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]
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]