A Systematic Literature Review on Cyberbullying in Social Media: Taxonomy, Detection Approaches, Datasets, And Future Research Directions

In the area of Natural Language Processing, sentiment analysis, also called opinion mining, aims to extract human thoughts, beliefs, and perceptions from unstructured texts. In the light of social media's rapid growth and the influx of individual comments, reviews and feedback, it has evolved as an attractive, challenging research area. It is one of the most common problems in social media to find toxic textual content.  Anonymity and concealment of identity are common on the Internet for people coming from a wide range of diversity of cultures and beliefs. Having freedom of speech, anonymity, and inadequate social media regulations make cyber toxic environment and cyberbullying significant issues, which require a system of automatic detection and prevention. As far as this is concerned, diverse research is taking place based on different approaches and languages, but a comprehensive analysis to examine them from all angles is lacking. This systematic literature review is therefore conducted with the aim of surveying the research and studies done to date on classification of  cyberbullying based in textual modality by the research community. It states the definition, , taxonomy, properties, outcome of cyberbullying, roles in cyberbullying  along with other forms of bullying and different offensive behavior in social media. This article also shows the latest popular benchmark datasets on cyberbullying, along with their number of classes (Binary/Multiple), reviewing the state-of-the-art methods to detect cyberbullying and abusive content on social media and discuss the factors that drive offenders to indulge in offensive activity, preventive actions to avoid online toxicity, and various cyber laws in different countries. Finally, we identify and discuss the challenges, solutions, additionally future research directions that serve as a reference to overcome cyberbullying in social media

Sharing feelings online: Studying emotional well-being via automated text analysis of Facebook posts

Digital traces of activity on social network sites represent a vast source of ecological data with potential connections with individual behavioral and psychological characteristics. The present study investigates the relationship between user-generated textual content shared on Facebook and emotional well-being. Self-report measures of depression, anxiety and stress were collected from 201 adult Facebook users from North Italy. Emotion-related textual indicators, including emoticon use, were extracted form users’ Facebook posts via automated text analysis. Correlation analyses revealed that individuals with higher levels of depression, anxiety expressed negative emotions on Facebook more frequently. In addition, use of emoticons expressing positive emotions correlated negatively with stress level. When comparing age groups, younger users reported higher frequency of both emotion-related words and emoticon use in their posts. Also, the relationship between online emotional expression and self-report emotional well-being was generally stronger in the younger group. Overall, findings support the feasibility and validity of studying individual emotional well-being by means of examination of Facebook profiles. Implications for online screening purposes and future research directions are discussed

Unveiling the Emotional and Psychological States of Instagram Users: A Deep Learning Approach to Mental Health Analysis

People can now communicate with others who have common tastes to them and engage in conversation together while furthermore exchanging ideas, photos, and clips that convey their emotional states due to social media’s technology. As a consequence, there is an opportunity to investigate person sentiments and thoughts in social networking sites data in order to understand their viewpoints and sentiments when utilizing these digital platforms for interaction. Utilizing social network data to diagnose depression has gained extensive acceptance, there is still a number of unidentified characteristics. Due to its potential to shed light on the forecasting model, model complexity is crucial for facilitating communication. For example, the majority of algorithms for machine learning produce results in the automatic depression forecasting test that are challenging for people to understand. In this research the mental health condition is analyzed using deep learning approach by considering the data from Instagram data. In this investigation, researchers created the Hybrid deep learning approach, which divided the sentiment ratings into different categories: Neutral, Positive, Negative. Researchers also contrasted the performance of the recommended approach with other machine learning algorithm on a number of criteria, including accuracy, sensitivity, F1 score, and precision

Themes and Participants’ Role in Online Health Discussion: Evidence From Reddit

Health-related topics are discussed widely on different social networking sites. These discussions and their related aspects can reveal significant insights and patterns that are worth studying and understanding. In this dissertation, we explore the patterns of mandatory and voluntary vaccine online discussions including the topics discussed, the words correlated with each of them, and the sentiment expressed. Moreover, we explore the role opinion leaders play in the health discussion and their impact on participation in a particular discussion. Opinion leaders are determined, and their impact on discussion participation is differentiated based on their different characteristics such as their connections and locations in the social network, their content, and their sentiment. We apply social network analysis, topic modeling, sentiment analysis, machine learning, econometric analysis, and other techniques to analyze the collected data from Reddit. The results of our analyses show that sentiment is an important factor in health discussion, and it varies between different types of discussions. In addition, we identified the main topics discussed for each vaccine. Furthermore, the results of our study found that global opinion leaders have more influence compared to local opinion leaders in elevating the health discussion. Our study has important theoretical and practical implications

Adam Deep Learning with SOM for Human Sentiment Classification

Nowadays, with the improvement in communication through social network services, a massive amount of data is being generated from user's perceptions, emotions, posts, comments, reactions, etc., and extracting significant information from those massive data, like sentiment, has become one of the complex and convoluted tasks. On other hand, traditional Natural Language Processing (NLP) approaches are less feasible to be applied and therefore, this research work proposes an approach by integrating unsupervised machine learning (Self-Organizing Map), dimensionality reduction (Principal Component Analysis) and computational classification (Adam Deep Learning) to overcome the problem. Moreover, for further clarification, a comparative study between various well known approaches and the proposed approach was conducted. The proposed approach was also used in different sizes of social network data sets to verify its superior efficient and feasibility, mainly in the case of Big Data. Overall, the experiments and their analysis suggest that the proposed approach is very promissing

Improving Health and Efficiency With Strategic Social Media Use in Health Organizations: A Critical Review of the Status Quo

The emergence of social networking systems as mainstream applications and an inherent element of daily life is a phenomenon observed throughout the world as the worldwide social media users exceeds 2.7 billion. Similar to other sectors, healthcare organizations have also started benefiting from social media in distinct ways such as collecting feedback, educating, communicating and supporting patients and citizens. Social networks can act as remarkable channels for healthcare providers, governmental institutions, pharmaceutical companies, hospitals and others to educate, communicate to, listen, connect to and engage existing and potential customers, patients, physicians and healthcare professionals. Despite the various benefits offered, health institutions, health professionals and stakeholders are reluctant to utilize social media due to several barriers and lack of expertise. This chapter aims to provide a better understanding on the ways healthcare companies can utilize social networks in detail to overcome use barriers and obtain related benefits

Comparación de técnicas de clasificación de aprendizaje de máquina en el diagnóstico del trastorno depresivo leve

A nivel mundial la depresión lo padece unos 350 millones de seres humamos y el 5% es a nivel de Latinoamérica, es así que, cada veintidós minutos un ser humano intenta hacerse daño, las edades con mayores problemas depresivos son los adolescentes el cual representa el 10%, el 6% adultos mayores de 18 años y 3.5% en niños de 6 a 10 años, en el Perú, el 80% de suicidios es a causa de la depresión, hay un millón setecientos mil personas que presentan cuadro depresivo, pero solo es atendido un 25% con atención especializada y el 65% simplemente no busca ayuda, estudios han demostrado que a nivel del ministerio de salud, el documento técnico llamado “auto escala de Zung”, es el más adecuado para la identificación de este problema analizando la medición de la depresión a través de información de aspectos cognitivos, afectivos y somáticos del paciente, dicho documento tiene una especificidad del 63% y sensibilidad del 97%, aprobando un acierto del 82% para discriminar la depresión. En esta investigación se construyó un método que inicia con la elaboración de un dataset de acuerdo a las variables de ingreso y salida así como el nivel de prioridad basados en el cuestionario de Zung, después se realizó la elección de las técnicas de aprendizaje de máquina, utilizadas para tratar casos de diagnóstico de depresión con mayor precisión, entre ellas lograron destacar, naive bayes, árbol de decisión, redes neuronales y maquinas vectores de soporte, acto seguido se implementó las técnicas mencionadas para ser comparadas y evaluadas según su desempeño, para el desarrollo de las mismas se utilizó la plataforma de google colaboratory con el lenguaje de programación python, según el método propuesto, desarrollado y evaluado se concluye que las redes neuronales tienen una precisión del 100% para el diagnóstico de depresión.TesisInfraestructura, Tecnología y Medio Ambient

Three Research Essays on Human Behaviors in Social Media

Social Media (SM) has grown to be one of the most popular Internet technologies for individual users and has fostered a global community. For instance, recent statistics reveal that monthly active users of Facebook are almost 1.5 billion by Mar 2015. At the same time, 20% of internet users in the US are expected to have Twitter accounts. This figure has grown from 15.2% in 2012, and is expected to rise to 24.2% by 2018 (Twitter 2015). People like spending their time on SM to track the latest news, seek knowledge, update personal status, and connect with friends. It is possible that being exposed to others’ positive information on SM could generate darker emotions, such as envy. Extant literature suggests that envy significantly influences human behaviors and life satisfaction (Krasnova et al. 2013). This dissertation, consisting of three essays, studies the effects of SM on human behaviors. Chapter 2 investigates how others’ positive information arouses envy and influences user behaviors from different angles. Chapter 3 focuses on how espoused national cultures reshape online benign envy and impact SM usage. Chapter 4 discusses the relationship between social media and envy with textual analysis techniques. Chapter 5 provides a summary and overall conclusion to this work. Chapter 2- Envy and How it can Influence SM Use Users tend to disclose the positive side of their lives on SM. Such information can be perceived in an extremely positive light in the eyes of their connections, which could leads to envy. In the current study, we develop a theoretical framework that elaborates the mechanism through which online envy is generated and consequently influences SM usage. We specify that online users experience two types of envy: malicious and benign envy, which have distinct impacts on IS use. Specifically, malicious envy plays a mitigating role and benign envy serves as an enhancer of SM use. Our findings provide valuable implications for both academic researchers and IS practitioners. Chapter 3 – Benign Envy, Social Media, and Culture Although envy universally exits in human society, its influence on human behaviors varies by cultural contexts. As shown in chapter 2, benign envy is a more salient factor in the social media context. In the current essay, we focus on investigating how different espoused national cultural values affect this relationship between online benign envy and consequent behaviors. We also developed a benign envy and IT usage model, which integrates four espoused national cultural values. We conceptualized several main constructs and then theoretically justified the relationships between them. As expected, if people experience benign envy when using SM, they are more likely to continue their use. Moreover, different espoused national culture values work as independent and moderating variables along with the envy procedures. People who hold different levels of culture behave distinctly. The study found that people who espouse a greater level of collectivism were be more likely to compare with other peers in order to evaluate their self-social status; people who espouse higher levels of uncertainty avoidance were more likely to experience benign envy; and the relationship between perceived enhancement and use intention was stronger for individuals with higher levels of espoused masculinity. However, espoused power distance values were not significantly moderating the relationship between perceived enjoyment and intended behavior in the current context (general SM). This study provided some theoretical and practical implications. Chapter 4 – Tweet, Favorite, Status, and Envy Many social media studies have demonstrated that aggregating social information could provide valuable insight into sociological, economical, healthcare, and other critical fields. Among these studies, Twitter has been one of the most popular social platforms that researchers value. It has a greater potential for academics to observe and explore critical social behaviors, such as envy, which could lead to avoidance of using certain IT platforms, emotional depression, and even worse, suicide. With text mining techniques, massive numbers of tweets can be collected, classified, and analyzed. The envy literature has largely theorized on the motivations of envy. However, in the IS context, envy related research is very limited, and the empirical tests are confounded by limited data. In order to address these gaps, we collected envy related tweets from Twitter and classified them into the two types (benign and malicious) of envy relying on text mining techniques with sentiment analysis (positive to negative). Based on the data set, we further analyzed the patterns of online envy. Additionally, by using logistic regression, the impacts of certain social media usage behaviors were tested on differentiating online envy. Our work included both qualitative observation and quantitative analysis, along with the evaluation of regression output

Cyber Security Concerns in Social Networking Service

Today’s world is unimaginable without online social networks. Nowadays, millions of people connect with their friends and families by sharing their personal information with the help of different forms of social media. Sometimes, individuals face different types of issues while maintaining the multimedia contents like, audios, videos, photos because it is difficult to maintain the security and privacy of these multimedia contents uploaded on a daily basis. In fact, sometimes personal or sensitive information could get viral if that leaks out even unintentionally. Any leaked out content can be shared and made a topic of popular talk all over the world within few seconds with the help of the social networking sites. In the setting of Internet of Things (IoT) that would connect millions of devices, such contents could be shared from anywhere anytime. Considering such a setting, in this work, we investigate the key security and privacy concerns faced by individuals who use different social networking sites differently for different reasons. We also discuss the current state-of-the-art defense mechanisms that can bring somewhat long-term solutions to tackling these threats