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

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:

: What are the application areas of social media

user-generated data analytics in real world?

: 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.

200

400

600

800

1000

1200

Areas of application

100

200

300

400

500

600

700

800

Topic

modeling

Sentiment

analysis

Social

Network

Analysis

Spatial

analysis

Temporal

analysis

Approaches

KDIR 2021 - 13th International Conference on Knowledge Discovery and Information Retrieval

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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

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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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

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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