K. Hazel Kwon

Social Network Influence on Online Behavioral Choices: Exploring Group Formation on Social Network Sites

Social media communication is characterized by reduced anonymity and off-to-online social interactions. These characteristics require scholars to revisit social influence mechanisms online. The current study builds on social influence literature to explore social network and gender effects on online behavior. Findings from a quasi-experiment suggest that both network-related variables and gender are significantly associated with online behavior. Perceived social environment, measured by personal network exposure rate, is more significant than objective reality, measured by frequency of received social messages, in determining behavior. We discuss the implications of social contagion effects on web-based strategic communication—including advertising, political campaigns, and social mobilization. Data limitations and the difficulty of measuring social network influence via social media are also discussed.

The impacts of identity verification and disclosure of social cues on flaming in online user comments

Study examines a large news commenting data to understand anonymity effect on flaming.Different anonymity control mechanisms affect online flaming differently.Voluntary approach is more effective than policy approach in reducing flaming.Social commenting is more effective for frequent users than for one-time users. While a growing body of literature attests to the relationship between user identifiability and inflammatory speech online, few studies have investigated the ways in which different anonymity control mechanisms affect the quality of online discussions. In this study, two mechanisms, a policy-driven and a voluntary approach, are examined for their conditional and interaction effects on reducing flaming in user comments online. Based on a large-scale, real-world data on political news comments in South Korea, the results suggest that whereas the policy-driven regulation does not reduce, and even increases, flaming, the voluntary approach significantly decreases it, especially among the moderate commenters. The findings are further speculated from an economic perspective by which transaction costs are perceived differently contingent on the ways in which anonymous commenting is regulated.

Swearing Effects on Citizen-to-Citizen Commenting Online

Swearing, the use of taboo languages tagged with a high level of emotional arousal, has become commonplace in contemporary political culture. The current study attempts to understand the ways in which swearing influences citizen-to-citizen news commenting online. Based on a large corpus of the 2-month user comments from 26 news websites in South Korea, the study examines swearing effects as well as its interplay with anonymity on garnering public attention and shaping other users’ perceptions of the comments. Findings suggest that swearing generally has a positive effect on increasing user attention to comments as well as gaining other users’ approvals. Comparisons between political and nonpolitical topics further suggest that swearing effect on gaining public attention is particularly prominent for political discussions. In contrast, the magnitude of change toward positive valence in public perception to comments is much greater for nonpolitical topics than for politics. From the findings, we conclude that an acceptable degree of swearing norms in online discussions vary across news topical arenas. The results also lead to discussions about the possibility of like-minded exposure to political comments as a default condition for online discussions. Finally, the study highlights the role of high-arousal emotions in shaping discursive participation in contemporary networked sociodigital environment.

Tweeting Badges: User Motivations for Displaying Achievement in Publicly Networked Environments

Badge systems, a common mechanism for gamification on social media platforms, provide a way for users to present their knowledge or experience to others. This study aims to contribute to the understanding of why social media users publicize their achievements in the form of online badges. Five motivational factors for badge display in public networked environments are distinguished-self-efficacy, social incentives, networked support, passing time, and inattentive sharing-and it is suggested that different badge types are associated with different motivations. System developers are advised to consider these components in their designs, applying the elements most appropriate to the communities they serve. Comparing user motivations associated with badges shared across boundaries provides a better understanding of how online badges relate to the larger social media ecosystem.

Is offensive commenting contagious online? Examining public vs interpersonal swearing in response to Donald Trump’s YouTube campaign videos

Purpose The purpose of this paper is to explore the spillover effects of offensive commenting in online community from the lens of emotional and behavioral contagion. Specifically, it examines the contagion of swearing – a linguistic mannerism that conveys high-arousal emotion – based upon two mechanisms of contagion: mimicry and social interaction effect. Design/methodology/approach The study performs a series of mixed-effect logistic regressions to investigate the contagious potential of offensive comments collected from YouTube in response to Donald Trump’s 2016 presidential campaign videos posted between January and April 2016. Findings The study examines non-random incidences of two types of swearing online: public and interpersonal. Findings suggest that a first-level (a.k.a. parent) comment’s public swearing tends to trigger chains of interpersonal swearing in the second-level (a.k.a. child) comments. Meanwhile, among the child-comments, a sequentially preceding comment’s swearing is contagious to the following comment only across the same swearing type. Based on the findings, the study concludes that offensive comments are contagious and have impact on shaping the community-wide linguistic norms of online user interactions. Originality/value The study discusses the ways in which an individual’s display of offensiveness may influence and shape discursive cultures on the internet. This study delves into the mechanisms of text-based contagion by differentiating between mimicry effect and social interaction effect. While online emotional contagion research to this date has focused on the difference between positive and negative valence, internet research that specifically looks at the contagious potential of offensive expressions remains sparse.

Is Aggression Contagious Online? A Case of Swearing on Donald Trump’s Campaign Videos on YouTube

This study explores whether aggressive text-based interactions in social media are contagious. In particular, we examine swearing behaviour of YouTube commentators in response to videos and comments posted on the official Donald Trump’s campaign channel. Our analysis reveals the presence of mimicry of verbal aggression. Specifically, swearing in a parent comment is significantly and positively associated with the likelihood and intensity of swearing in subsequent ‘children’ comments. The study also confirms that swearing is not solely a product of an individual speech habit but also a spreadable social practice. Based on the findings, we conclude that aggressive emotional state can be contagious through textual mimicry.

Proximity and Terrorism News in Social Media: A Construal-Level Theoretical Approach to Networked Framing of Terrorism in Twitter

This study investigates networked framing of terrorism news in Twitter by distinguishing three proximity effects (geographic, social, and temporal proximity) on audience and media institutional frames (i.e., episodic/thematic and space frames), based on construal-level theory. An analysis of tweets during the Boston Marathon bombing and the Brussels Airport attack finds that institutional and audience frames show similarity but do not always converge on Twitter. Similarities in the audience and institutional frames are attributed to a universal human tendency for social categorization, inherent in the minds of not only ordinary citizens but also journalists. Proximity effects, however, were more salient on audience frames than on institutional frames.

Crisis and Collective Problem Solving in Dark Web: An Exploration of a Black Hat Forum

This paper explores the process of collective crisis problem-solving in the darkweb. We conducted a preliminary study on one of the Tor-based darkweb forums, during the shutdown of two marketplaces. Content analysis suggests that distrust permeated the forum during the marketplace shutdowns. We analyzed the debates concerned with suspicious claims and conspiracies. The results suggest that a black-market crisis potentially offers an opportunity for cyber-intelligence to disrupt the darkweb by engendering internal conflicts. At the same time, the study also shows that darkweb members were adept at reaching collective solutions by sharing new market information, more secure technologies, and alternative routes for economic activities.

A system for intergroup prejudice detection: The case of microblogging under terrorist attacks

Abstract Intergroup prejudice is a distorted opinion held by one social group about another, without examination of facts. It is heightened during crises or threat. It finds expression in social media platforms when a group of people express anger, resentment and dissent towards another. This paper presents a system for automated detection of prejudiced messages from social media feeds. It uses a knowledge discovery framework that preprocesses data, generates theory-driven linguistic features along with other features engineered from textual content, annotates and models historical data to determine what drives detection of intergroup prejudice especially during a crisis. It is tested on tweets collected during the Boston Marathon bombing event. The system can be used to curb abuse and harassment by timely detection and reporting of intergroup prejudice.

Disentangling User Samples: A Supervised Machine Learning Approach to Proxy-population Mismatch in Twitter Research

ABSTRACT This study addresses the issue of sampling biases in social media data-driven communication research. The authors demonstrate how supervised machine learning could reduce Twitter sampling bias induced from “proxy-population mismatch”. Particularly, this study used the Random Forest (RF) classifier to disentangle tweet samples representative of general publics’ activities from non-general—or institutional—activities. By applying RF classifier models to Twitter data sets relevant to four news events and a randomly pooled dataset, the study finds systematic differences between general user samples and institutional user samples in their messaging patterns. This article calls for disentangling Twitter user samples when ordinary user behaviors are the focus of research. It also builds on the development of machine learning modeling in the context of communication research.