=Paper=
{{Paper
|id=Vol-3067/paper10
|storemode=property
|title=Social media influence analysis Techniques Systematic Literature Review
|pdfUrl=https://ceur-ws.org/Vol-3067/paper10.pdf
|volume=Vol-3067
|authors=Yosr Sahnoun,Mariam Chaabane,Ismael Bouassida Rodriguez
|dblpUrl=https://dblp.org/rec/conf/tacc/SahnounCR21
}}
==Social media influence analysis Techniques Systematic Literature Review ==
https://ceur-ws.org/Vol-3067/paper10.pdf
Social media influence analysis Techniques
Systematic Literature Review
Yosr Sahnoun1 , Mariam Chaabane1 and Ismael Bouassida Rodriguez1
1
ReDCAD, University of Sfax, Sfax, Tunisia
Abstract
Nowadays, the use of Social Media networks is growing endlessly and rapidly, those networks have
become a substantial pool for unstructured data. Social media influence (SMI) describes the social
media influencers (SMIs) capacity to influence other people’s thinking, feelings and characteristics in
online and outline communities. The analysis of the influence activity affects all different kinds of
fields from multiple perspectives such as strategic planning and decision making until product creation
and distribution. The main contribution of this paper is to present results of a Systematic Literature
Review (SLR) that highlights the different Techniques used in the analysis of social media influence,
when addressing influencers-followers interactions. After a careful review of the 55 extracted articles,
we found that 4 data representation models have been used with social media related data analysis and
10 data analysis techniques to address 6 different research objectives in more than 20 different fields. In
Interactions and users relationships purpose, Graph was the most used data representation model and
data analysis techniques.
Keywords
Influence, Systematic literature review (SLR), Social media influence (SMI), Data analysis techniques
1. Introduction
According to the Statista report [1], over 3.6 billion people were using social media worldwide,
a number projected to increase to almost 4.41 billion in 2025. The report shows that 4.57 billion
people around the world use the internet, of those users, 346 million new users have come
online within the last 12 months. Internet users spend an average of 144 minutes on social media
per day. The process of analyzing or mining social networks helps in gathering information
that optimize influence maximization. People use Social Media Platforms to connect with
their friends and family members, to introduce themselves to others by sharing their daily
live news and follow channels or pages. This spontaneous behavior created what we now call
influencers and followers. Which change the way that organisations connect with their clients.
Our Study helps in choosing the appropriate representation model and analysis technique that
matches the analysis purpose and goes with certain platforms. The better way to answer a
questions of effectiveness comparing more than one different bath is Systematic Literature
Reviews. This paper is organized as follows. In Section 2, we represent the Systematic Literature
Tunisian Algerian Conference on Applied Computing (TACC 2021), December 18—20, 2021, Tabarka, Tunisia
$ Yosr.Sahnoun@redcad.tn (Y. Sahnoun); Mariam.Chaabane@redcad.tn (M. Chaabane); bouassida@redcad.tn
(I. Bouassida Rodriguez)
© 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
Review planning. Results and Discussion are presented in Section 3. We conclude with general
highlights and perspectives for further work in Section 4.
2. Systematic Literature Review Planning
2.1. Research Questions
Aiming to find all relevant primary studies related to the different types of models used to
describe the phenomenon of influence in social media networks, the following research questions
(RQ) were established:
• RQ1: Which techniques have been used to analyse users related data in social media?
• RQ2: What is the purpose of analyzing users related data in social media?
• RQ3: How is it possible to use analyzing techniques when addressing influencers/followers
interactions?
Subsequently, we determined the initial research in the databases. In relation to the keywords,
three groups were formed:
• Groupe1: (“social media",“social networks")
• Groupe2: (“analysis", “analytics", “analyzing", “analyze", “content analysis")
• Groupe3: (“influencers",“influencer", “followers" ,“follower")
2.2. Search Strategy
The search strategy combines the key concepts of our search question in order to retrieve
accurate results. It is an organized structure of key words, which are “social media”, “analysis”,
“influencer” and “followers”, used to search a database. Then, we added synonyms, variations,
and related terms for each keyword. A Boolean operator (AND and OR) allow us to try different
combinations of search terms.The final search string is (“social media” OR “social networks”)
AND (“analysis” OR “analytics” OR “analyzing” OR “analyze” OR “content analysis”) AND
(“influencers” OR “influencer” OR “followers” OR “follower”).
2.3. Selection Criteria
After obtaining the search results from the different sources, a set of exclusion/ inclusion criteria
was applied to help in the identification of relevant primary studies. Therefore, Inclusion Criteria
(IC) are used to select primary studies which indicate Related data analysis techniques, purpose,
or influencers/followers interactions for Social media networks. For the Exclusion Criteria (EC)
they are used to remove those primary studies that do not address the main topics searched in
this SLR, are not available, or are directly related to an included primary study of the same author.
• Inclusion Criteria (IC):
– Publications that match one of the search items
– Publications that have best practices version
– Publications that are related to social media networks related data analysis
– Publications that are related to the phenomenon of influence in social media net-
works
– Publications that are relate to the research questions
• Exclusion Criteria (EC):
– Publications that not match one of the search items
– Publications that do not have best practices version
– Publications that are published before or on the 31.12.1999
– Publications that are not related to the phenomenon of influence in social media
networks
– Publications that are not relate to the research questions
2.4. Data collection
The number of papers resulting in the search is summarized in TABLE 1. After filtering irrelevant,
duplicate and incomplete papers, a total of 55 papers in TABLE 3 were selected for the reviewing
process. TABLE 2 presents the filtering process. The state of the art is presented as follows
according to the different cases. The selected papers per resources are distributed as shown in
TABLE 3.
Resource Number of papers
Springer 111
IEE Xplore Digital Library 1
ACM Digital library 191
Google Scholar 30
Science Direct 157
Hyper Articles en Ligne (HAL) 48
Total 538
Table 1
Search results by Resource
Irrelevant and duplicates 3
Incomplete and not related to RQ, Excluded by reading title and abstract 452
File not found 2
Total for Introduction reading 68
Not related to RQ, Excluded by reading Introduction 30
Total for reading 55
Table 2
Filtered search results
Resource Number of papers
Springer 7
IEE Xplore Digital Library 5
ACM Digital library 17
Google Scholar 9
Science Direct 14
Hyper Articles en Ligne (HAL) 5
Total 55
Table 3
Filtered search results by Resource
3. Results and Discussion
3.1. Social Media Users Related data analysis techniques
3.1.1. Data model:
When we start defining the Social Media Users Related data analysis techniques we found that
the first thing we need to see is the real-state of the abstract data or the data model that defines
the initial information. There are different models to represent it, we chose to classify them
into 4 main categories summarized in TABLE 4.
Data model Numbers of Papers Percentage
Graphs 33 73.33%
Dataset 9 20%
Log 7 15.45 %
Diagrams 6 13.33%
Table 4
Data models used in social media related data analysis
Graphs: are the most common used data model. Each Graph has his own parameters related
to the topic and the field of search. The social networks is the top sous-category with more than
15 articles [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18], it defines a social structure made
up from a set of different social actors. Some authors create a specific model such as Small-world
network multiple influence model (SWMI model) [19], Community-Author-Recipient-Topic
model (CART) [20], RT and MT model [4]. There are some types of Networks related only to
influence analysis like User-Follower Network [21], influencer Graph [22]. The general ones are
Graph Neural Networks (KOLs), Knowledge Graph(KG) [23], Random geometric graph, random
networks [24] and Network diagram [25]. Peng, Sara, Taeho, Hui, Krishna, Xiang-Yang, Kevin
and al. [26, 9] used Social Graph that represents social relations between entities. Some authors
mix between more then one representation. Marco and Mattia [27] used Graph Representation
Learning. Lauren, Robert, Augustin and Eugene [3] used network diagram of operation model
and Graph-based representation for trust/reputation systems. Fan and Cassandra [28] used One-
mode and two-mode network, interaction networks and class-level and group-level discussion
networks. Mozhgan and Kevin C. [29] used Graph-based data representation and Hypergraph
data representation.
Datasets: are the second category, each platform has a specific type of data. Twitter dataset
[30] is compiled from various tweets which centered on topics, hashtags, and objects. Epinions
dataset [31, 32] is the organization of data into incremental snapshots. Facebook dataset [33]
is more complicated, representing the number of acting users, number of users that reacted,
number of posts, number of comments and time span of data. Some datasets are related to the
real-world for example to study the Location-based social networks (LBSN) [34].
Logs: category has 3 sub-category. Venkata, Weizhong and Xiaowei [21] used logarithms in
comparing the results founded in the Twitter User-Follower network follows power-law degree
distribution. Also, Simone, Diego, Giuseppe and Maurizio [35] used logarithms in his study.
The second sub-category is Big data which refers to the large, fast or complex type of data
that it’s difficult or impossible to process using the traditional methods. There are four papers
mention this type [12, 36, 37, 38]. The last sub-category is Clustering [13], it is used to extract
the Trusted and Non-Trusted nodes.
Diagrams: category results to a schematic representation. Mozhgan and Kevin C. [29] used
Fuzzy models and diagrams that look like Graph. Sunagul [5] used Sociogram, it is a graphic
representation of social links that a person has. Also Lars and Francis applied Sociogram that
represent participant’s friendship network. Marco and Mattia [39] used schematic diagram
using social relation.
3.1.2. Data Analysis model
In this part we will focus on the different data analysis model used to analyse Social Media
Users Related data. We propose 10 main categories represented in TABLE 5. Probabilistic model
and Graphs are the earliest used analysis techniques. Every work ad at least one of the three
statistical categories: tables, curves and histograms. The most used category is Analysis model.
The latest work used mining techniques like Artificial intelligence (AI) and Prediction.
Data Analysis model Numbers of Papers Percentage
Analysis model 26 49.05%
Tables 19 35.84%
Curves 18 33.96%
Ranking metrics 16 30.18%
Artificial intelligence(AI) 12 22.62%
Graphs 11 20.75%
Histograms 6 11.32%
Prediction 6 11.32%
Probabilistic model 5 9.43%
numeric coefficient 4 7.54%
Table 5
Data Analysis models/Techniques used in social media related data analysis
Analysis model: is a technical representation that results from designing a model that
analyse different information, behavior, function, interaction or relations. The ones related to
influence are: Influence model [40, 37, 11] Social media Influencer Model[25], Action-Reaction
Influence Model (ARIM) [33, 8], Influence evaluation model [2] and Influence model for sen-
timent inference [10]. Others are related to topics that helps in decisions making such as
recommendation model [36], Latent Dirichlet Allocation topic model [41], Graphical repre-
sentation of Latent Dirichlet Allocation [26], Multi-Criteria Decision Making model (MCDM)
[37] and numerous theoretical models [30, 11, 38]. Other types are Diffusion models like Flow
diagrams and Block diagrams [29, 26, 22], Tow-step-flow model, multi-step-flow model [4],
Filtering model [30, 38] and Emerging Model [42]. Also there is models related to social or
marketing analysis: The inbound and outbound models [43], Alternative Communications
Model theory [44], Covariance-based structural equation modeling (CB-SEM) [42] hypothesized
mediation model [17] and Social Information Retrieval [45].
Tables: are used to represent comparisons e.g. Country-Level Micro-blog User Behaviour
and Activity [39], Models on top-K Recommendation [23] Top 10 users different diagrams [37],
Direct Trust in communities and network [13], classifiers [35] and probability distributions by
groups [46]. Also, to describe number of users, location, visits and clustered locations[34]. To
present results like Words frequency[38], Linear Regression Results [9] Kendall Rank Correlation,
Hierarchical Multiple Regression [12]. Finally, to list results like randomly picked communities
from the observed communities [21].
Curves: are used to represent correlations in most of them e.g. correlation strength between
different countries [39], Pearson’s Correlation [47], descriptive statistics of key study variables
[14], Descriptive correlations [17] and Correlation between user features [48]. There are analysis
models used to extract those representation like: Principal component analysis [49], Diffusion
models [26], Bilinear state space model (BSSM) [11], Empirical model [50], Model R, model
R-Squared, Adjusted R-Squared [15], Bass model [19] and Opinion dynamics models [18]. They
are also used to represent evaluations like: Average week-on-week growth rates, Social media
followers by week [50] and Evolution of retweeting network [48] or to represent Influence
probability [34]. Sara, Taeho, Hui, Krishna, Xiang-Yang and Kevin [9] used Life Curve. Saike,
Xiaolong, Danie, Kainan, Zhu and Chuan [11] used ROC curve.
Histograms: are used to represent quantity evolution throw time e.g. Histogram of the
Influence Score [9], Absolute and Relative influence [34], account creation dates for Twitter
followers of incumbent US senators campaigning in 2018 [51] and probability of influence
relationship [10]. Also to show Participant centralities for different networks [28].
Ranking metrics: are an algorithms used to rank components of social media like tweets
[39, 12, 46], posts, Hashtags, Trends [52], Page rank [8] or even influencers and followers
ranking. They are often used to analyse users related data in Twitter. Also we can use them to
rate parameters like frequency and percentage e.g. the frequencies for the component items of
Twitter and YouTube use [15], The percentage of users following at least one of the top (key)
opinion leaders [23], Percentage distribution of top influencers [41]. The most used techniques
to characterize top users are ranking metrics like Swiss Market Index (SMI) [4] and NavigTweet
[53]. This technique is also used to realize comparisons between the most powerful influencers
according to betweenness centrality and Page Rank and worth mentioning the hashtags [5].
Artificial intelligence(AI): analysis techniques has started in recent years. There are 5
sup-categories: (1) Clustering e.g. K-means clustering method [27], Distribution of nearest
neighbors of regional network nodes [39] and K-means clustering method [39]. (2) Machine
learning techniques [30, 38]. (3) Heuristic model e.g. energy-propagation model [9], influence
propagation model [2] and diffusion model based on cascade model [48]. (4) Mining techniques
like Mining Micro-Influencers [35]. (5) knowledge engineering e.g. Knowledge representation
and reasoning [39].
Graph: are not only used for primary data extraction but also for deep learning methods
to describe data by graphs designed. There are a multitude of model to design graph such
as: Independent Cascade Model [44, 2], Linear Threshold Model [9], Heterogeneous Influence
graph model [30] and Trust Model (SNTrust) [13]. Also there are a different types of networks
such as: Bayesian network [29], Two-link network topology, Parallel link topology [24] and
social network [44]. Those models also used to extract Social Network graph density reduction
[20] and Instructor’s centralities [28].
Prediction: are models used to give a future vision or estimation. The most used prediction
modelis the Standing Ovation model (SOM) [30, 38]. Daekook, Bomi, Byoungun, Youngjo and
Yongtae [19] creates more then one estimation to compare between them. Paul, Liam and Jordi
[50] used difference-in-difference models of social media followers to analyse the social media
music fans followers future behavior.
Probabilistic model: in the field of social media analysis are used for different purposes: (1)
mining structural influence to analyze relationships in social network [47], (2) identification of
influencers in online social networks [20], (3) analyzing dynamics of information diffusion [48],
(4) evaluating Role of Conformity in Opinion Dynamics in Social Networks [18], (5) modeling
Topic [44].
Numeric coefficient: are used for different reasons one of them is to represent the size of
an individual’s social network and their ability to influence that network [40]. Also, to show
how influence scores change [6]. Some of the authors use numeric coefficient to rate the most
frequent words and User quality ratio vs. RT Quality ratio vs. Replay ratio for the Top users
[30]. Pearson Coefficient [54] is one of the most used coefficients.
3.2. Social Media Users Related data analysis purpose
Reading all the 55 articles we found six main important reasons presented as follow in TABLE 6
behind analyzing users related data.
Analysis main purpose Numbers of Papers Percentage
Interactions and users relationships 13 23.64%
Influencers Behaviour 13 23.64%
Influence Modeling 10 18.18 %
Influence Evaluation 9 16.36%
Mining Influence 6 10.91%
Influence Optimisation 4 7.27%
Table 6
Summarizing social media related data analysis purposes
3.3. Social Media Users Related data analysis search Fields
After deduce the six main reasons, we found that each of them is related with a search field
or maybe more than one. While reading other SLR related to the social media analysis they
frequently mention that there is an extensive variety of fields benefit from social media related
data analysis, but most of them chose to focus between one, two and three domains. Based
on 3.0 Detailed (four digit) subject codes, we extract 23 fields deduct from 4 main fields: (1)
Humanities and social science, (2) Natural sciences, (3) Formal sciences and (4) Professions
and applied sciences, more than 60 sub-field and more than 100 sub-sub-field. We notice that
it affects and optimises all different kinds main fields. The four top fields are Sociology with
41.81%, Business with 36.36%, Computer sciences 30.90% and Interdisciplinary studies with
27.26%.
3.4. Social Media Users interactions analysis techniques
After extracting the 6 main purposes of analysing social media users related data we found that
the top of them is: Interactions and users relationships with a total of 13 articles (bibliographic
portfolio). More than 61% of those papers used Graphs as a representation, 23% used Dataset,
15% used Logs and 8% used Diagrams. Coming to the analysis techniques most of the authors
prefer to combine between more than one techniques. Nadia, Mourad, Lin, Ben, Yousra, Ahmad
Kamran, Basit, Ahmad Raza, Fan and Cassandra used Graphs [10, 47, 13, 28]. Monika, Amel,
Katarzyna, Alda, Nadia, Mourad, Lin and Ben [33, 10, 47] used analysis Model. Marco, Mattia,
Lauren, Robert, Augustin and Eugene [27, 3] used Ranking Metrics. For the statistic techniques:
Yousra, Ahmad Kamran, Basit, Ahmad Raza, Venkata, Swamy, Weizhong and Xiaowei [13, 21]
used tables. Yousra, Ahmad Kamran, Basit, Ahmad Raza, Lin, Ben, Faisal M., Ramaravind and
Joyojeet [54, 47, 13] used curves. Nadia, Mourad, Fan and Cassandra [10, 28] used Histograms.
Marco and Mattia [27] added Artificial Intelligence, Lin and Ben [47] added Probabilistic Model
and Prediction also Faisal M., Ramaravind and Joyojeet [54] added Numeric Coefficients. The
most used platforms for interactions analysis are Twitter and Facebook, but in 2019 researchers
are concentrated more on Instagram. For the fields there are a diversity in the chosen fields but
the most figured ones were Sociology and Business.
4. Conclusion
The present Systematic Literature Review sought to contribute in the identification of users
related data analysis techniques as well as to present the analysis main purpose and the fields
that were most engaged regarding social media influence. This review consisted of literature
published between 2000 and 2020. We did list 4 data representation models, 10 data analysis
techniques, 6 main purposes behind social media related data analysis and more than 20 fields
were extracted. The most used analysis techniques were Graphs as for the data representation
model. The most-frequently appeared purpose was interactions and users relationships analysis.
For the fields we found that Sociology, Business and Computer sciences are the top connected
fields with social media industry. As a future work, we plan to develop a mining approach to
extract the interaction model between influencers and followers, using graphs. Further more
we will apply graph matching and transformation techniques on the interaction model, using
GMTE, for analysis purposes.
Acknowledgments
This work was partially supported by the LABEX-TA project MeFoGL:"Méthodes Formelles
pour le Génie Logiciel".
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