Let's Gang Up on Cyberbullying

Lieberman, Henry; Dinakar, Karthik; Jones, Birago

doi:10.1109/mc.2011.286

Cited by 25 publications

(6 citation statements)

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“…The social science study of bullying has a long history but is limited by small sample size and time-consuming data collection [28]. The computer science study of bullying is emerging with promising results [43,22,10,33,20,5,21]. The latter has a distinct focus on bullying in social media, where participants of a bullying episode post their bullying experiences.…”

Section: Introduction To Bullyingmentioning

confidence: 99%

Fast learning for sentiment analysis on bullying

Zhu

Bellmore

2012

Proceedings of the First International Workshop on Issues of Sentiment Discovery and Opinion Mining

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Bullying is a serious national health issue among adolescents. Social media offers a new opportunity to study bullying in both physical and cyber worlds. Sentiment analysis has the potential to identify victims who pose high risk to themselves or others, and to enhance the scientific understanding of bullying overall. We identify seven emotions common in bullying. While some of the emotions are well-studied before, others are non-standard in the sentiment analysis literature. We propose a fast training procedure to recognize these emotions without explicitly producing a conventional labeled training dataset. We apply our procedure to social media posts on bullying and discuss our findings.

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Section: Introduction To Bullyingmentioning

confidence: 99%

Fast learning for sentiment analysis on bullying

Zhu

Bellmore

2012

Proceedings of the First International Workshop on Issues of Sentiment Discovery and Opinion Mining

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“…Regarding the use of automatic methods to detect dangerous behavior in online communities, previous research has focused on cyber-bullying and not on physical bullying as we do. For example, (Lieberman, Dinakar, and Jones 2011) focuses exclusively on observable behavior (e.g., finding insulting or racist messages on status updates to online social networks), whereas our approach is aimed at finding the latent behavior associated with children's roles within the classroom, which may or may not be explicit in the game.…”

Section: Related Workmentioning

confidence: 99%

Identifying Bullies with a Computer Game

Mancilla-Caceres

Amir

et al. 2021

AAAI

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Current computer involvement in adolescent social networks (youth between the ages of 11 and 17) provides new opportunities to study group dynamics, interactions amongst peers, and individual preferences. Nevertheless, most of the research in this area focuses on efficiently retrieving information that is explicit in large social networks (e.g., properties of the graph structure), but not on how to use the dynamics of the virtual social network to discover latent characteristics of the real-world social network. In this paper, we present the analysis of a game designed to take advantage of the familiarity of adolescents with online social networks, and describe how the data generated by the game can be used to identify bullies in 5th grade classrooms. We present a probabilistic model of the game and using the in-game interactions of the players (i.e., content of chat messages) infer their social role within their classroom (either a bully or non-bully). The evaluation of our model is done by using previously collected data from psychological surveys on the same 5th grade population and by comparing the performance of the new model with off-the-shelf classifiers.

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“…(5) 'Positive' and 'negative' words. Positive or negative tone of a message is considered as a language feature effective for cyberbullying detection [35]. Many cyberbullying detection studies claim that cyberbullying contents include negative words [5].…”

Section: Textual Featuresmentioning

confidence: 99%

A Socio-contextual Approach in Automated Detection of Cyberbullying

Tahmasbi

Rastegari

2018

Proceedings of the 51st Hawaii International Conference on System Sciences

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Cyberbullying is a major cyber issue that is common among adolescents. Recent reports show that more than one out of five students in the United States is a victim of cyberbullying. Majority of cyberbullying incidents occur on public social media platforms such as Twitter. Automated cyberbullying detection methods can help prevent cyberbullying before the harm is done on the victim. In this study, we analyze a corpus of cyberbullying Tweets to construct an automated detection model. Our method emphasizes on the two claims that are supported by our results. First, despite other approaches that assume that cyberbullying instances use vulgar or profane words, we show that they do not necessarily contain negative words. Second, we highlight the importance of context and the characteristics of actors involved and their position in the network structure in detecting cyberbullying rather than only considering the textual content in our analysis.

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Let's Gang Up on Cyberbullying

Cited by 25 publications

References 0 publications

Fast learning for sentiment analysis on bullying

Fast learning for sentiment analysis on bullying

Identifying Bullies with a Computer Game

A Socio-contextual Approach in Automated Detection of Cyberbullying

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