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.     

Eating Disorder Symptom and Fear Change Trajectories During Imaginal Exposure Therapy: A Slope Network Analysis

Eating disorders (EDs) are characterized by fears related to food, body image, and social evaluation. Exposure-based interventions hold promise for targeting a range of ED fears and reducing ED psychopathology. We investigated change mechanisms and optimal fear targets in imaginal exposure therapy for EDs using a novel approach to network analysis. Individuals with an ED (N = 143) completed up to four online imaginal exposure sessions. Participants reported ED symptoms and fears at pretreatment, posttreatment, and 6-month follow-up. We constructed networks of symptoms (Model 1), fears (Model 2), and combined symptoms and fears (Model 3). Change trajectory networks from the slopes of symptoms/fears across timepoints were estimated to identify how change in specific ED symptoms/fears related to change in other ED symptoms/fears. The most central changing symptoms and fears were feeling fat, fear of weight gain, guilt about one’s weight/shape, and feared concerns about consequences of eating. In Model 3, change in ED fears bridged to change in desire to lose weight, desiring a flat stomach, following food rules, concern about eating with others, and guilt. As slope networks present averages of symptom/fear change slopes over the course of imaginal exposure therapy, further studies are needed to examine causal relationships between symptom changes and heterogeneity of change trajectories. Fears of weight gain and consequences of eating may be optimal targets for ED exposure therapy, as changes in these fears were associated with maximal change in ED pathology. Slope networks may elucidate change mechanisms for EDs and other psychiatric illnesses.     

The educational pyramid: A model for community intervention

Urban adolescents represent a group who are at special risk for a variety of psychological and behavioral problems. A life skills program was implemented with middle and high school students, using an educational pyramid approach to maximize the ecological appropriateness of the intervention and to increase the likelihood that the program will be continued with progressively lessened university facilitation. Additionally, the educational pyramid offers the opportunity for all participants—university faculty, graduate students, collaborating professionals in the community, and high school leaders—to be both teachers and learners.     

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.     

从“孔子学院”看汉语和中国教育的国际化认同

教育无国界是有识之士的共识,也是不可逆转的潮流。中国采取在世界各地建立孔子学院的形式加大吐人对汉语和中国的教育的认识,文章从孔子学院在世界各地的运作形式,阐述了孔子学院发展近四年多来对世界的影响和世界对汉语和中国教育的国际化认同以及对有关的问题进行了反思。     

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.     

The self-reported driving behaviour of young drivers in Lithuania: An application of the behaviour of young novice drivers scale – Lithuania (BYNDS-Li)

BackgroundWith just one year left in the Decade of Action for Road Safety, it is timely nations reflect on their progress in the realm of improving road safety more generally, and in young driver road safety specifically given the pernicious problem that is young driver risky driving behaviour and road crashes. Effective intervention requires a fundamental foundation of understanding the nature of the problem. Therefore the current study explored the self-reported risky driving behaviour of young drivers in Lithuania, a nation classified as a developed country as recently as 2015.MethodThe self-report Behaviour of Young Novice Drivers Scale (BYNDS, 1) was applied in a sample of 457 Lithuanian young drivers aged 18–24 years, after a rigorous forward-backward translation process.ResultsSeven factors (risky exposure, transient rule violations, driver misjudgements, driver mood, vehicle overcrowding, personal seatbelt use, substance consumption) explained 65.2% of the variance in self-reported risky driving behaviour as measured by the BYNDS-Li. The most common risky driving behaviours included driving in excess of posted speed limits, and driving at high risk times such as at night and on weekends.Discussion and implicationsThe seven-factor structure of the BYNDS-Li supports arguments that culturally-valid measures should be operationalised in jurisdictions other than those in which they were developed (in the case of the BYNDS, Queensland, Australia). Moreover, systems thinking argues that interventions and efforts must be multi-sectoral and collaborative interventions. In the case of young driver road safety, these should be framed within the 4E’s of education, engineering, enforcement, and engagement.     

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.