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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.

This chapter explores the media coverage of the 2016 Presidential campaign and reveals the corruption fantasy themes that emerged. Media coverage of corruption can uniquely affect voter attitudes and public policy formulation and implementation, as revealed in previous scholarship on media coverage of corruption. By tracing the competing narratives offered in media coverage utilizing the constant comparative method, the dramatic characters, Crooked Hillary and Corrupt Businessman Trump, are identified and their storylines are explicated. Analysis reveals these dramatic fantasy themes chained through social media, evincing and promoting the narratives that drove media coverage of our political leaders and public policy results. The chapter illustrates that the narratives involving corruption were prominent and negative, further indicating that the media’s obsession with scandal contributed to and supported the narratives that portrayed both candidates as corrupt, adding pollution to the 2016 U.S. political environment.

In the last few years, information and communication technologies (ICTs) and social media have become increasingly relevant to politicians and political parties alike, often used to issue statements or campaigning, among others. At the same time, many citizens have become more involved in politics, partly due to the highly interactive and social environments that the social networking services (SNS) provide. Political events flow through these networks, influencing their users; such events, however, often start offline (outside the online platform) and are, therefore, hard to track. Event studies, a methodology often used in financial and economic studies, can be translated to social networks to help modeling the effect of external events in the network. In the present case, the event study methodology is applied to two sample cases: the tariff war between the United States and China, with multiple responses and retaliations from both sides, and the Brexit referendum. In both cases, the Twitter social networks that arise from users who discuss the respective subjects are analyzed to examine how political events shape and modify the network. Results show how event studies, combined with the possibilities offered by the ICTs both in data retrieval and analysis, can be applied to understand the effect of external political events, allowing researchers to quantitatively track, observe, and analyze the spread of political information over social network platforms. This is a first step toward obtaining a better understanding on how political messages are diffused over social networks and their effects in the network structures and behaviors.

This paper aims to examine how exposure to a presidential candidate's high engagement Instagram images influences a citizen's candidate evaluations.

Data were collected via Amazon MTurk. A 3 × 2 experimental design was employed to test the persuasive effect of exposure of the “most liked” and “most commented on” images of the top four 2016 US presidential primary candidates on a US citizen's candidate evaluation.

Results reveal that highly engaging Instagram images of unfamiliar presidential candidates positively influenced candidate evaluations. However, the same was not true for more well-known presidential candidates.

This study was not conducted during a live campaign and only examined four of the top 2016 presidential primary candidates.

The research includes implications for marketers seeking to increase engagement and reach in Instagram marketing campaigns. This study shows that even brief exposure to a highly engaged post involving an unfamiliar person/product on social media can significantly alter evaluations of that person or product.

To the authors' knowledge, no experimental designs have addressed how Instagram posts influence users' political attitudes and behaviors within the political marketing and communications literature.

The purpose of this paper is to propose and experiment a framework for analysing the tweets to find the basis of popularity of a person and extract the reasons supporting the popularity. Although the problem of analysing tweets to detect popular events and trends has recently attracted extensive research efforts, not much emphasis has been given to find out the reasons behind the popularity of a person based on tweets.

In this paper, the authors introduce a framework to find out the reasons behind the popularity of a person based on the analysis of events and the evaluation of a Web-based semantic set similarity measure applied to tweets. The methodology uses the semantic similarity measure to group similar tweets in events. Although the tweets cannot contain identical hashtags, they can refer to a unique topic with equivalent or related terminology. A special data structure maintains event information, related keywords and statistics to extract the reasons supporting popularity.

An implementation of the algorithms has been experimented on a data set of 218,490 tweets from five different countries for popularity detection and reasons extraction. The experimental results are quite encouraging and consistent in determining the reasons behind popularity. The use of Web-based semantic similarity measure is based on statistics extracted from search engines, it allows to dynamically adapt the similarity values to the variation on the correlation of words depending on current social trends.

To the best of the authors’ knowledge, the proposed method for finding the reason of popularity in short messages is original. The semantic set similarity presented in the paper is an original asymmetric variant of a similarity scheme developed in the context of semantic image recognition.