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

Great Divisions: The Evolution of Polarization During the Man-made Emergency of January 6, 2021.

Polarization, which refers to the formation of two opposing groups based on the users' beliefs and opinions, has a growing body of literature. However, social media polarization differs from offline polarization in that beliefs change almost instantaneously on social media as a result of events unfolding. We investigate the uses of social media communication that has resulted in polarized opinions among individuals prior to, during, and after the January 6th Capitol riots. Analyses of the dominant narratives on Twitter surrounding the incident reveal a high level of polarization throughout the unfolding of the event, with increased polarization possibly attributable to the onset of the crisis. We also observed that polarization is a dynamic phenomenon: as an event unfolds, polarization changes, and knowing how it changes is important for timely crisis resolution. We propose three measures of polarization that could be used to examine polarization accurately during a crisis

Where Does My Product Stand? A Social Network Perspective on Online Product Reviews

Customer reviews often include comparative comments on competing products. Adopting the The Strength of Weak Ties theory, we build a product social network around “strong tie” and “weak tie” entities. By performing text mining on comparative customer reviews collected from Amazon, we successfully identify strong and weak ties in a product network and compute the strength of these ties. Utilizing these network properties, we generate network graphs based on different product features and discover the underlying competitive relationships among them. In particular, our regression analysis shows that the strength of ties positively contributes to the review rating of a product and the strength of weak ties plays a more significant role than the strength of strong ties. These results will benefit vendors in online market to discover potential competitors, effectively tailor their marketing and product development efforts, and better position their products to increase profit and explore new market opportunities

User engagement and uncertainty from COVID-19 misinformation on social media: an examination of emotions and harms

During the COVID-19 pandemic, people were often exposed to harmful social media misinformation. Prior studies have devoted their efforts to detecting misinformation and understanding the psychological features related to misinformation. This paper contributes to the literature of handling crisis misinformation by connecting psychological characteristics to people’s actual actions. Anchoring on social media user engagement reflected in the numbers of retweets, we examine the effects of expressed uncertainty and emotions as well as various platform-specific aspects (hashtags and URLs) by extracting features from captured conversations on Twitter social media platform. Subsequently, we quantify expected harms from the chosen COVID-19 misinformation scenarios from the judgements of several healthcare experts, which were then utilized to classify scenarios into different categories for further analyses. With much of the hypotheses supported in both main effects and interaction effects, the study has theoretical contributions in establishing a mechanism to measure expressed uncertainty and emotions from captured Twitter conversations, measuring misinformation harms from professional experts and examining causal relationships between social media behaviour and uncertainty, emotions, harms and several platform specific features. It also has practical contributions of deriving insights to help involved stakeholders in crisis communications understand the role of misinformation harms, and to reduce misinformation diffusion and minimize possible harms

Debunking Misinformation Using a Game Theoretic Approach

The growth in social media communication has witnessed an increase in misinformation. The harmful effects of misinformation range from defamation of reputation to loss of life. Information Systems research has a long history of studying the antecedents of misinformation and how to find out when it occurs. However, there is limited research on how to deal with the harm caused by misinformation. The governing bodies responsible for handling misinformation need to be strategic in addressing ever-increasing misinformation and prioritizing the type of misinformation. In this study, we propose an evolutionary theoretic-based game model to establish equilibrium to optimize the efforts needed by the governing bodies to debunk the misinformation. We propose to empirically validate our model using COVID-19-related social media tweets using experiments. In doing so, we contribute to a growing body of literature on misinformation harm and evolutionary game theory, while offering practical solutions to address an important societal problem

An Exploratory Analysis of Alarming and Reassuring Messages in Twitterverse during the Coronavirus Epidemic

Twitter has become one of the widely used crisis communication tools. In the recent coronavirus outbreak, social media users exchange millions of messages ranging from crisis response to awareness creating messages. We conduct an exploratory analysis using tweets collected over initial one month of its outbreak to unveil useful trends and gain insights into managing crisis. Our findings from the analysis present that both alarming and reassuring messages surface during crisis situations, however, it is the reassurance messages that build confidence in individuals to overcome the distress that an epidemic creates. Following the guidelines of Crisis Management, we establish a linguistic based approach to explore the tweets text. Our findings provide insights that the gap between the number of alarming and reassuring messages widen as the epidemic spreads. Our findings also show that significantly, there exists a cluster of similar messages among the deluge of information. Identifying these clusters help manage the crisis situation

HOW HELPFUL ARE COMPARATIVE REVIEWS FOR PREDICTING PRODUCT DEMAND?

The increased Internet usage has driven a rapid growth of e-commerce transactions. One of the key determinants of the increased online transactions is the influence of electronic word-of-mouth (eWOM) in the form of online reviews. In particular, comparative reviews that compare similar products provide valuable information for consumers to evaluate multiple products and play a pivotal role in driving consumer purchase decisions. By constructing a product network based on products connected by comparative reviews, we develop several new network centrality measures and empirically examine the impact of eWOM through these new centrality measures and the semantic similarity of the comparative reviews. We find that the comparative reviews are key eWOM measures that influence the product’s sales within a product network. Our findings also demonstrate that the text semantic similarity is a better measure of the strength of tie in a comparative product network than the review sentiment. Our study contributes to the eWOM literature by utilizing text review semantic similarity to capturing review strength based on the latent product features, and to the network graph theory through the new centrality measures we have developed. Overall, our findings provide important insights for e-commerce platform operators and vendors to leverage the impact of eWOM and help consumers compare products in a more effective manner

Cyber-rumor Sharing: The Case of Zika Virus

The Zika Virus brought spread fear not only because of its direct impact on many lives but also due to the various cyber-rumor messages spread across social media (Valecha et al., 2017). It is not surprising that several medical societies, WHO, CDC, etc. have put in their best efforts in educating people about the situation. Due to the significant time taken by those organizations in providing factual information, there has been a widespread problem of rumors associated with health information. When health information is manipulated, it results in frustration, panic and disturbances among the lives of general people. It comes as no surprise that the Atlantic states, “of all the categories of [misinformation], health news is the worst.†\ \ The dark side of social media in the form of cyber-rumors has motivated many researchers in behavioral Information Systems to study the core nature of the message (Oh et al., 2018). A key element for successful health-related cyber-rumor management is “to understand what makes citizens prone to engaging in [health-related] cyber-rumor sharing†(Kwon & Rao, 2017; p. 307). Prior literature in the rumor context has investigated polarity of a rumor in a news channel (Oh et al., 2013). It has found that most of the rumors that spread in case of a disaster scenario possess negative sentiment. In this paper, we focus on another characteristic of the cyber-rumor message – distance from point of interest (Kwon et al., 2017). \ \ In this paper, we argue that the spread of a rumor is closely associated with the distance from point of interest. The farther the distance, the greater is the uncertainty of information in the healthcare context. Following this, we explore whether citizens’ cyber-rumor sharing is influenced by distance. We investigate how the concept of distance affects diffusion characteristics of social media messages. Specifically, we incorporate social, temporal and spatial distances in the context of health messages to evaluate its diffusion. We conceptualize these distances in a generalizable and operational way, and address the following research question associated with cyber-rumor sharing in the context of Zika virus: What proximity characteristics (social, temporal and spatial) of a message make it spread? \ \ We focus on citizens’ cyber-rumor sharing tendency that arises within the larger context of a Zika virus health crisis on Twitter social media platform. The focus of this emergent research paper is to conceptualize the social, temporal and spatial distances, and build a regression model that investigates the effect of these distances on cyber-rumor sharing. Such a model will allow us to quickly identify the emergence of viral rumor messages and monitor the ongoing development of these messages in a timely manner. Such a model will allow more efficient utilization of communication channels which help healthcare officials to reduce panic situations and promote reliable information sharing. Preliminary results confirm our claim that as temporal and spatial distance increases, so does the cyber rumor sharing. Contrary to our claim, we find that as social distance increases, cyber rumor sharing decreases.

User Privacy, Surveillance and Public Health during COVID-19 – An Examination of Twitterverse

Online users frequently rely on social networking platforms to transmit public concerns and raise awareness about societal issues. With many government organizations actively employing social media data in recent times, the need for processing public concerns on social media has become a critical topic of interest across academic scholars and practitioners. However, the growing volume of social media data makes it difficult to process all the issues under a single umbrella, causing to overlook the main topic of interest within communication technologies, such as privacy. For example, during the COVID-19 pandemic, arguments on privacy and health issues exploded on Twitter, with several threads centered on contact tracking, health data gathering, and its usage by government agencies. To address the challenges of rising data volumes and to understand the importance of privacy concerns, particularly among users seeking greater privacy protection during this pandemic, we conduct a focused empirical analysis of user tweets about privacy. In this two-part research, our first study reveals three macro privacy issues of discussion distilled from the Twitter corpus, subsequently subdivided into 12 user privacy categories. The second study builds on the findings of the first study, focusing on the primary difficulties highlighted in the macro privacy subjects—contact tracing and digital surveillance. Using a document clustering approach, we present implications for the focal privacy topics that policymakers, agencies, and governments should consider for offering better privacy protections and help the community rebuild