U.S. Religious Landscape on Twitter

Religiosity is a powerful force shaping human societies, affecting domains as diverse as economic growth or the ability to cope with illness. As more religious leaders and organizations as well as believers start using social networking sites (e.g., Twitter, Facebook), online activities become important extensions to traditional religious rituals and practices. However, there has been lack of research on religiosity in online social networks. This paper takes a step toward the understanding of several important aspects of religiosity on Twitter, based on the analysis of more than 250k U.S. users who self-declared their religions/belief, including Atheism, Buddhism, Christianity, Hinduism, Islam, and Judaism. Specifically, (i) we examine the correlation of geographic distribution of religious people between Twitter and offline surveys. (ii) We analyze users' tweets and networks to identify discriminative features of each religious group, and explore supervised methods to identify believers of different religions. (iii) We study the linkage preference of different religious groups, and observe a strong preference of Twitter users connecting to others sharing the same religion.Comment: 10 page

The Impact of Mindfulness on Non-Malicious Spillage within Images on Social Networking Sites

Insider threat by employees in organizations is a problematic issue in today’s fast-paced, internet-driven society. Gone are the days when securing the perimeter of one’s network protected their business. Security threats are now mobile, and employees have the ability to share sensitive business data with hundreds of people instantaneously from mobile devices. While prior research has addressed social networking topics such as trust in relation to information systems, the use of social networking sites, social networking security, and social networking sharing, there is a lack of research in the mindfulness of users who spill sensitive data contained within images posted on social networking sites (SNS). The author seeks to provide an understanding of how non-malicious spillage through images relates to the mindfulness of employees, who are also deemed insiders. Specifically, it explores the relationships between the following variables: mindfulness, proprietary information spillage, and spillage of personally identifiable information (PII). A quasi-experimental study was designed, which was correlational in nature. Individuals were the unit of analysis. A sample population of business managers with SNS accounts were studied. A series of video vignettes were used to measure mindfulness. Surveys were used as a tool to collect and analyze data. There was a positive correlation between non-malicious spillage of sensitive business, both personally identifiable information and proprietary data, and a lack of mindfulness

Dual Drivers of Facebook Usage and Regret Experience in Networking versus Brand page Usage

In this article, we draw on Uses and Gratifications Theory (UGT) to identify the dual drivers (positive and negative) of two Facebook usage types: online networking versus brand page usage, and their potential respective effects on regret experience and on Facebook continuous intention. We also investigate the role played by perceived privacy concerns in these two mechanisms. Our findings indicate that exhibitionism, entertainment value and specific functional gratifications; i.e. interpersonal connectivity for social networking and information value for brand page usage; are significant drivers for both usage types. Whereas, regret experienced by users in these two contexts seem to follow divergent paths and affect differently Facebook continuance intention

Control vs Content: A Systematic Review of the Social Media Research Literature

The rapid advancement of web 2.0 applications paved the way for the development of social media applications and the many features of these applications enable them to attract millions of users. Social media platforms have altered how people interact with the world and one another. Researchers in the field of Information Systems have investigated social media platforms and technologies extensively. Despite the growing number of studies on social media, however, the area remains under examined. Given the pace and consistency of innovation in this field, identifying directions for future studies of social media-related phenomena requires a careful review of the related research completed to date

Twitter Sentiment Analysis: An Examination of Cybersecurity Attitudes and Behavior

This exploratory study examines the cybersecurity attitudes and actual behavior over time using the data collected on the social media microblogging platform, Twitter. We plan to use the sentiment analysis and text mining techniques on original tweets related to cybersecurity collected at two different time periods. Upon completion of this research, we would present the analysis of the relationship between the cybersecurity attitudes and behavior and how behaviors may be shaped by the attitudes. This research work aims to contribute to the extant literature in cybersecurity and endeavors to enhance our understanding of cybersecurity attitude and behavior by validating the proposed research model and hypotheses by using real-time, user-generated, social media data

Factors Affecting UAE Consumers’ Attitudes Towards Using Social Networking Sites in Hotel Selection

This study focuses on the influence of social networking sites on travelers' attitudes towards hotel selection in the UAE. A social networking site (SNS) is a form of social media that provides a platform for people to connect with each other. It is very important to examine how individual travelers are utilizing SNS when they select a hotel. Therefore, the objective of this study is to investigate if Perceived Ease of Use (PEU), Perceived Usefulness (PU), and Perceived Risk (PR) have any influence on attitude toward using social media (ATUSM). The study will use an exploratory research design which employs quantitative data. Convenience sampling technique was used to distribute the self-administered survey questionnaires on different hotels in the UAE. As hypothesized, the results show the positive effect of Perceived Ease of Use (PEU), Perceived Usefulness (PU), and Perceived Risk (PR) on consumers' attitude toward using social media (ATUSM) in hotel selection. Keywords: social networking sites, attitude, perceived ease of use, perceived usefulness, perceived risk, hotel selection DOI: 10.7176/JMCR/66-03 Publication date:March 31st 202

Catching the Video Virus

In the process of computer-mediated exchange, some online videos travel from one person to another resulting in the process of diffusion of the video. However, there are very few empirical investigations of the audience involved in the process. This exploratory research employs Rogers\u27 diffusion of innovations as a theoretical framework to study online video users. Theories from social networks on tie strength and homophily are applied to create an integrated diffusion model. Based on survey data from college students, online video audience was profiled in two ways: one based on individual characteristics and another on activities with video content. Participants in the viral transmission process were found to be novelty-seekers, highly connected to others and appreciative of entertaining videos. An integrated model exploring the antecedents of viral transmission of online videos identified age, sex, Internet usage, and network connectedness as significant predictors. Contrary to previous findings, strong and homophilous ties were found to significantly contribute toward the viral spread. The findings of this study will add to the body of knowledge on diffusion research by enhancing understanding of individuals involved in an evolving medium. A profile of online video users will help marketers identify and reach the right audienc

Self-disclosure model for classifying & predicting text-based online disclosure

Les médias sociaux et les sites de réseaux sociaux sont devenus des babillards numériques pour les internautes à cause de leur évolution accélérée. Comme ces sites encouragent les consommateurs à exposer des informations personnelles via des profils et des publications, l'utilisation accrue des médias sociaux a généré des problèmes d’invasion de la vie privée. Des chercheurs ont fait de nombreux efforts pour détecter l'auto-divulgation en utilisant des techniques d'extraction d'informations. Des recherches récentes sur l'apprentissage automatique et les méthodes de traitement du langage naturel montrent que la compréhension du sens contextuel des mots peut entraîner une meilleure précision que les méthodes d'extraction de données traditionnelles. Comme mentionné précédemment, les utilisateurs ignorent souvent la quantité d'informations personnelles publiées dans les forums en ligne. Il est donc nécessaire de détecter les diverses divulgations en langage naturel et de leur donner le choix de tester la possibilité de divulgation avant de publier. Pour ce faire, ce travail propose le « SD_ELECTRA », un modèle de langage spécifique au contexte. Ce type de modèle détecte les divulgations d'intérêts, de données personnelles, d'éducation et de travail, de relations, de personnalité, de résidence, de voyage et d'accueil dans les données des médias sociaux. L'objectif est de créer un modèle linguistique spécifique au contexte sur une plate-forme de médias sociaux qui fonctionne mieux que les modèles linguistiques généraux. De plus, les récents progrès des modèles de transformateurs ont ouvert la voie à la formation de modèles de langage à partir de zéro et à des scores plus élevés. Les résultats expérimentaux montrent que SD_ELECTRA a surpassé le modèle de base dans toutes les métriques considérées pour la méthode de classification de texte standard. En outre, les résultats montrent également que l'entraînement d'un modèle de langage avec un corpus spécifique au contexte de préentraînement plus petit sur un seul GPU peut améliorer les performances. Une application Web illustrative est conçue pour permettre aux utilisateurs de tester les possibilités de divulgation dans leurs publications sur les réseaux sociaux. En conséquence, en utilisant l'efficacité du modèle suggéré, les utilisateurs pourraient obtenir un apprentissage en temps réel sur l'auto-divulgation.Social media and social networking sites have evolved into digital billboards for internet users due to their rapid expansion. As these sites encourage consumers to expose personal information via profiles and postings, increased use of social media has generated privacy concerns. There have been notable efforts from researchers to detect self-disclosure using Information extraction (IE) techniques. Recent research on machine learning and natural language processing methods shows that understanding the contextual meaning of the words can result in better accuracy than traditional data extraction methods. Driven by the facts mentioned earlier, users are often ignorant of the quantity of personal information published in online forums, there is a need to detect various disclosures in natural language and give them a choice to test the possibility of disclosure before posting. For this purpose, this work proposes "SD_ELECTRA," a context-specific language model to detect Interest, Personal, Education and Work, Relationship, Personality, Residence, Travel plan, and Hospitality disclosures in social media data. The goal is to create a context-specific language model on a social media platform that performs better than the general language models. Moreover, recent advancements in transformer models paved the way to train language models from scratch and achieve higher scores. Experimental results show that SD_ELECTRA has outperformed the base model in all considered metrics for the standard text classification method. In addition, the results also show that training a language model with a smaller pre-training context-specific corpus on a single GPU can improve its performance. An illustrative web application designed allows users to test the disclosure possibilities in their social media posts. As a result, by utilizing the efficiency of the suggested model, users would be able to get real-time learning on self-disclosure