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Although the targets of envy have received increasing attention in management research, how envied employees respond to envy remains ambiguous and merits further investigation. Drawing upon regulatory focus theory, this paper aims to reconcile these inconsistent findings by developing and testing a model that elucidates how different types of being envied (i.e. benignly or maliciously) can elicit either favorable or unfavorable motivational and behavioral reactions.

An experience sampling study was conducted on 131 employees across 10 consecutive workdays in China. Focusing on within-person effects, multilevel mediation models using multilevel structural equation modeling were applied.

Results indicated that on days when employees are benignly envied, they engage in more organizational citizenship behavior (OCB) due to increased daily promotion focus. On the contrary, on days when employees are maliciously envied, they participate in more counterproductive work behavior (CWB) due to decreased daily promotion focus.

Organizations and managers should take a more holistic view of workplace envy when considering that envied employees may use OCB to deal with benign envy. Conversely, considering that CWB may emerge from employees who are maliciously envied, it is crucial for managers to be vigilant in discouraging and addressing malicious envy in the workplace.

This paper takes an initial foray into incorporating the concepts of benign envy and malicious envy into the literature on being envied and provides a novel perspective to explain why being envied can lead to both functional and dysfunctional responses.

Social media is replete with malicious and unempathetic rhetoric yet few studies explain why these emotions are publicly dispersed. The purpose of the study is to investigate how the intergroup counter-empathic response called schadenfreude originates and how it prompts media consumption and engagement.

The study consists of two field surveys of 635 in-group members of two professional sports teams and 300 residents of California and Texas with political party affiliations. The analysis uses SEM quantitative methods.

Domain passion and group identification together determine the harmonious/obsessive tendencies of passion for an activity and explain the schadenfreude response toward the rival out-group. Group identification is a stronger driver of obsessive passion compared to harmonious passion. Schadenfreude directly influences the use of traditional media (TV, radio, domain websites), it triggers social media engagement (posting), and it accelerates harmonious passion's effects on social media posting.

The study is limited by the groups used to evaluate the research model, sports, and politics.

The more highly identified and passionate group members experience greater counter-empathy toward a rival. At extreme levels of group identification, obsessive passion increases at an increasing rate and may characterize extremism. Harboring feelings of schadenfreude toward the out-group prompts those with harmonious passion for an activity to more frequently engage on social media in unempathetic ways.

This study links the unempathetic, yet common emotion of schadenfreude with passion, intergroup dynamics, and media behavior.

Social networks (SNs) have recently evolved from a means of connecting people to becoming a tool for social engineering, radicalization, dissemination of propaganda and recruitment of terrorists. It is no secret that the majority of the Islamic State in Iraq and Syria (ISIS) members are Arabic speakers, and even the non-Arabs adopt Arabic nicknames. However, the majority of the literature researching the subject deals with non-Arabic languages. Moreover, the features involved in identifying radical Islamic content are shallow and the search or classification terms are common in daily chatter among people of the region. The authors aim at distinguishing normal conversation, influenced by the role religion plays in daily life, from terror-related content.

This article presents the authors' experience and the results of collecting, analyzing and classifying Twitter data from affiliated members of ISIS, as well as sympathizers. The authors used artificial intelligence (AI) and machine learning classification algorithms to categorize the tweets, as terror-related, generic religious, and unrelated.

The authors report the classification accuracy of the K-nearest neighbor (KNN), Bernoulli Naive Bayes (BNN) and support vector machine (SVM) [one-against-all (OAA) and all-against-all (AAA)] algorithms. The authors achieved a high classification F1 score of 83\%. The work in this paper will hopefully aid more accurate classification of radical content.

In this paper, the authors have collected and analyzed thousands of tweets advocating and promoting ISIS. The authors have identified many common markers and keywords characteristic of ISIS rhetoric. Moreover, the authors have applied text processing and AI machine learning techniques to classify the tweets into one of three categories: terror-related, non-terror political chatter and news and unrelated data-polluting tweets.