Journal
COMPUTERS IN HUMAN BEHAVIOR
Volume 44, Issue -, Pages 305-314Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.chb.2014.11.052
Keywords
Bullying; Twitter; Social media; Machine learning; Computer science
Funding
- Div Of Information & Intelligent Systems
- Direct For Computer & Info Scie & Enginr [1216758] Funding Source: National Science Foundation
Ask authors/readers for more resources
This paper explores the utility of machine learning methods for understanding bullying, a significant social-psychological issue in the United States, through social media data. Machine learning methods were applied to all public mentions of bullying on Twitter between September 1, 2011 and August 31, 2013 to extract the posts that referred to discrete bullying episodes (N = 9,764,583) to address five key questions. Most posts were authored by victims and reporters and referred to general forms of bullying. Posts frequently reflected self-disclosure about personal involvement in bullying. The number of posts that originated from a state was positively associated with the state population size; the timing of the posts reveal that more posts were made on weekdays than on Saturdays and more posts were made during the evening compared to daytime hours. Potential benefits of merging social science and computer science methods to enhance the study of bullying are discussed. (C) 2014 Elsevier Ltd. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available