Social Media Analytics: An Overview of Applications and
Approaches
Zeinab Khanjarinezhadjooneghani and Nasseh Tabrizi
Department of Computer Science, East Carolina University, Greenville, NC, U.S.A.
Keywords: Social Media Analysis, Social Media Data, Social Media Analysis Approaches, Applications Area,
Data Analysis, Knowledge Discovery.
Abstract: Users' activities on social media generate useful and valuable information about the general public that can
be used in policymaking and the decision-making processes. In this paper we review social media analysis
approaches and its' application areas by sampling and reviewing studies from IEEE Xplore, Science Direct,
and SpringerLink. The primary focus of this paper is on the role of social media data in extracting, tracking,
and evaluating general public activities, public opinions, and public behaviour. We look at several application
areas, including disaster, urban planning, public health, politics, business, and marketing. The frequently used
approaches in these areas are topic analysis, sentiment analysis, social network analysis, spatial and temporal
analysis. Moreover, this study provides insight for those who wish to learn about social media's role as a data
source for research related to our real-world issues and events.
1 INTRODUCTION
Through social media platforms, people can share
their ideas, thoughts, feelings, pictures, and videos
about different subjects and interact with other
people. People's social media activities generate
valuable data that can be used to discover users'
behaviours, thoughts, emotions, and interactions.
Recently there is a growing trend in using social
media data in different research areas; also, this data
has attracted the attention of many research fields
related to real-world issues and events.
"The process of gathering data from social media
and analysing them to help decision-makers address
specific problems" is called social media analytics
by Lee, I. (Lee, 2018). Social media data can be
considered a data source for data-driven research.
This data will help researchers gain information about
the general public and apply the information to
improve the quality of decisions and policies related
to the general public issues and business goals.
Some review papers studied social media data
analysis from different aspects. Ghani et al. discussed
employing machine learning techniques to analyse
social media big data (Ghani et al., 2019). The authors
in (Al-garadi et al., 2018) discussed the application of
big social media data analysis based on content
analysis and network analysis. These application
areas included health surveillance, political
campaigns, monitoring disasters, detecting threats,
news sources, education sector, information
diffusion, security, internet traffic, and human
behaviour. Also, ( Drus & Khalid, 2019) cited world
events, healthcare, politics, and business as
applications of Twitter data sentiment analysis. The
authors in (Liu & Young, 2018) discussed social
media data's ability to monitor physical activity by
considering topic modelling, sentiment analysis, and
social network analysis (SNA) as the approaches to
study this data. In ( Rousidis et al., 2019), Rousidis et
al. presented a literature review of predictive analysis
with social media by discussing current used
techniques from 2015- 2019. They studied social
media data as a predictive tool in the financial,
marketing, and socio-political contexts.
Many papers on social media analysis methods,
social media analysis application areas, and various
social media analysis tasks have been published over
time. This paper contributes by reviewing published
papers from 2011 to 2020 to look at social media
analysis from a different angle. This paper aims to
identify areas of application of social media analysis
related to real-world issues and events and to identify
the most used analysis approaches in these areas.
Khanjarinezhadjooneghani, Z. and Tabrizi, N.
Social Media Analytics: An Overview of Applications and Approaches.
DOI: 10.5220/0010657600003064
In Proceedings of the 13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2021) - Volume 1: KDIR, pages 233-240
ISBN: 978-989-758-533-3; ISSN: 2184-3228
Copyright
c
 2021 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
233

The rest of this paper is organized as follows:
Research methodology, Social media data analytics
applications, Social media analytics approaches, and
Conclusion.
2 RESEARCH METHODOLOGY
According to the goals stated in the introduction
section, this paper tries to answer the following
questions:
Q
1
: What are the application areas of social media
user-generated data analytics in real world?
Q
2
: What approaches have been used more often to
learn about the public?
We classify papers related to social media data
analytics into 4 categories:
1. Task-oriented research
2. Approach-oriented research
3. Technique- oriented research
4. Application-oriented research
The Task-oriented research focuses on the tasks
related to social media data analytics, such as opinion
mining, rumour detection, and trend detection; the
second group focuses on the approaches that can be
applied to analyse social media data to do these tasks
to extract knowledge. The third group focuses on
techniques and methods used to perform this analysis
approaches. These techniques include methods such
as machine learning, data mining, natural language
processing, and deep learning methods. The
Application-oriented research focuses on the
applications of the extracted knowledge in various
research fields.
This paper answers the research questions by
focusing on the papers published in the application-
oriented and approach-oriented groups. We reviewed
62 related studies among the studies published in
IEEE, Science Direct, and SpringerLink databases
between 2011 and 2020.
Initially, we used the following query strings in
major computer science research databases: “social
media”, “social media analysis” and “social media
analytics”. We sampled studies belonging to the
application-oriented group to find various research
areas that utilized social media data in real-world
investigations.
Next, we constructed new queries by combining
each of these areas with the previous queries. For
example, we used “social media analysis” AND
“public health” (Boolean AND) to find what analysis
approaches have been used. Figure 1 shows the
distribution of papers for each application area.
Again, we created new queries by combining
approaches and first queries such as “social media
analysis” AND “sentiment analysis”. Figure 2 shows
the distribution of papers for each approach.
Figure 1: Number of papers for each application area.
Figure 2: Number of papers for each approach.
When sampling papers for each of the above
steps, we considered papers that focused on
discovering information about the general public for
business goals and general public goals. In addition,
we only considered papers that used social media as
a data source for real-world application areas. We
excluded papers that dealt with issues and tasks
specific to social media.
0
200
400
600
800
1000
1200
Areas of application
0
100
200
300
400
500
600
700
800
Topic
modeling
Sentiment
analysis
Social
Network
Analysis
Spatial
analysis
Temporal
analysis
Approaches
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3 SOCIAL MEDIA DATA
ANALYSIS AREAS OF
APPLICATIONS
There are research areas such as political science,
psychology studies and human behaviour mining
studies, and many others that employ social media
analysis (Topîrceanu & Udrescu, 2018). In this paper,
we are interested in research papers that utilize social
media data to learn about public thoughts, public
actions, and public behaviours to improve strategies
related to the decision-making process for real-world
issues. This section discusses the identified research
areas to answer the first question Q
1
of this study.
3.1 Disaster Studies
Disaster studies use social media data to learn about
human emotions toward disaster, to track human
emotions and behaviour over disaster times, to
understand group behaviour, to track the disaster
situation and to track trends during a disaster
(Kankanamge et al., 2020; Kim & Park, 2020).
The result of these studies can help to improve the
decision-making process related to disaster
management (Shibuya, 2017). For example, (Kim &
Park, 2020) proposed a framework that utilizes a
group-based social network analysis and semantic
analysis to see online group behaviours during a
disaster event. (Kankanamge et al., 2020) studied
Twitter data to determine disaster severity changes
over time and to identify the zones affected by the
disaster. Also, (Li et al., 2017) investigated the human
emotions associated with extreme disasters using data
obtained from Twitter and ReliefWeb using
clustering-based and semantic lexicon LIWC
approaches. This approach also helped to study the
dynamics of human emotional responses such as
sadness, anxiety, and anger during the period of an
entire event. The authors in (Hong et al., 2018), using
spatial and temporal analysis and topic modelling on
Twitter data, investigated emerge, and change of
topics during the snowstorm in urban and rural areas
to find the people's needs for information and
communication with governments.
3.2 Urban Planning Studies
Social media geo-tagged data have been used in some
urban planning studies to gain information on
tracking human activities, determining the
distribution of human dynamics, investigating
transportation behaviour, determining urban land use
and landscape, understanding urban life behaviour,
urban dynamics modelling (Lin & Geertman, 2019;
Abbar et al., 2018; Chen et al., 2019; Miyazawa et
al.,2018). Abbar et al. investigated the geo-tagged
context data from Twitter of two cities of Doha and
London to understand the urban dynamics of cities
(Abbar et al., 2018). (Heikinheimo et al., 2020)
investigated the use of urban green spaces of
Helsinki, Finland, by people. This paper applied
social media data (Twitter, Flicker, and Instagram) to
evaluate this data's ability to answer these questions:
which urban green space do people use, what time,
and how do people use and value urban green spaces.
According to the findings of this study, social media
data revealed patterns of leisure time activities. The
authors in (Chen et al., 2019) mentioned the use of
geo-referenced social media data to identify the urban
spatial structure and urban vibrancy in highly dense
cities. This study showed that social media data
(Facebook check-ins) can be used to indicate human
activities and characterize spatial structures.
Urban planning applies data analytics methods
such as text mining, social network analysis, spatial-
temporal analysis, and descriptive statistical analysis
to get information from social media data.
3.3 Public Health Studies
Social media data can be applied to track and monitor
the disease pandemics, track and detect infectious
disease, identify and track adverse drug reactions, and
analyse people's behaviour to gain information about
their mental health (Al-garadi et al., 2018; Dredze,
2012). For example, Razak et al. in (Razak et al.,
2020) presented a system that can be applied to
analyse depression status using Machine Learning
based on personal Twitter posts. Their proposal
system used rule-based Vader Sentiment Analysis,
Naive Bayes, and Convolutional Neural Network.
There are other studies, such as (Biradar & Totad,
2019) that it also investigated the recognizing
depression from Twitter data to monitor mental
health.
Dredze in (Dredze, 2012) studied how social
media can change public health by discovering the
variant topics related to health on Twitter. This study
applied supervised learning to filter tweets to find
tweets that are related to health, then developed the
Ailment Topic Aspect Model (ATAM) to investigate
these tweets. The result of this study discovered 15
illnesses, such as influenza, insomnia, obesity, dental
problems, and seasonal allergies. Again, Sidana et al.
studied health monitoring on social media over time
by considering health-related topics on twitter and
Social Media Analytics: An Overview of Applications and Approaches
235

applying latent topic analysis methods (Sidana et al.,
2018). They defined the problem as a health transition
detection problem and a prediction problem. Then,
they provided two Ailment Topic Aspect Model
models to address the problems.
Social media also are being used to detect and
explore adverse drug reaction (ADR) (Alimova &
Tutubalina, 2017; Lia et al., 2019; Nguyen et al.,
2017). Alimova and Tutubalina (Alimova &
Tutubalina, 2017) worked on ADR classification
task. They applied Linear SVM and Logistic
Regression classifiers with a set of features including
sentiment and semantic features, word embedding,
and lexicon features and tested on two benchmark
corpora of user reviews and tweets. Many other
studies and research reveal the potential benefits of
social media data to monitor public health and to get
knowledge about public health, to help and to change
health information management, and to affect health
policymaking and decision making (Zhou et al.,
2018).
3.4 Political Studies
The use of social media platform to publish and share
political opinions is not deniable (Karami & Elkouri,
2019). So, it provides chances to use these data in
political studies. By investigating political studies
through social media data, we found that most of
these studies are about mining people's opinions
related to political issues or track public opinions,
detecting political polarization, tracking political
elections and political events, and predicting political
events such as election results.
Sally and Wickramasinghe used Facebook data to
study the trend in Sri Lankan politics over a period
(Sally & Wickramasinghe, 2020). Singh et al., by
using Twitter data related to the 2017 Punjab
assembly elections and applying machine learning
algorithms to do polarity analysis, provided a method
to predict the result of the election (Singh et al.,
2020). Using sentiment analysis, (Oyebode & Orji,
2019) and (Wicaksono et al., 2016) suggested
methods to predict the results of the Nigerian
Presidential Election and the US Presidential
Election. Qi et al. studied Twitter and Reddit data
about Hong Kong protests by sentiment analysis to
track the changes in public sentiment about main
events (Qi et al., 2019). Takikawa and Nagayoshi
study the twitter data to investigate the echo chamber.
They employed social network analysis to detect
communities and applied topic modelling to study the
content of twitter data (Takikawa & Nagayoshi,
2017).
3.5 Marketing Studies
Among the reviewed papers related to social media
analysis application areas, most studies are related to
business and marketing. Some of these studies
investigated social media data to extract information
and knowledge to improve the business management
system, business strategies, and marketing.
Marketers analyse social media data to detect
customers and make advertising, to seek ways to do
marketing about their products, and to understand the
users' views about the products (Sathiyanarayanan et
al., 2019). Customer reviews in social networks
provide the opportunity to gain information about the
users' environment, customer habits, and usage
behaviour (Briele et al., 2019). Social media data can
be used to discover customer behaviours, users'
purchasing behaviours, customer emotions and
opinion (Wieneke & Lehrer, 2016). Social network
analysis can be performed to study the network
structure of social media data to detect the most
important users to do viral marketing. Also, the
authors in (Ahn & Spangler, 2014) created a model to
predict the sale by applying topic analysis and
sentiment analysis with time-series data.
3.6 Business Studies
Business analysts can apply social media data for
business exploration to get information about
products, gain knowledge about corporate, evaluate
the quality of services, and compare the services
provided by different companies and businesses
(Arasu et al., 2020; He et al., 2018). The authors in
(Arasu et al., 2020) proposed the use of the WEKA
machine learning tool to develop the social media
marketing strategy by predicting online consumer
behaviour. The authors in (Ahmad et al., 2019) tried
to provide suggestions to help businesses develop
their business strategies by analysing and comparing
the social media data content of the business
competitors.
Social media data provide a valuable source of
data for businesses and industries which need this
data to improve their business strategies by learning
about general public for specific goals. The authors in
(Yan & Subramanian, 2018) investigated the
potentiality of social media analysis in tourism
industry in UAE. The authors in (Serna et al., 2017)
studied the bicycle role for transportation in
sustainable tourism by applying sentiment analysis
approach. This paper used data from TripAdvisor,
Twitter, and Facebook about bicycle-sharing system
(Serna et al., 2017).
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236

4 SOCIAL MEDIA ANALYSIS
APPROACHES
Considering that we mainly focus on the text as user-
generated data, we choose five social media analysis
approaches to answer question Q
2
of this study. These
approaches can help to extract valuable knowledge
from social media data to learn about the public.
4.1 Topic Modelling or Topic Analysis
Monitoring human behaviour and human opinion,
tracking different topics and events on social media,
and detecting topical trends on social media provide
useful information for various research areas such as
political studies, public health research, and disaster
management studies. Topic modelling is one of the
approaches that can be used to perform the above
tasks. Topic modelling is a statistical model for
identifying the latent variables or topics from large
datasets (Vayansky & Kumar, 2020; Ko et al., 2017).
The aim of topic modelling is to detect patterns and
discover the relationship among data from a
collection of text documents, and in social media data
analytics, the purpose is to use these obtained topics,
to show the trends and hot topics (Jelodar et al., 2019;
Hidayatullah et al., 2018). The topic modelling
approach can discover the discussions about a
specific subject on social media to understand
people’s opinions towards that subject.
Jeong et al. in (Jeong et al., 2019) by applying
Latent Dirichlet Allocation (LDA)-based topic
modelling and sentiment analysis presented a social
media mining approach for product planning. (Deng
et al., 2020) proposed a multi-level LDA topic model
to analyse social media data to understand people's
concerns during disasters caused by humans. It used
this model to identify the public's information needs
and the evolution of these needs over time.
4.2 Sentiment Analysis
Sentiment Analysis (SA) is another helpful analytics
approach in social media data analysis that helps to
convert user-generated data to useful information.
The sentiment analysis method performs the
classification task. It aims to detect and interpret
people's opinions, emotions, and attitudes towards
any particular topic by classifying the input data into
positive, negative, or neutral sentiments (Drus &
Khalid, 2019; Sathya et al., 2019).
Opinions are valuable knowledge that has a
significant role in some tasks, such as planning,
reasoning, and decision-making (Zul et al., 2018) (Li
et al., 2019). The authors in (Drus & Khalid, 2019)
referred to health, business and marketing, politics,
and public action as the application of SA, and it can
be used to study world events such as a disaster. The
authors in (Singh & Wu, 2021) applied SA on
Tweeter data to study substance abuse during
COVID-19, which is one of the global healthcare
system concerns. (Raginia et al., 2018) proposed a
method based on SA of social media data to
understand people’s needs and classify the needs
during times of disaster to improve the helping
process.
4.3 Social Network Analysis
Social network analysis (SNA) represents the users
and actors as nodes. It shows connections and
interactions as edges, and it studies the structure of
these nodes and their links based on social network
theory (Lemay etal., 2019; Mrsic et al., 2019; Lee,
2018). SNA is used to display and visualize the
network, leading to understanding the circulation of
information and interactions in the network. SNA is
used to improve the strategies to expedite the
circulation of information about a subject. And SNA
can be applied to find influential users or groups that
have strong influences on other users in a network
(Lemay et al., 2019) (Pudjajana et al., 2018). The
authors in (Struweg, 2020) applied SNA to study the
spread of the announcement of the South African
health insurance bill on Twitter. The authors in
(Huang & Chiu, 2020) employed SNA in mining
public opinion and needs on health issues. They used
SNA to investigate the interaction between health
agencies and the public through the health-related
policy posts on Facebook.
SNA, along with content analysis, has been used
in some research areas to improve decisions and
strategies. For example, Kim and Hastak in (Kim &
Hastak, 2018) applied SNA on Facebook data of the
city of Baton Rouge, which are after the 2016
Louisiana flood, to extract knowledge from
emergency social network data. The goal was to help
emergency agencies develop their social media
operation strategies to lead to a better disaster
mitigation plan. Also, authors in (Van der Zee &
Bertocchi, 2018) applied SNA on user-generated
content on TripAdvisor to study tourists' behaviour
for a single urban tourist destination. They stated that
the extracted knowledge can help to improve
destination management strategies in distributing
tourists in urban tourism spots of an area.
Social Media Analytics: An Overview of Applications and Approaches
237

4.4 Spatial and Temporal Analysis
The spatial and temporal analysis study the users' data
to extract spatial and temporal information and
features of data. These analyses can reveal the
temporal pattern and geographical information of
data (Gosal et al., 2019; Zhao, 2018). Check-in and
Geo-tagged social media data can be scrutinized by
applying spatial and temporal analysis to provide
valuable information about human mobility activities,
the spatial distribution of public opinion, and the
emergence, evolution, and decline of public opinion
over time (Yao et al., 2018; Zhu et al., 2020). For
example, the authors (Wu et al., 2020), by applying
spatial and temporal analysis, studied public
behaviour in disasters. They employed LDA topic
modelling and DBSCAN clustering and Markov
transition probability matrix to perform spatio-
temporal analysis in their proposed framework. The
authors in (Zhu et al., 2020), studied social media data
about COVID-19 by topic mining, text sentiment, and
spatial-temporal analyses. They tried to discover
information about public opinion toward COVID-19.
Also, the authors in (Gong et al., 2020) mentioned the
use of social media data in crowd managing in cities
during city events. This paper explored the social
media data by employing temporal analysis with
other analysis methods to understand crowd and
pedestrian behaviours.
5 CONCLUSIONS
In this paper we discuss the social media analysis role
in discovering information about the general public to
improve decision-making for real-world issues. We
review 62 papers by sampling published papers
between 2011- 2020. This study focuses primarily on
user-generated data and network structure as data
because of their abilities to reveal information about
the general public that is used in decision-making and
planning for real-world problems.
For areas of applications of social media analysis
related to real-world concerns, there are disaster
studies, urban planning studies, public health studies,
political studies, and business and marketing
studies. And the most common data analysis
approaches used in these areas are topic analysis,
sentiment analysis, SNA, spatial and temporal
analysis. Applying a proper mix of these approaches
for each of these application areas can provide a
powerful tool to achieve meaningful information and
knowledge about the public for a target application.
The spatio-temporal analysis is rarely used alone. It is
used in most papers along with other approaches.
As a future research goal, social media data's
problems and limitations to cover the data-driven
research for real-world issues should be investigated.
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