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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.

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.

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.

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.

Facebook “likes” are often used as a proxy of users’ attention and an affirmation of what is posted on Facebook (Gerodimos & Justinussen, 2015). To determine what factors predict “likes,” the authors analyzed Facebook posts made by the campaigns of Hillary Clinton, Bernie Sanders, and Donald Trump, the top three candidates from the 2016 US primary election. Several possible factors were considered, such as the types of posts, the use of pronouns and emotions, the inclusion of slogans and hashtags, references made to opponents, as well as candidate’s mentions on national television. The results of an ordinary least-squared regression analysis showed that the use of highly charged (positive or negative) emotions and personalized posts (first-person singular pronouns) increased “likes” across all three candidates’ Facebook pages, whereas visual posts (posts containing either videos or photos) and the use of past tenses were liked more often by Hillary Clinton and Bernie Sanders’ followers than by Trump’s followers. Television mentions boosted likes on Clinton and Sanders’ posts but had a negative effect on Trump’s. The study contributes to the growing literature on digitally networked participation (Theocharis, 2015) and supports the emerging notion of the new “hybrid media” system (Chadwick, 2013) for political communication. The study also raises questions as to the relevance of platforms such as Facebook to deliberative democratic processes since Facebook users are not necessarily engaging with the content in an organic way, but instead might be guided to specific content by the Facebook timeline algorithm and targeted ads.

This study aims to understand the extent to which scholarly networks are connected both in person and through information and communication technologies, and in particular, how distance, disciplines, and motivations for participating in these networks interplay with the clusters they form. The focal point for our analysis is the Graphics, Animation and New Media Network of Centres of Excellence (GRAND NCE), a Canadian scholarly network in which scholars collaborate across disciplinary, institutional, and geographical boundaries in one or multiple projects with the aid of information and communication technologies. To understand the complexity in such networks, we first identified scholars’ clusters within the work, want-to-meet, and help networks of GRAND and examined the correlation between these clusters as well as with disciplines and geographic locations. We then identified three types of motivation that drove scholars to join GRAND: practical issues, novelty-exploration, and networking. Our findings indicate that (1) scholars’ interests in the networking opportunities provided by GRAND may not easily translate into actual interactions. Although scholars express interests in boundary-spanning collaborations, these mostly occur within the same discipline and geographic area. (2) Some motivations are reflected in the structural characteristics of the clusters we identify, while others are irrelevant to the establishment of collaborative ties. We argue that institutional intervention may be used to enhance geographically dispersed, multidisciplinary collaboration.