A Social Network Analysis (SNA) Study On Data Breach Concerns Over Social Media

In the current era of digital devices, the concerns over data privacy and security breaches are rampant. Understanding these concerns by analyzing the messages posted on the social media from linguistic perspective has been a challenge that is increasing in complexity as the number of social media sites increase and the volume of data increases. We investigate the diffusion characteristics of the information attributed to data breach messages, first based on the literary aspects of the message and second, we build a social network of the users who are directly involved in spreading the messages. We found that the messages that involve the technicalities, threat and severity related security characteristics spread fast. Contrary to conventional news channels related posts on social media that capture wide attention, breach information diffusion follows a different pattern. The messages are widely shared across the tech-savvy groups and people involved in security-related studies. Analyzing the messages in both linguistic and visual perspective through social networks, researchers can extract grounded insights into these research questions

Sentiment Analysis of Twitter in Tourism Destinations

[EN] Given the importance of electronic word of mouth (eWOM), this paper analyses the content of messages generated by users related to a tourist destination and shared through Twitter. We propose three research questions regarding eWOM behaviour in Twitter focused on the expertise of the reviewer, sentiment analysis of a tweet and its content.In order to address those research questions we carry out text mining analysis by retrieving existing information on Twitter (over 1500 tweets) regarding to Venice as a tourist destination.Authors acknowledge financial support of research project UV-INV_AE19-1212255.Perez Cabañero, C.; Bigne, E.; Ruiz Mafe, C.; Cuenca, AC. (2020). Sentiment Analysis of Twitter in Tourism Destinations. Editorial Universitat Politècnica de València. 181-189. https://doi.org/10.4995/CARMA2020.2020.11621OCS18118

Social media use among American Indians in South Dakota: Preferences and perceptions

Social media use data is widely being used in health, psychology, and marketing research to analyze human behavior. However, we have very limited knowledge on social media use among American Indians. In this context, this study was designed to assess preferences and perceptions of social media use among American Indians during COVID-19. We collected data from American Indians in South Dakota using online survey. Results show that Facebook, YouTube, TikTok, Instagram and Snapchat are the most preferred social media platforms. Most of the participants reported that the use of social media increased tremendously during COVID-19 and had perceptions of more negative effects than positive effects. Hate/harassment/extremism, misinformation/made up news, and people getting one point of view were the top reasons for negative effects.Comment: 20 pages, 6 figures, 2 Tables, Appendix Tables (7

Exploring Causal Relationships Among Emotional and Topical Trajectories in Political Text Data

We explore relationships between dynamics of emotion (arousal and valence) and topical stability in political discourse in two diachronic corpora of Austrian German. In doing so, we assess interactions among emotional and topical dynamics related to political parties as well as interactions between two different domains of discourse: debates in the parliament and journalistic media. Methodologically, we employ unsupervised techniques, time-series clustering and Granger-causal modeling to detect potential interactions. We find that emotional and topical dynamics in the media are only rarely a reflex of dynamics in parliamentary discourse

"Jane sent me this article, so it must be true!" - How tie strength and emotional tone influence information behavior

Fake news are a threat of the information age, yet many factors that determine their spread, such as emotional tone and tie strength, remain under-researched. Responding to calls for research, we developed an experimental study that explains the impact of emotional tone and tie strength in the context of instant messaging. We hypothesize effects on the willingness to fact-check and intention to share, mediated by sender credibility and news believability. Our results will contribute to the academic literature on various levels: we consider the emotional and relational dimensions of fake news sharing. Furthermore, we provide a multidimensional understanding of the emotionality of strong-tie contacts sharing fake news in a seemingly private and safe environment. For policymakers, we provide insights that help detect fake news, and we provide individuals with persuasion knowledge to self-protect against fake news

Antecedents of Online Customers Reviews’ Helpfulness: A Support Vector Machine Approach

Online customer reviews (OCRs) have become an important part of online customers’ decision making and People use online reviews to make decision to buy or not to buy products and services. This study aims to answer two research questions: (1) what are the antecedents of helpfulness of online reviews based on their contents? (2) How do content-based cues on OCRs influence their helpfulness? We posit a research model to study the effect of peripheral and central cues in OCRs on online review helpfulness. Online review web pages will be collected from Amazon website using a web crawler. This article will be one of the first studies that investigate OCRs helpfulness based on the central cues in the text of the review. In addition, this research will be the first study that applied the support vector machine as a machine learning method to analyze the text of OCRs

Escaping the Echo: Understanding the Impact of Social Media on Overconcentration of Emerging Technology Security Threats

Social media platforms prioritize sensational or trending content, often overshadowing less popular but important topics and hindering discourse diversification. They evolve into echo chambers, where users predominantly encounter views aligned with their own. Security threat awareness for emerging technologies remains restricted, primarily because of the overconcentration of discussions influenced by both human and algorithmic factors. We seek to identify security threats related to emerging technology that are overshadowed and underrepresented due to the overconcentration of others. Next, we study uncertainty reduction approaches and emotional appraisal dimensions to understand how they contribute to the amplification or overconcentration of specific security threats. By combining computational NLP techniques to detect overconcentrated topics with scenario-based factorial surveys, this study proposes to provide a thorough examination of threat amplification in the realm of social media

Donald Trump’s use of Twitter during his campaign ahead of the US presidential election of 2016 – why Trump’s tweets are emotionally effective

This paper shows how Donald Trump uses Twitter to spread emotions, more specifi cally fear and anger. Noteworthy is the fact that Trump’s discourse is not primarily emotional. Although anger may sound legitimate, fear is viewed as an emotion one should be ashamed of. Rather than verbalising these emotions, discrediting his opponents – i.e. other candidates and journalists – and legitimising his own discourse for the sake of moral values are the hallmarks of Trump’s rhetoric. By presenting the future as precarious and uncertain, he stands as the only one able to make America powerful and infl uential (again). He adopts the stance of a victim, which is amplifi ed by the impact that social networks have on communities in terms of affi liation