this subject in a way that the solutions provided seem suitably sophisticated for most social network analysis tasks.

Using social media, users first put their basic information, which can be false, into the social media guide to find their interesting topics. After they add friends and some aims or topics, social media algorithms expand their interests. Social media stars propose helpful suggestions for users to enrich their lives: friends from the same region; old school and faculty; and phone contact. It also suggests hobbies related to his work domain or studies. Then, after setting some preferences, social media stars suggest similar pages and friend types and eliminate ignored suggestions. Its suggestions are based on an intelligent system that analysts change needs, trembling all the time to suggest the most helpful. It certainly encourages users to use it more. The most important thing that users can do here is to search for new aims, new friends, or a new place and all the suggested updates. The challenge was that users could have a period or ephemeral aims. We think time has a big impact on social media users and the importance of a dynamic social media structure for researchers. There are many works in literature dealing with this idea, but no one has put into consideration the social media algorithms and strategies that determine social media influence.

The majority of works in the literature have focused on the identification of the most influential people and significant nodes, called top-k. It represents an interesting research area. Without considering Users can be influenced directly by their friends or groups of friends. They may influence them at the same time. Users share influence. All types of influencers have an important effect and can be the most influential because of their direct contact.

Influence maximization is the trendiest topic that is being exploited and hopes to maximize users’ influence like marketing publicity. But we do not have a search engine that deals with protecting users from negative and also positive users, who may likewise pose a risk by fostering wrath, resentment, hostility, hatred, and indignation, to minimize influence.

User interests that change throughout time and trendy topics have a direct relationship with influence detection by simply comparing users’ aims or interest topics. User interests change according to place or locality and season, influencing them, so we can predict influencers’ node switches. For example, they can also change locales.

Different works deal with the relationship strength and still cannot know the real life and direct relationship. Users may not communicate and discuss clear ideas on social media and may just exchange media or some symbols. Other contributions where the authors have aimed to exploit the users’ locality information without considering that some users have double nationality or can aim with other country topics. The great challenge is, therefore, to find a solution to all these situations. The work of Sinha and Swearingen ( 2001 ) showed that suggestions from subject friends are better than recommender systems. The other challenge now is to study users’ influencers’ friends and predict their new aims.

There are many types of influencers defined as: content creators MARWICK ( 2013 ) digital opinion leaders, moderators Enke and Borchers ( 2019 ) celebrity influencers, mega-influencers, and other works dealing with micro-influencers Campbell and Grimm ( 2019 ); Campbell and Farrell ( 2020 ); Leonardi and Monti ( 2020 ) that can be identified and found. So research is anticipated to rise.

7 Conclusion and prospects

7.1 summary.

Social media has emerged as a popular platform for members to discuss anything, give opinions, and express feelings and important moments about ideas and facts from everyday life Bao et al. ( 2013 ). In this article, we have provided an overview of the current state and future directions of influencer identification on social media. We have reviewed and classified the main existing contributions in the literature into the identification influencers category. At first, we tried to talk more about this domain and alleged it in some works. Then, we cited some other classifications that deal with the same subject, and at least we have applied our classification proposition in some works according to our knowledge. We have proposed some new ideas that can change the orientation of searches on social media users’ interest topics, and why not aim to influence new works?

Our study has some limitations. Most importantly, we did not aim at the structure and topology of social media. We have also noted that we identify significant nodes that influence other nodes in the influenced community and also detect them.

We also know that social media is virtual, but behind this virtual exists a real person whose acts, interactions, and shows are real. They are influenced by internal factors like friends and family, college classmates, and next-door neighbors, and external factors like life coaches, travel bloggers, influence, fashionistas, fitness models, and makeup artists. So we have detailed some important influencers’ identification.

7.2 Prospects

In this state, to conclude our survey, we aimed to synthesize the main research works proposed in the literature, solve the problem of identifying influencers, and present the various algorithms, methods, methodologies, and proposed frameworks. It remains a topical problem, given the importance of the social media community in recent years, and it will continue in the future. Three new avenues may be outlined for this work’s prospects. The first step is to undertake more complete comparison research to provide more information to scholars and particularly on how to find social media influencers. In the second part of the study, we will run more experiments on a multitude of other standard data sets to confirm the principal approaches provided in the literature’s performance and robustness. The third part consists of proposing a new approach to finding new influencers on social networks by using other techniques and criteria based on the new networks’ users’ characteristics.

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Gammoudi, F., Sendi, M. & Omri, M.N. A Survey on Social Media Influence Environment and Influencers Identification. Soc. Netw. Anal. Min. 12 , 145 (2022). https://doi.org/10.1007/s13278-022-00972-y

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Library Dissertation Showcase

Social media influencers, the new advertising agency an exploration into ‘social media influencer’ marketing and its impact on the cosmetics industry.

• Summer Stevenson
• Year of Publication:
• BA (Hons) Business and Management

This dissertation examines the impact of ‘social media influencers’ within the cosmetics industry, with a particular focus toward their role as a marketing agency utilised by businesses to reach their target audiences in a cost-effective manner. In examining this topic previous literature in the field was reviewed, primarily the introduction of Web 2.0. and how it combined with word-of-mouth marketing to produce electronic-word-of-mouth marketing, and thus bring the introduction of social media influencers. Bandura’s (1971) ‘Social Influencer Theory’ as well as the impact of different types of influencer were also reviewed.

This research goes beyond previous literature by examining the under-saturated topic of the negative impact influencers can have over businesses. The research specifically examines the idea that influencers have their own brand image which can contaminate that of the business, causing a decline of sales as consumers do not wish to support brands which they deem immoral. This study was conducted using a quantitative-qualitative, self-conducted questionnaire, in which respondents were asked to rate their opinions regarding a specific statement through use of a Likert scale.

The results show that influencers are able to increase business sales. Primarily, as credibility as a characteristic of influencers was deemed an effective way for influencers to use word-of-mouth marketing. The premise that an influencer could be considered ‘friend-like’ is rejected by respondents. It is also equally accepted that influencers can damage business reputation, as their brand image contaminates that of the business they collaborate with, meaning by representing an ‘immoral influencer’ a business supports immoral activity in the eyes of the consumer. Influencers were also found to damage reputation as many respondents agreed that they engaged in pre-purchase evaluation through watching influencer reviews and could be persuaded not to purchase by a negative one.

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