When I Hate to Follow You: Hate-Following, Envy, and Schadenfreude on Instagram

Popular media has documented an increasing interest in the phenomenon of “hate-following”. The current study explored the utility of hate-following motive, independent of hate, within the framework of dual approach of envy and schadenfreude. Undergraduate Instagram’s users (n = 560, 73.39% females; M age =21.14, SD = 2.04) were asked to recall an envy episode and filled in measures of state envy, hate-following motive, and hate, then read a schadenfreude scenario and completed measure of schadenfreude. We found that both hate-following motive and hate were positively associated with pain of envy, malicious envy, and schadenfreude, but only hate-following motive positively associated with benign envy. While the hate-followers experiences of malicious envy predicted higher schadenfreude, the presence of benign envy predicted lower schadenfreude. These findings highlight the unique contribution of hate-following motive, while at the same time open up many questions as to how this antisocial but nevertheless functional motive may be improved

INFORMATION AND PSYCHOLOGICAL EFFECTS AS A STRATEGY OF MODERN CAMPAIGN

Статья содержит результаты теоретического анализа феномена личностной устойчивости к информацион-психологическому воздействию. Представлены результаты пилотажного исследования субъективной оценки значимых социально-психологических факторов устойчивости к информационно-психологическому воздействию на примере предвыборной кампании.The article contains the results of a theoretical analysis of the phenomenon of resistance to the personal information and psychological warfare. The results of the pilot study of subjective evaluation of relevant socio-psychological factors of resistance to information and psychological impact on the example of the campaign

Possibilities of Predicting a Person's Substance Use Behaviour and Mental Health Through Social Media in a COVID-19 Crisis Context

The negative psychological consequences of the COVID-19 pandemic and the forced isolation of a large proportion of people worldwide have demonstrated the need to develop ways and technologies to reduce the effects of sudden threats of this type. The basis of any practical work to minimize the negative psychological consequences of the COVID-19 pandemic associated with substance use is the monitoring and diagnosis of the psychological resources of the individual. The article aims to show the possibilities of predicting the behavior of an individual through the content analysis of posts and reposts of their profile on the social network VKontakte on the example of the propensity to use psychoactive substances and to substantiate the possibilities of optimizing and automating such prediction through the use of category markers. Content analysis was carried out by latent semantic analysis of texts extracted from posts and reposts of VKontakte social network users with subsequent content analysis through selecting markers - category words. As a result, a categorical grid was built, which increases the efficiency of content analysis of posts and reposts of users and is suitable for further automation of such research by machine learning methods.1-1