- Felice Resnik, Amy Bellmore, Junming Xu, and Xiaojin Zhu. Celebrities emerge as advocates in tweets about bullying. In Translational Issues in Psychological Science. 2016.
[pdf ] - Understanding and Fighting Bullying with Machine Learning
Junming Sui.
PhD thesis, Department of Computer Sciences, University of Wisconsin-Madison. 2015.
[pdf ] - How Youth Assign Bullying Roles to Celebrities in Social Media
Felice Resnik, Angela Calvin, Hsun-Chih Huang, Jun-Ming Xu, Kwang-Sung Jun, Amy Bellmore, Xiaojin Zhu.
Poster at the biennial meeting of the Society for Research in Child Development. Philadelphia, Pennsylvania, USA. 2015 - Amy Bellmore, Angela Calvin, Jun-Ming Xu, and Xiaojin Zhu. The five W’s of bullying on Twitter: Who, what, why, where, when. In Computers in Human Behavior. 2014. Accepted.
- Angela J. Calvin, Amy Bellmore, Jun-Ming Xu, and Xiaojin Zhu. #bully: Uses of Hashtags in Posts about Bullying on Twitter. In Journal of School Violence. 2014. Accepted.
- School Bullying in Twitter and Weibo: a Comparative Study.
Jun-Ming Xu, Hsun-Chih Huang, Amy Bellmore, and Xiaojin Zhu.
In the Eighth International AAAI Conference on Weblogs and Social Media (ICWSM). 2014.
[pdf ] - An examination of regret in bullying tweets.
Junming Xu, Benjamin Burchfiel, Xiaojin Zhu, and Amy Bellmore.
In North American Chapter of the Association for Computational Linguistics – Human Language Technologies (NAACL HLT). short paper, 2013.
[pdf | slides ] - Using Social Media Data to Distinguish Bullying from Teasing
Huang H.C. Xu, J. Kwang-Sun, J. Bellmore, A. and Zhu, X.
Poster at the biennial meeting of the Society for Research in Child Development. Seattle, WA. 2013
[pdf ] - Fast learning for sentiment analysis on bullying
Jun-Ming Xu, Xiaojin Zhu, and Amy Bellmore
In ACM KDD Workshop on Issues of Sentiment Discovery and Opinion Mining (WISDOM) 2012.
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.
[pdf | slides ]
Jun-Ming Xu, Kwang-Sung Jun, Xiaojin Zhu, and Amy Bellmore
In North American Chapter of the Association for Computational Linguistics – Human Language Technologies (NAACL HLT) 2012.
We introduce the social study of bullying to the NLP community. Bullying, in both physical and cyber worlds (the latter known as cyberbullying), has been recognized as a serious national health issue among adolescents. However, previous social studies of bullying are handicapped by data scarcity, while the few computational studies narrowly restrict themselves to cyberbullying which accounts for only a small fraction of all bullying episodes. Our main contribution is to present evidence that social media, with appropriate natural language processing techniques, can be a valuable and abundant data source for the study of bullying in both worlds. We identify several key problems in using such data sources and formulate them as NLP tasks, including text classification, role labeling, sentiment analysis, and topic modeling. Since this is an introductory paper, we present baseline results on these tasks using off-the-shelf NLP solutions, and encourage the NLP community to contribute better models in the future.
[paper | slides ]
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