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- All Subjects: Cyberbullying
- Creators: Dani, Harsh
Description
The present study was designed to extend previous research on early adolescents' involvement in electronic aggression and victimization. A new measure for electronic victimization and aggression was created for this study in order to better assess this type of peer harassment in early adolescence. The first goal of the study was to describe young adolescents' involvement in electronic aggression and victimization by exploring the links between electronic victimization and aggression and (a) youth demographic characteristics (e.g., gender, ethnicity), (b) involvement in traditional forms of aggression and victimization, and (c) gender of the aggression/victimization context (i.e., same-sex aggressor -victim versus other-sex aggressor- victim dyad). The second goal was to examine how electronic victimization and aggression were associated with self-esteem and relationship efficacy. Participants were 826 (49.9% female) 7th and 8th grade students (M age = 12.5 years old; SD = .67). Students were administered surveys during school hours. Results indicated that girls were more likely to be involved in both electronic aggression and victimization than boys. Further, girls were more likely to be both electronic aggressors and victims simultaneously than boys. Finally, those involved with electronic aggression reported higher levels of relationship efficacy than their peers and involvement as an aggressor/victim was associated with lower self-esteem than any other involvement category.
ContributorsMartin, Melissa (Author) / Updegraff, Kimberly A (Thesis advisor) / Ladd, Becky (Committee member) / Martin, Carol (Committee member) / Arizona State University (Publisher)
Created2013
Description
Cyberbullying is a phenomenon which negatively affects individuals. Victims of the cyberbullying suffer from a range of mental issues, ranging from depression to low self-esteem. Due to the advent of the social media platforms, cyberbullying is becoming more and more prevalent. Traditional mechanisms to fight against cyberbullying include use of standards and guidelines, human moderators, use of blacklists based on profane words, and regular expressions to manually detect cyberbullying. However, these mechanisms fall short in social media and do not scale well. Users in social media use intentional evasive expressions like, obfuscation of abusive words, which necessitates the development of a sophisticated learning framework to automatically detect new cyberbullying behaviors. Cyberbullying detection in social media is a challenging task due to short, noisy and unstructured content and intentional obfuscation of the abusive words or phrases by social media users. Motivated by sociological and psychological findings on bullying behavior and its correlation with emotions, we propose to leverage the sentiment information to accurately detect cyberbullying behavior in social media by proposing an effective optimization framework. Experimental results on two real-world social media datasets show the superiority of the proposed framework. Further studies validate the effectiveness of leveraging sentiment information for cyberbullying detection.
ContributorsDani, Harsh (Author) / Liu, Huan (Thesis advisor) / Tong, Hanghang (Committee member) / He, Jingrui (Committee member) / Arizona State University (Publisher)
Created2017