Towards Measuring Adversarial Twitter Interactions against Candidates in the US Midterm Elections

Adversarial interactions against politicians on social media such as Twitter have significant impact on society. In particular they disrupt substantive political discussions online, and may discourage people from seeking public office. In this study, we measure the adversarial interactions against candidates for the US House of Representatives during the run-up to the 2018 US general election. We gather a new dataset consisting of 1.7 million tweets involving candidates, one of the largest corpora focusing on political discourse. We then develop a new technique for detecting tweets with toxic content that are directed at any specific candidate.Such technique allows us to more accurately quantify adversarial interactions towards political candidates. Further, we introduce an algorithm to induce candidate-specific adversarial terms to capture more nuanced adversarial interactions that previous techniques may not consider toxic. Finally, we use these techniques to outline the breadth of adversarial interactions seen in the election, including offensive name-calling, threats of violence, posting discrediting information, attacks on identity, and adversarial message repetition

A Survey on Cybercrime Using Social Media

There is growing interest in automating crime detection and prevention for large populations as a result of the increased usage of social media for victimization and criminal activities. This area is frequently researched due to its potential for enabling criminals to reach a large audience. While several studies have investigated specific crimes on social media, a comprehensive review paper that examines all types of social media crimes, their similarities, and detection methods is still lacking. The identification of similarities among crimes and detection methods can facilitate knowledge and data transfer across domains. The goal of this study is to collect a library of social media crimes and establish their connections using a crime taxonomy. The survey also identifies publicly accessible datasets and offers areas for additional study in this area

"My boss is racist" A study of employees' perceptions of boss' racist comments on Twitter

Diverse populations in the United States have recognized a structure of systematic racism in American workplace. This study seeks to identify different types of perceived racism by employers and evidence of perceived organizational injustice. To identify racism in the workplace, this study focused on employee tweets. Specifically, two content analyses were conducted to distinguish between perceived racist behaviors. The first analysis uses the Equal Employment Opportunity Commission’s (EEOC) model of discrimination types to identify the types of discrimination within the tweets, and the second analysis uses Colquitt’s (2011) organizational justice to find evidence of organizational injustices within the tweets. Results supported that employees perceived both verbal and nonverbal racist behaviors by their bosses, and those racist behaviors include all three types of workplace, harassment, and policy discrimination as defined by the EEOC. In addition, this study also concluded that employees perceive procedural, interpersonal, distributive, and informational injustices through boss’ racist behaviors