bluuebunny/crossref_metadata_2025 huggingface dataset:
Feature Optimization in Sentiment Analysis by Term Co-occurrence Fitness Evolution (TCFE)
Authors: Sonawane Sudarshan S. , Kolhe Satish R.
Published: July 2022

The opinion of a target audience is a major objective for the assessing state of efficacy pertaining to reviews, business decisions surveys, and such factors that require decision making. Feature selection turns out to be a critical task for developing robust and high levels of classification while decreasing training time. Models are required for stating the scope for depicting optimal feature selection for escalating feature selection strategies to escalate maximal accuracy in opinion mining. Considering the scope for improvement, an n-gram feature selection approach is proposed where optimal features based on term co-occurrence fitness is proposed in this article. Genetic algorithms focus on determining the evolution and solution to attain deterministic and maximal accuracy having a minimal level of computational process for reflecting on the sentiment scope for sentiment. Evaluations reflect that the proposed solution is capable, which outperforms the separate filter-oriented feature selection models of sentiment classification.
Classification of Code-Mixed Bilingual Phonetic Text Using Sentiment Analysis
Authors: Singh Shailendra Kumar , Sachan Manoj Kumar
Published: July 2022

The rapid growth of internet facilities has increased the comments, posts, blogs, feedback, etc., on a large scale on social networking sites. These social media data are available in an unstructured form, which includes images, text, and videos. The processing of these data is difficult, but some sentiment analysis, information retrieval, and recommender systems are used to process these unstructured data. To extract the opinion and sentiment of internet users from their written social media text, a sentiment analysis system is required to develop, which can work on both monolingual and bilingual phonetic text. Therefore, a sentiment analysis (SA) system is developed, which performs well on different domain datasets. The system performance is tested on four different datasets and achieved better accuracy of 3% on social media datasets, 1.5% on movie reviews, 1.35% on Amazon product reviews, and 4.56% on large Amazon product reviews than the state-of-art techniques. Also, the stemmer (StemVerb) for verbs of the English language is proposed, which improves the SA system's performance.
An Extensive Text Mining Study for the Turkish Language
Authors: Şahin Durmuş Özkan , Kılıç Erdal
Published: July 2022

In this study, the authors give both theoretical and experimental information about text mining, which is one of the natural language processing topics. Three different text mining problems such as news classification, sentiment analysis, and author recognition are discussed for Turkish. They aim to reduce the running time and increase the performance of machine learning algorithms. Four different machine learning algorithms and two different feature selection metrics are used to solve these text classification problems. Classification algorithms are random forest (RF), logistic regression (LR), naive bayes (NB), and sequential minimal optimization (SMO). Chi-square and information gain metrics are used as the feature selection method. The highest classification performance achieved in this study is 0.895 according to the F-measure metric. This result is obtained by using the SMO classifier and information gain metric for news classification. This study is important in terms of comparing the performances of classification algorithms and feature selection methods.
Learning Algorithms of Sentiment Analysis
Authors: Acampa Suania , De Falco Ciro Clemente , Trezza Domenico
Published: July 2022

The uncritical application of automatic analysis techniques can be insidious. For this reason, the scientific community is very interested in the supervised approach. Can this be enough? This chapter aims to these issues by comparing three machine learning approaches to measuring the sentiment. The case study is the analysis of the sentiment expressed by the Italians on Twitter during the first post-lockdown day. To start the supervised model, it has been necessary to build a stratified sample of tweets by daily and classifying them manually. The model to be test provides for further analysis at the end of the process useful for comparing the three models: index will be built on the tweets processed with the aim of detecting the goodness of the results produced. The comparison of the three algorithms helps the authors to understand not only which is the best approach for the Italian language but tries to understand which strategy is to verify the quality of the data obtained.
Integrating Feature and Instance Selection Techniques in Opinion Mining
Authors: You Zi-Hung , Hu Ya-Han , Tsai Chih-Fong , Kuo Yen-Ming
Published: July 2022

Opinion mining focuses on extracting polarity information from texts. For textual term representation, different feature selection methods, e.g. term frequency (TF) or term frequency–inverse document frequency (TF–IDF), can yield diverse numbers of text features. In text classification, however, a selected training set may contain noisy documents (or outliers), which can degrade the classification performance. To solve this problem, instance selection can be adopted to filter out unrepresentative training documents. Therefore, this article investigates the opinion mining performance associated with feature and instance selection steps simultaneously. Two combination processes based on performing feature selection and instance selection in different orders, were compared. Specifically, two feature selection methods, namely TF and TF–IDF, and two instance selection methods, namely DROP3 and IB3, were employed for comparison. The experimental results by using three Twitter datasets to develop sentiment classifiers showed that TF–IDF followed by DROP3 performs the best.
A Machine Learning-Based Lexicon Approach for Sentiment Analysis
Authors: Sahu Tirath Prasad , Khandekar Sarang
Published: July 2022

Sentiment analysis can be a very useful aspect for the extraction of useful information from text documents. The main idea for sentiment analysis is how people think for a particular online review, i.e. product reviews, movie reviews, etc. Sentiment analysis is the process where these reviews are classified as positive or negative. The web is enriched with huge amount of reviews which can be analyzed to make it meaningful. This article presents the use of lexicon resources for sentiment analysis of different publicly available reviews. First, the polarity shift of reviews is handled by negations. Intensifiers, punctuation and acronyms are also taken into consideration during the processing phase. Second, words are extracted which have some opinion; these words are then used for computing score. Third, machine learning algorithms are applied and the experimental results show that the proposed model is effective in identifying the sentiments of reviews and opinions.
Sentiment Analysis of Brand Personality Positioning Through Text Mining
Authors: Lu Ruei-Shan , Tsao Hsiu-Yuan , Lin Hao-Chaing Koong , Ma Yu-Chun , Chuang Cheng-Tung
Published: July 2022

This article uses text mining and a Chinese word segmentation program developed by the Chinese Knowledge and Information Processing Group in Taiwan's Academia Sinica to analyze Facebook posts from 14 e-commerce companies. In addition, a list of keywords representing brand personalities is analyzed to reveal key factors affecting which social media posts attract consumers' attention. This research uses statistical analysis with a nonmanual questionnaire that is efficient and based on computer science to provide a reference for businesses operating Facebook fan pages and internet marketing.
Machine Learning in Sentiment Analysis Over Twitter
Authors: Zerrouki Kadda
Published: July 2022

Social networks are the main resources to gather information about people's opinions and sentiments towards different topics as they spend hours daily on social media and share their opinions. Twitter is a platform widely used by people to express their opinions and display sentiments on different occasions. Sentiment analysis's (SA) task is to label people's opinions as different categories such as positive and negative from a given piece of text. Another task is to decide whether a given text is subjective, expressing the writer's opinions, or objective. These tasks were performed at different levels of analysis ranging from the document level to the sentence and phrase level. Another task is aspect extraction, which originated from aspect-based sentiment analysis in phrase level. All these tasks are under the umbrella of SA. In recent years, a large number of methods, techniques, and enhancements have been proposed for the problem of SA in different tasks at different levels. Sentiment analysis is an approach to analyze data and retrieve sentiment that it embodies. Twitter sentiment analysis is an application of sentiment analysis on data from Twitter (tweets) in order to extract sentiments conveyed by the user. In the past decades, the research in this field has consistently grown. The reason behind this is the challenging format of the tweets, which makes the processing difficult. The tweet format is very small, which generates a whole new dimension of problems like use of slang, abbreviations, etc. The chapter elaborately discusses three supervised machine learning algorithms—naïve Bayes, k-nearest neighbor (KNN), and decision tree—and compares their overall accuracy, precisions, as well as recall values; f-measure; number of tweets correctly classified; number of tweets incorrectly classified; and execution time.
Building Sentiment Analysis Model and Compute Reputation Scores in E-Commerce Environment Using Machine Learning Techniques
Authors: Elmurngi Elshrif Ibrahim , Gherbi Abdelouahed
Published: July 2022

Online reputation systems are a novel and active part of e-commerce environments such as eBay, Amazon, etc. These corporations use reputation reporting systems for trust evaluation by measuring the overall feedback ratings given by buyers, which enables them to compute the reputation score of their products. Such evaluation and computation processes are closely related to sentiment analysis and opinion mining. These techniques incorporate new features into traditional tasks, like polarity detection for positive or negative reviews. The “all excellent reputation” problem is common in the e-commerce domain. Another problem is that sellers can write unfair reviews to endorse or reject any targeted product since a higher reputation leads to higher profits. Therefore, the purpose of the present work is to use a statistical technique for excluding unfair ratings and to illustrate its effectiveness through simulations. Also, the authors have calculated reputation scores from users' feedback based on a sentiment analysis model (SAM). Experimental results demonstrate the effectiveness of the approach.
Sentiment Analysis as a Restricted NLP Problem
Authors: Kumar Akshi , Gupta Divya
Published: July 2022

With the accelerated evolution of social networks, there is a tremendous increase in opinions by the people about products or services. While this user-generated content in natural language is intended to be valuable, its large amounts require use of content mining methods and NLP to uncover the knowledge for various tasks. In this study, sentiment analysis is used to analyze and understand the opinions of users using statistical approaches, knowledge-based approaches, hybrid approaches, and concept-based ontologies. Unfortunately, sentiment analysis also experiences a range of difficulties like colloquial words, negation handling, ambiguity in word sense, coreference resolution, which highlight another perspective emphasizing that sentiment analysis is certainly a restricted NLP problem. The purpose of this chapter is to discover how sentiment analysis is a restricted NLP problem. Thus, this chapter discussed the concept of sentiment analysis in the field of NLP and explored that sentiment analysis is a restricted NLP problem due to the sophisticated nature of natural language.
Automatic Human Emotion Classification in Web Document Using Fuzzy Inference System (FIS)
Authors: Shakeel P Mohamed , Baskar S
Published: July 2022

Textual information mining deals with various information extraction methods that can be evolved from the rapid growth of textual information through human machine interface for analyzing emotions which are taken by a facial expression. The problem of emotions in text is concerned with the fast development of web 2.0 documents that are assigned by users with emotion labels, namely: sadness, surprise, happiness, empathy, anger, warmness, boredom, and amusement. Such emotions can give a new characteristic for document categorization. Textual information mining deals with various information extraction methods that can evolved from the rapid growth of textual information through a human machine interface for analyzing emotions, which are taken by a facial expression. The problem of emotions from text is concerned with the fast development of web 2.0 documents that are assigned by users with emotion labels. Such emotions can give a new characteristic for document categorization.
An Approach to Opinion Mining in Community Graph Using Graph Mining Techniques
Authors: Rao Bapuji
Published: July 2022

Opinions are the central theme to almost all human activities, as well the key influencers of our behaviours. Opinions related to sentiments, evaluations, attitudes, and emotions are the features of studying of opinion mining. It is important to study peoples of various communities sentiments about the schemes implemented by the government agencies as well as NGOs. The opinion mining is about the opinions of various communities of villages of a Panchayat about various social schemes implemented by the government of India. This article proposes an algorithm for opinion mining in a community graph for various social schemes run by the Panchayat using graph mining techniques. The algorithm has been implemented in C++ programming language.
Sentiment Analysis Using Cuckoo Search for Optimized Feature Selection on Kaggle Tweets
Authors: Kumar Akshi , Jaiswal Arunima , Garg Shikhar , Verma Shobhit , Kumar Siddhant
Published: July 2022

Selecting the optimal set of features to determine sentiment in online textual content is imperative for superior classification results. Optimal feature selection is computationally hard task and fosters the need for devising novel techniques to improve the classifier performance. In this work, the binary adaptation of cuckoo search (nature inspired, meta-heuristic algorithm) known as the Binary Cuckoo Search is proposed for the optimum feature selection for a sentiment analysis of textual online content. The baseline supervised learning techniques such as SVM, etc., have been firstly implemented with the traditional tf-idf model and then with the novel feature optimization model. Benchmark Kaggle dataset, which includes a collection of tweets is considered to report the results. The results are assessed on the basis of performance accuracy. Empirical analysis validates that the proposed implementation of a binary cuckoo search for feature selection optimization in a sentiment analysis task outperforms the elementary supervised algorithms based on the conventional tf-idf score.
Sentiment Analysis of Arabic Documents
Authors: Rahab Hichem , Djoudi Mahieddine , Zitouni Abdelhafid
Published: July 2022

Today, it is usual that a consumer seeks for others' feelings about their purchasing experience on the web before a simple decision of buying a product or a service. Sentiment analysis intends to help people in taking profit from the available opinionated texts on the web for their decision making, and business is one of its challenging areas. Considerable work of sentiment analysis has been achieved in English and other Indo-European languages. Despite the important number of Arabic speakers and internet users, studies in Arabic sentiment analysis are still insufficient. The current chapter vocation is to give the main challenges of Arabic sentiment together with their recent proposed solutions in the literature. The chapter flowchart is presented in a novel manner that obtains the main challenges from presented literature works. Then it gives the proposed solutions for each challenge. The chapter reaches the finding that the future tendency will be toward rule-based techniques and deep learning, allowing for more dealings with Arabic language inherent characteristics.
Opinion Mining for Instructor Evaluations at the Autonomous University of Ciudad Juarez
Authors: Jiménez Rafael , García Vicente , López Abraham , Mendoza Carreón Alejandra , Ponce Alan
Published: July 2022

The Autonomous University of Ciudad Juárez performs an instructor evaluation each semester to find strengths, weaknesses, and areas of opportunity during the teaching process. In this chapter, the authors show how opinion mining can be useful for labeling student comments as positives and negatives. For this purpose, a database was created using real opinions obtained from five professors of the UACJ over the last four years, covering a total of 20 subjects. Natural language processing techniques were used on the database to normalize its data. Experimental results using 1-NN and Bagging classifiers shows that it is possible to automatically label positive and negative comments with an accuracy of 80.13%.
A Survey on Aspect Extraction Approaches for Sentiment Analysis
Authors: Yadavilli Vrps Sastry , Seshadri Karthick
Published: July 2022

Aspect-level sentiment analysis gives a detailed view of user opinions expressed towards each feature of a product. Aspect extraction is a challenging task in aspect-level sentiment analysis. Hence, several researchers worked on the problem of aspect extraction during the past decade. The authors begin this chapter with a brief introduction to aspect-level sentimental analysis, which covers the definition of key terms used in this chapter, and the authors also illustrate various subtasks of aspect-level sentiment analysis. The introductory section is followed by an explanation of the various feature learning methods like supervised, unsupervised, semi-supervised, etc. with a discussion regarding their merits and demerits. The authors compare the aspect extraction methods performance with respect to metrics and a detailed discussion on the merits and demerits of the approaches. They conclude the chapter with pointers to the unexplored problems in aspect-level sentiment analysis that may be beneficial to the researchers who wish to pursue work in this challenging and mature domain.
Assessing Public Opinions of Products Through Sentiment Analysis
Authors: Ng C. Y. , Law Kris M. Y. , Ip Andrew W. H.
Published: July 2022

In the world of social networking, consumers tend to refer to expert comments or product reviews before making buying decisions. There is much useful information available on many social networking sites for consumers to make product comparisons. Sentiment analysis is considered appropriate for summarising the opinions. However, the sentences posted online are generally short, which sometimes contains both positive and negative word in the same post. Thus, it may not be sufficient to determine the sentiment polarity of a post by merely counting the number of sentiment words, summing up or averaging the associated scores of sentiment words. In this paper, an unsupervised learning technique, k-means, in conjunction with sentiment analysis, is proposed for assessing public opinions. The proposed approach offers the product designers a tool to promptly determine the critical design criteria for new product planning in the process of new product development by evaluating the user-generated content. The case implementation proves the applicability of the proposed approach.
Experimenting Language Identification for Sentiment Analysis of English Punjabi Code Mixed Social Media Text
Authors: Bansal Neetika , Goyal Vishal , Rani Simpel
Published: July 2022

People do not always use Unicode, rather, they mix multiple languages. The processing of codemixed data becomes challenging due to the linguistic complexities. The noisy text increases the complexities of language identification. The dataset used in this article contains Facebook and Twitter messages collected through Facebook graph API and twitter API. The annotated English Punjabi code mixed dataset has been trained using a pipeline Dictionary Vectorizer, N-gram approach with some features. Furthermore, classifiers used are Logistic Regression, Decision Tree Classifier and Gaussian Naïve Bayes are used to perform language identification at word level. The results show that Logistic Regression performs best with an accuracy of 86.63 with an F-1 measure of 0.88. The success of machine learning approaches depends on the quality of labeled corpora.
Towards Semantic Aspect-Based Sentiment Analysis for Arabic Reviews
Authors: Behdenna Salima , Barigou Fatiha , Belalem Ghalem
Published: July 2022

Sentiment analysis is a text mining discipline that aims to identify and extract subjective information. This growing field results in the emergence of three levels of granularity (document, sentence, and aspect). However, both the document and sentence levels do not find what exactly the opinion holder likes and dislikes. Furthermore, most research in this field deals with English texts, and very limited researches are undertaken on Arabic language. In this paper, the authors propose a semantic aspect-based sentiment analysis approach for Arabic reviews. This approach utilizes the semantic of description logics and linguistic rules in the identification of opinion targets and their polarity.
Airbnb or Hotel?
Authors: Li Zhiyong , Chen Honglin , Huang Xia
Published: July 2022

Advances in information technology have hugely influenced the tourism industry. Many tourists can generate and share their travel tips through social media, and people consult online reviews before making travel arrangements because they could access these sources of information easily. Either positive or negative reviews could increase consumer awareness of Airbnb. Using the approach of text mining and sentiment analysis, examining whether guests' emotions are positive or negative, this study investigates the attributes that influence Airbnb consumers' experiences compared with their previous hotel experiences by analysing big data of guests' online reviews. Findings reveal that the factors of guests' positive sentiment are the atmosphere, flexibility, special amenities, and humanized service; the factors of guests' negative sentiment are not value for money, have to clean the room before leaving, sharing amenities and space with strangers, disturbed by hosts' noisy recreational activities, and troubled by hosts' requesting good reviews.
Communicating Natural Calamity
Authors: Capolupo Nicola , Piscopo Gabriella
Published: July 2022

This chapter aims at understanding the dynamics that led to the exchange and value co-creation/co-production in the interaction between P.A. and citizens during natural calamities. In addition, it proposes a horizontal communication model in which both actors cooperate to respond to crisis, a semantic and semiotic space on the net able to satisfy their information needs. When natural disasters occur, citizens' primary need is to reach as much information as possible about the status of loved ones possibly involved in the accident, road traffic, how to give an effective contribution to the cause without hindering, etc. On the other hand, P.A. and rescuers need to know as much information as possible about the reports, on the site of the disaster so as to intervene promptly to help the population in danger. Therefore, P.A. and citizens are called upon to cooperate to guarantee crisis containment, crisis management, and also future crisis prevention.
Sentiment Analysis and Sarcasm Detection (Using Emoticons)
Authors: Dagar Vibhu , Verma Amber , K. Govardhan
Published: July 2022

Sentiment analysis is contextual mining of text which identifies and extracts subjective information in source material and helps a business to understand the social sentiment of their brand, product, or service while monitoring online conversations. However, analysis of social media streams is usually restricted to just basic sentiment analysis and count-based metrics. This is akin to just scratching the surface and missing out on those high value insights that are waiting to be discovered. Twitter is an online person-to-person communication administration where overall clients distribute their suppositions on an assortment of themes, talk about current issues, grumble, and express positive or on the other hand negative notions for items they use in life. Hence, Twitter is a rich source of information for supposition mining and estimation investigation.
Behaviour and Emotions of Working Professionals Towards Online Learning Systems
Authors: Attili Venkata Ramana , Annaluri Sreenivasa Rao , Gali Suresh Reddy , Somula Ramasubbareddy
Published: July 2022

Student behaviour in the classroom depends on various influential factors (such as family, friends, locality, habits, etc.). Once a student enters into professional life after completing the graduation, it finds it difficult to get back to the learning process due to a variety of issues. In such situations, most of the students go for online courses to improve their skills or to get a promotion at work by upgrading their academic degrees. The tendency of working professionals attending online classes is increasing rapidly due to the vast development in technology in recent times and due to the demand for innovative Secunderabad, e technologies. In this paper, a detailed study on a variety of participants from different work domains was carried out to study the sentiments of working professionals by analysing their behaviour and emotions using Hadoop, big data, and R-Language. Using the RFacebook API, the functioning of the students was analysed in this work by using R programming. Results have shown that the behaviour of 89% working professionals is positive, and emotionally, 75% were satisfied with online courses. However, the tendency of being lazy was also expressed by many for online courses.
When Emotions Rule Knowledge
Authors: Fteimi Nora , Hornung Olivia , Smolnik Stefan
Published: July 2022

Although emotions play an important role in human behavior and knowledge studies, knowledge management (KM) research considers them from specific angles and, to date, has lacked a comprehensive understanding of the emotions dominating KM. To offer a holistic view, this study investigates the presence of emotions in KM publications by applying a sentiment analysis. The authors present a sentiment dictionary tailored to KM, apply it to KM publications to determine where and how emotions occur, and categorize them on an emotion scale. The considerable amount of positive and negative emotions expressed in KM studies prove their relevance to and dominance in KM. There is high term diversity but also a need to consolidate terms and emotion categories in KM. This study's results provide new insights into the relevance of emotions in KM research, while practitioners can use this method to detect emotion-laden language and successfully implement KM initiatives.
Tracking How a Change in a Telecom Service Affects Its Customers Using Sentiment Analysis and Personality Insight
Authors: Adl Ammar , Elfergany Abdelsadeq Khamis
Published: July 2022

Tracking the effect of change a telecom service on customer feeling is an important process for telecom companies. As a result of tangible growth and large competition among telecom companies, customer retention and satisfaction are the most important challenges faced by telecom companies nowadays. Customer retention can be achieved by identifying the feeling of the telecom customers after changing service and take care of the customers by modifying the services that aren't accepted by its customers. Hence, this article was done by using a combination of four stages of: text pre-processing, personality analysis, sentiment analysis, and a chatbot system. This article shows the effect of using the personality traits, agreeableness and emotional range, with sentiment analysis to help reaching a full description of customer feel. Combining the sentiment analysis Naïve Bayes technique in the natural language processing and personality insights pre-learning stage and adding feedback using the obtained results achieves higher accuracy than using the traditional sentiment analysis techniques.
Analyzing the Impact of e-WOM Text on Overall Hotel Performances
Authors: Aakash Aakash , Aggarwal Anu G. , Aggarwal Sanchita
Published: July 2022

A flourishing of the importance of customer reviews has been observed in this digital era. This is especially true in hotel sector, which allows guests to express their satisfaction towards the service in the form of open-structured online reviews and overall ratings over travel agency websites. Using reviews data of 2001 hotels from Tripadvisor.com, the chapter analyzes the overall hotel performances through linguistic features of e-WOM such as its length, readability, sentiment, and volume. The chapter develops a regression model for evaluating guest satisfaction by using overall ratings as its measure, validated through hotel review data. Data analysis result shows that review volume, sentiment index, and readability have significant positive affect over guest satisfaction whereas length shows the negative influence. This chapter discusses beneficial implications for researchers and practitioners working in this field.
Facebook and Google as Regrettable Necessities
Authors: Frigato Pietro , Santos-Arteaga Francisco J.
Published: July 2022

The following article considers the results from two different studies, a European one involving over 20,000 respondents and an American one closing on 1,000, to illustrate how online platforms such as Facebook and Google can be defined as regrettable necessities. We define regrettable necessities as those whose consumption provides a direct disutility to consumers. That is, other than the standard utility derived from the access to a given service, a direct disutility in terms of privacy losses and preference manipulation results from their use. In addition, users acknowledge this fact and are aware of the disutility suffered, though not necessarily of its intensity, highlighting the fundamental strategic role played by these platforms in current voting environments.
A Systematic Review on Self-Construal and Social Network Sites
Authors: Lee Soon Li , Tam Cai Lian
Published: July 2022

The present research was conducted to systematically review existing research that examined the relationships of the aspects of self-construal and social network sites (SNS) usages. A total of 12 research articles met the inclusion criteria for the present review. The reviewed research articles mainly supported the significant relationship of the interdependent self-construal and SNS-related outcomes. The present review highlighted that the reviewed relationships differed. Some findings supported the direct effect of self-construal on SNS-related outcomes, whereas some supported the indirect effects of intervening variables on these relationships. The reviewed findings supported the influence of self-construal on cognition, emotion, and motivation. Implications of the present systematic review were discussed in the manuscript.
Trial by Social Media
Authors: Taylor Jacqui , Tarrant Gemma
Published: July 2022

Social media makes it easier than ever to access information and opinions associated with criminal proceedings and viewing or discussing these pre-trial could reduce juror impartiality. This study explored whether viewing social media comments influenced mock juror verdicts. Seventy-two participants formed 12 six-person ‘mock juries'. All participants received information regarding a murder trial. Nine groups were exposed to social media comments, manipulated to be negative, positive or neutral towards the defendant. The remaining three groups only received trial information (control condition). Results showed that prior to group discussion, exposure to negatively-biased comments significantly increased the number of guilty verdicts, however these effects disappeared after group discussion. Therefore, although jurors may be unable to remain impartial before a trial, jury discussion can remove these prejudices, supporting previous group research. Further research is suggested where participants interact actively with social media, rather than passively viewing comments.
Unemployment, Personality Traits, and the Use of Facebook
Authors: Ukpabi Dandison C. , Olawumi Olayemi , Balogun Oluwafemi Samson , Nwachukwu Chijioke E. , Sunday Adewale Olaleye  , Kolog Emmanuel Awuni , Agjei Richard O. , Adusei-Mensah Frank , Awoniyi Luqman , Atsa'am Donald Douglas , Adeyemi Oluwafikayo
Published: July 2022

Different personality traits respond differently to unfavourable life situations. Unemployment can have several negative social, economic, and domestic consequences. Many people use social media for a variety of reasons. The aim of this study is to examine the way different personality traits respond to Facebook in the period of unemployment. Data was obtained from 3,002 unemployed respondents in Nigeria. The study used regression model to analyse the data. Among the five personality traits, results indicated that the relationship between neuroticism and online social support was negative. However, the relationship between online social support and satisfaction was positive. The study highlights several theoretical and practical implications.
Support for Cyberbullying Victims and Actors
Authors: Alim Sophia , Khalid Shehla
Published: July 2022

This study analyses the post content and the emotions reflected in 10 open Facebook groups associated with cyberbullying, with the highest number of group members. Automated extraction via Facebook API was used to gather the data. Altogether, 313 Facebook posts were extracted and coded for content analysis. Sentiment analysis and parts of speech (POS) tagging was used to explore the emotions reflected in the content. The study findings revealed that (1) the content of the posts was mainly opinion-based in comparison to expressing personal experiences of cyberbullying. This indicated Facebook groups require stronger moderation due to digression of topics discussed. (2) Only 3% of posts in this study contained advice about cyberbullying. (3) Sentiment analysis of the posts showed that the Facebook groups focused on cyberbullying, reflected more positive sentiments in their posts. This is encouraging to cyberbullying victims to share information on cyberbullying. The findings in this study lay the foundations for more research into support for cyberbullying victims.
What Are Basketball Fans Saying on Twitter?
Authors: Güçlü Burçin , Garza Marcela , Kennett Christopher
Published: July 2022

Social media receives growing interest from sports executives. Yet, very little is known about how to make use of such user-generated, unstructured data. By exploring tweets generated during Turkish Airlines Euroleague's Final Four event, which broadcasted the four tournaments of championship among four finalist teams, the authors studied how fans respond to gains and losses and how engaged they were during games through the course of the event. The authors found that favorable reactions were received when teams won, but the magnitude of unfavorable reaction was larger when teams lost. When it came to the organizer rather than the teams, the organizer of the event received most of the positive feedback. The authors also found that main source of tweets was smartphones while tablets were not among real-time feedback devices.
An Examination of Motivation and Media Type
Authors: Marjerison Rob Kim , Lin Yinan , Kennedyd Sarmann I.
Published: July 2022

This paper explores the motivations and priorities of Chinese Millennials' use of social media with regard to the sharing of content. A commercially important demographic, this group are highly active on social media. The amount of content that is shared online is immense. Some shared content “goes viral” and can be seen by vast numbers of users. The findings of this study are based on the results of over 650 online surveys and include both theoretical and practical contributions to the body of knowledge regarding the nature of viral propagation of content in Chinese social media. This contribution to the understanding and insight social media activities of this significant and commercially consumer demographic may be of value to online promoters and marketers as well those interested in the use of social media for commercial purposes in the design and management of their online and social media presence, marketing, and advertising strategies.
Understanding Social Media Addiction Through Personal, Social, and Situational Factors
Authors: Kirezli Ozge , Aydin Asli Elif
Published: July 2022

The main objective of this chapter is to gain an in-depth understanding of the social media addiction construct. For this purpose, prior studies on social media addiction are reviewed. Based on this review the influence of several personal, social, and situational factors on social media addiction are examined. Firstly, personal factors such as demographic characteristics, personality traits, self-esteem, well-being, loneliness, anxiety, and depression are studied for their impact on social media addiction. Next, the social correlates and consequents of social media addiction are identified, namely need for affiliation, subjective norms, personal, professional, and academic life. Lastly, situational factors like amount of social media use and motives of use are inspected. Following the review of literature an empirical study is made to analyze factors that discriminate addicted social media users from non-addicted social media users on the basis of these different factors.
Examining the Psychosocial Dimensions of Young People's Emergent Social Media Behavior
Authors: Andoh-Quainoo Lydia
Published: July 2022

Social media usage among young people has grown astronomically, generating interest among a number of interest groups. This chapter fills a gap on social media psychosocial antecedents propelling high-usage behavior and the subsequent psychosocial outcomes showing in attachment to the social media. The chapter explored the emergent psychosocial needs driving young people's level of usage in social media and the consequences, among a population in Ghana. The findings revealed that young consumers' social media behavior could be greatly influenced by their social psychological needs, but individual psychological variables did not significantly predict usage behavior in social media. The findings also suggest that young people are more emotionally attached to social media, slightly attached cognitively and not attached behaviorally. This implies young people have developed some level of emotional involvement for the use of social media which could affect their well-being positively or negatively.
The Impact of Similarity and Self-Esteem on Facebook Behaviors, Perceptions, and Attitudes
Authors: Balint Bryon
Published: July 2022

From their inception, electronic social networks (ESNs) have held the potential to either (1) expose individuals to a greater diversity of beliefs and interests by removing geographical barriers to communication; or (2) act as “feedback loops” by facilitating relationships and communication among like-minded individuals. In this survey study, the author will examine changes in communication behaviors and perceptions on Facebook from 2013 to 2017. The findings conclude that individuals with lower self-esteem have become less willing to share their views on Facebook, perceive a higher number of negative experiences, and spend less time communicating and more time passively consuming content. The same behavioral changes are found when individuals believe that fewer of their online “friends” have similar beliefs, and when individuals are more prone to “unfriending” others. General comfort in sharing views online is associated with a higher willingness to share views and communicate on Facebook, but also more negative experiences.
Remaining Facebook versus Face-to-Face Friends after a Romantic Breakup
Authors: Medeiros Dylan T , Webb Lynne M
Published: July 2022

The study queried whether the relational characteristics that influence individuals to remain face-to-face friends with former romantic partners following a break up also impact the decisions to remain Facebook “friends” with former romantic partners. The sample included over 300 young adults who met two criteria: They maintained an active Facebook account and reported a pre-marital, romantic break-up. The results revealed that the variables that impact post-dissolution friendship decisions of former romantic partners in the face-to-face context (quantity of relational investments, relational satisfaction, and relational disengagement strategies) do not impact former romantic partners' decisions to maintain or dissolve Facebook friendships. These results provide evidence that romantic partners may experience different relational motivations and dynamics in online versus off-line venues.
Today Is Your Birthday!
Authors: Morah Doris Ngozi , Uzochukwu Chinwe Elizabeth
Published: July 2022

Facebook birthdays have become increasingly trendy among young people globally and in Africa since the arrival of smart technologies. The study investigates the idea of celebrating birthdays on Facebook among selected Nigerian university students and reactions to this new trend. Using the diffusion of innovations theory and mixed research methods, in-depth interviews were conducted on six respondents, and profile activities of 300 Facebook users' content was analysed to investigate the research objectives. Findings demonstrate that most users found it convenient and economical to celebrate birthdays on Facebook. Online celebrations offer more global online visibility than offline parties leading to the formation of digital cultures and connections. The result led to a proposal for the bicultural convergence model that explains user experiences to social media effect. Poor network and high-cost data tariff, among others, posed challenges. The study recommends the provision of cheap or free internet access in Nigeria and Africa to enable increased growth of the digital social culture.
#Childathlete
Authors: Mitchell Fallon R. , Woodruff Sarah J. , van Wyk Paula M. , Santarossa Sara
Published: July 2022

The present study aimed to examine the tone and focus of the conversation associated with #childathlete on Instagram. Additionally, the visual content of five child athlete Instagram accounts were analyzed to determine if fitspiration (e.g., exercise, healthy eating, inspiration, showcase strength, and empowerment) or objectification (e.g., emphasis of specific body parts, suggestive posing, or emphasis on appearance) were promoted. Using Netlytic, a text analysis was conducted to analyze the conversation surrounding #childathlete and the top five child athlete accounts (based on likes) that were managed by parents were selected for visual content analysis. The text analysis revealed that the conversation was positive in tone and focused on sport/exercise. Analysis of the visual content indicated that the child athlete accounts focused athleticism, activity, and fitness, with little presence of objectification. Future research should further explore social media as a strategy for promoting and improving physical activity among users.
Communication on Social Network Sites
Authors: Otieno Denish Ouko , Kirigha Faith Halima , Akwala Alfred Okoth
Published: July 2022

Facebook is regarded as a popular social network in Kenya more so among the youth besides other sites like Instagram and Twitter. A study conducted by World Wide Web Foundation revealed that more than one in every five women in Kenya have experienced cyberbulling. Such study results justifies that the internet is rife with harassment with more women bearing the brunt as compared to men. The main objective of the study was to establish forms of cyber-bullying among young women between 25 to 35 years old on Facebook platform. It was established that unwanted trolls, doxing, and sexual harassment were the dominant cyberbullying trends on Facebook among young women in Kenya with men being the highest perpetrators of cyberbullying than women. It was also established that there is lack of awareness among young women on Facebook Safety, which is a resource developed by Facebook for reporting inappropriate content and help protect people who may be feeling self-injury due to cyberbullying on Facebook.
Rise of Facebook in the USA and WeChat in China
Authors: Choudhury Naziat
Published: July 2022

The owners of Facebook and WeChat repeatedly promote their media as the preferred platform for people to connect. Improving social relationships was marketed as the reason for their innovation. But users' urge to unite on these OSN services alone cannot explain the success of these media in the US and China. There is a different or rather new business approach underpinning these OSN services that contribute to their success. The author argues that there is an implication of owners' profit-based interest in ensuring the popularity of their online platforms. Audience commodity analysis as discussed by Dallas W. Smythe and Christian Fuchs is employed in the contexts of the US and China to comprehend the complex factors related to online social media owners' interest and their negotiation with the government in online media's prosperity. Through archival research including examination of newspapers, policy documents from OSN-based companies, and survey results from 2015 to mid-2018, this chapter demonstrates the political economy of Facebook and WeChat.
Positive vs. Negative Emotions and Network Size
Authors: Al-Saggaf Yeslam
Published: July 2022

This chapter looks at the relationship between the expression of positive and negative emotions in Twitter and users' network size. The questions that guided this study are: Do users who tweet twice or more “I am bored,” “I am excited,” “I feel lonely,” “I feel loved,” “I feel sad,” and “I feel happy” gain more followers and friends or lose them? Do users who express positive emotions twice or more have more followers and friends compared to users who express negative emotions or less? Do users who express boredom, excitement, loneliness, feeling loved, sadness, and happiness twice or more interact more with their networks or less? To address these questions, the study collected 35,096 English tweets in 2016. The findings indicate that users who tweeted these emotions, their number of followers and number of friends have increased, not decreased and that only users who expressed excitement had more followers and friends than users who expressed boredom. The study contributes to the literature on the benefits that lonely, sad, and bored users can reap from expressing emotions in Twitter.
The Tipping Point
Authors: Oh Soo Kwang , Hong Seoyeon , Park Hee Sun
Published: July 2022

While previous researchers have addressed motivations to join and continue using social media, this paper focuses on why users quit certain social media and change their favorite platforms, such as the current shift from Facebook to Twitter to Instagram and Snapchat. Furthermore, this exploratory study seeks to build an understanding of social media usage and motivations for switching from a cross-cultural perspective by comparing findings from Korean and U.S. users. Findings from 19 focus group sessions (n = 118) highlight influences regarding modes of usage, user control, commitment, addiction, privacy, perceived relationships, self-construals, and social/cultural trends. Findings are further analyzed and compared in light of relevant theoretical frameworks and cultural differences.