Aha over Haha: Brands benefit more from being clever than from being funny

We examine whether the cleverness of a brand's humor attempt affects consumers' brand attitudes and engagement. A clever humor attempt is any humor attempt wherein the consumer feels she must make mental connections to solve the joke (e.g., understand a cultural reference, understand the dual meaning of a pun). Across five studies, we demonstrate that as the cleverness of a humor attempt increases, consumers report higher brand attitudes and are more engaged with the brand. This effect is mediated by perceptions of brand warmth and competence and moderated by consumers' need for cognition.     

Does partisan media make a pawn of mistrust? Institutional trust and preventive COVID-19 health behaviors in a polarized pandemic

In a rapidly developing crisis such as the COVID-19 pandemic, people are often faced with contradictory or changing information and must determine what sources to trust. Across five time points (N = 5902) we examine how trust in various sources predicts COVID-19 health behaviors. Trust in experts and national news predicted more engagement with most health behaviors from April 2020 to March 2022 and trust in Fox news, which often positioned itself as counter to the mainstream on COVID-19, predicted less engagement. However, we also examined a particular public health behavior (masking) before and after the CDC announcement recommending masks on 3 April 2020 (which reversed earlier expert advice discouraging masks for the general public). Prior to the announcement, trust in experts predicted less mask-wearing while trust in Fox News predicted more. These relationships disappeared in the next 4 days following the announcement and reversed in the 2 years that follow, and emerged for vaccination in the later time points. We also examine how the media trusted by Democrats and Republicans predicts trust in experts and in turn health behaviors. Broadly we consider how the increasingly fragmented epistemic environment has implications for polarization on matters of public health.     

“Keep them out to save our inside:” discourses on immigration by the Spanish far right

Vox is a far-right, Spanish political party that has steadily grown to become the third main party in the national congress. Immigration is a major presence in Vox's political agenda. Through Critical Discourse Analysis, we analyze the party's public speeches and Twitter communications on immigration in the last 3 years, from the beginning of the COVID-19 pandemic in 2020 to the Ukraine-Russia war in 2022. These contexts have provided a fertile ground for Vox's concerns with the protection of national borders, the criminalization of African and irregular immigrants, and the Spanish Government's ineffectiveness to protect the Spaniards' homes. Vox's main discursive strategies entail constructions of migrants and migration based on dichotomous binaries, culture clash, exclusionary discourses of domopolitics, and fears of imminent social and cultural changes. These constructions are based on the unproblematized belief on essential and unchangeable values that forge the identity of the homeland, which is implicitly threatened by immigrants. Against the migratory invasion, Vox constitutes itself as the ethical protector of the Spanish society and nation, “out of care for the insiders and not out of hatred for outsiders.”     

Examining approaches to encourage COVID-19 vaccination on social media

Following the release of the first COVID-19 vaccinations many people utilized social media to promote vaccination among their social circles. These attempts to persuade others to get vaccinated ranged from positive encouragement (e.g., emphasizing the prosocial benefits and positive outcomes) to shame and threats (e.g., name calling and threating to end friendships over vaccination status). The present study investigated how these different social media messages affected COVID-19 vaccination intentions. In June 2021, shortly after vaccines had been made freely available to anyone over the age of 16 in the United States, unvaccinated participants read a manipulated Twitter message designed to be either encouraging or shaming. Message-type did not significantly affect intentions to become vaccinated against COVID-19; however, participants who saw the encouraging message reported that the post made them feel more likely to get vaccinated. Self-efficacy was also manipulated but did not reveal any significant effects. Additional analyses suggest that having personal experience with COVID-19 moderates reactions to these different messages. We discuss limitations and promising avenues for future research on the effects of social media messages on health behaviors.     

Does online social support uniquely buffer effects of stress during the COVID-19 pandemic?: A natural experiment

In-person sources of social support buffer effects of stress on mental health. However, online social support inconsistently demonstrates stress-buffering effects. Highly stressful circumstances, such as the first month of COVID-19 lockdown, may be necessary to benefit from support received from online networks. We investigated whether online support demonstrated an increased stress-buffering effect on depressive symptoms during the first month of COVID-19 lockdown. We collected cross-sectional data on three distinct groups of participants from February to April 2020—preceding lockdown (pre-COVID; n = 53), up to four weeks following university closures (initial lockdown; n = 136), and the final weeks of the semester (later lockdown; n = 127). Initial lockdown participants reported significantly more stress than pre-COVID but not later lockdown participants. The online social support by stress by COVID phase interaction was only significant for the initial versus later lockdown comparison. Online support buffered stress during initial lockdown but not later lockdown. Stress-buffering effects of offline support were observed and did not depend on COVID phase. Online support may only buffer stress when stress is heightened and offline support is less available.     

Imitative violence in the real world: A reanalysis of homicide rates following championship prize fights

Recently, Phillips [Am Soc Rev, 1983; 48:560–568] reported that the homicide rate increases on the third day after heavyweight championship prize fights. The present paper reports a reanalysis of Phillips's data using more sophisticated statistical techniques and examining several theoretically important variables not discussed by Phillips or his critics. Using a conservative analysis strategy, our results suggest that the increases in homicides reported by Phillips were not a methodological artifact as suggested by Baron and Reiss [Am Soc Rev 1985; 50:347–363, 372–376]. The homicide increases only occur on the first weekend or holiday after prize fights that receive the greatest publicity.     

Popular Politics and the Clinton/Lewinsky Affair: The Implications for Leadership

The Clinton/Lewinsky scandal unfolded in an era of "new media" politics that presented fresh and often unanticipated challenges for presidential leadership. New media actors, such as call-in talk radio and TV hosts, tabloid journalists, and Internet gossip columnists, played a significant role in scandal politics. They influenced the framework within which stories were reported and perceived by the public. New media channels, in particular, framed the events leading up to the presidential impeachment in terms of dramatic, prime time–style entertainment. This entertainment news frame allowed citizens to compartmentalize their perceptions of President Clinton as a leader versus a private individual involved in a sex scandal. Media Politics can explain, at least in part, President Clinton's strong job performance evaluations in the midst of one of the most publicized political scandals of the century.     

Social media reduce users' moral sensitivity: Online shaming as a possible consequence

In this study, we propose that social media reduce users' moral sensitivity through the mediation of the perceived moral intensity of hostile comments, which leads to behavioral consequences for online shaming. Three separate studies were conducted to explore this statement. Study 1 (N = 160) compared moral sensitivity between participants in simulated social media situations and a control group. Study 2 (N = 412) tested the mediating role of perceived moral intensity through self-rated questionnaires. Study 3 (N = 295) examined the behavioral consequences of reduced moral sensitivity on online shaming by manipulating social media and perceived moral intensity. Across these three studies with their different methodologies, we found consistent support for our prediction that social media reduce users' moral sensitivity. Also, our findings shed light on perceived moral intensity as a mediator. As expected, less perceived moral intensity and less moral sensitivity (as serial mediators) induced by social media led to a higher tendency to participate in online shaming. In addition, our research suggests that the harmful effects of social media could be restricted by improving users' perceived moral intensity in the form of reminders. These findings provide novel insights into the underlying mechanism of cyberviolence on social media and also contribute to the literature on the antecedents and consequences of moral sensitivity.     

FNDNet – A deep convolutional neural network for fake news detection

With the increasing popularity of social media and web-based forums, the distribution of fake news has become a major threat to various sectors and agencies. This has abated trust in the media, leaving readers in a state of perplexity. There exists an enormous assemblage of research on the theme of Artificial Intelligence (AI) strategies for fake news detection. In the past, much of the focus has been given on classifying online reviews and freely accessible online social networking-based posts. In this work, we propose a deep convolutional neural network (FNDNet) for fake news detection. Instead of relying on hand-crafted features, our model (FNDNet) is designed to automatically learn the discriminatory features for fake news classification through multiple hidden layers built in the deep neural network. We create a deep Convolutional Neural Network (CNN) to extract several features at each layer. We compare the performance of the proposed approach with several baseline models. Benchmarked datasets were used to train and test the model, and the proposed model achieved state-of-the-art results with an accuracy of 98.36% on the test data. Various performance evaluation parameters such as Wilcoxon, false positive, true negative, precision, recall, F1, and accuracy, etc. were used to validate the results. These results demonstrate significant improvements in the area of fake news detection as compared to existing state-of-the-art results and affirm the potential of our approach for classifying fake news on social media. This research will assist researchers in broadening the understanding of the applicability of CNN-based deep models for fake news detection.