#bully: Uses of Hashtags in Posts About Bullying on Twitter

Journal of School Violence

Volumn 14, Issue 1, 2015, Pages 133-153

  Calvin, Angela J ^a   Bellmore, Amy ^a   Xu, Jun Ming ^b   Zhu, Xiaojin ^b  

^a UNIVERSITY OF WISCONSIN MADISON   (United States)

^b University of Wisconsin Madison   (United States)

Author keywords

bullying; hashtags; social media; Twitter

Indexed keywords

EID: 84912522665     PISSN: 15388220     EISSN: 15388239     Source Type: Journal    
DOI: 10.1080/15388220.2014.966828     Document Type: Article

References (29)

1. Bishop, C. M. (2006). Pattern recognition and machine learning. New York, NY: Springer-Verlag.
2. Bosse, T., & Stam, S. (2011). A normative agent system to prevent cyberbullying. WI-IAT 2011, 425–430. doi:10.1109/WI-IAT.2011.24
3. Boyd, D., Golder, S., & Lotan, G. (2010, January). Tweet, tweet, retweet: Conversational aspects of retweeting on Twitter. HICSS-43. IEEE: Kauai, HI. doi:10.1109/HICSS.2010.412
4. Carnegie Mellon University. (2014, October 1). #OriginStory. Retrieved from http://www.cmu.edu/homepage/computing/2014/summer/originstory.shtml
5. Gleason, B. (2013). #Occupy Wall Street: Exploring informal learning about a social movement on Twitter. American Behavioral Scientist, 57, 966–982. doi:10.1177/0002764213479372
6. Gonzales, A. L., & Hancock, J. T. (2011). Mirror, mirror on my Facebook wall: Effects of exposure to Facebook on self-esteem. Cyberpsychology, Behavior, and Social Networking, 14, 79–83. doi:10.1089/cyber.2009.0411
7. Hancock, J. T., & Dunham, P. J. (2001). Impression formation in computer-mediated communication revisited. Communication Research, 28, 325–347. doi:10.1177/009365001028003004
8. Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction (2nd ed.). New York, NY: Springer-Verlag.
9. Huang, J., Thornton, K. M., & Efthimiadis, E. N. (2010). Conversational tagging on Twitter. In Proceedings of the 21st ACM Conference on Hypertext and Hypermedia (HT’10; pp. 173–178). New York, NY. doi:10.1145/1810617.1810647
10. Hughes, S. A. (2011). Jamey Rodemeyer, bullied teen who made ‘It Gets Better’ video, commits suicide. The Washington Post. Retrieved from http://www.washingtonpost.com/blogs/worldviews/post/jamey-rodemeyer-bullied-teen-who-made-it-gets-better-video-commits-suicide/2011/09/21/gIQAVVzxkK_blog.html
11. Katrandjian, O. (2011). Jamey Rodemeyer suicide: Lady Gaga pays tribute to bullying victim. ABC News. Retrieved from http://abcnews.go.com/blogs/entertainment/2011/09/jamey-rodemeyer-suicide-lady-gaga-pays-tribute-to-bullying-victim/
12. Kirkland, P. (2014). Can Twitter activism #bringbackourgirls? The Washington Post. Retrieved from http://www.washingtonpost.com/blogs/she-the-people/wp/2014/07/23/can-twitter-activism-bringbackourgirls/
13. Kontostathis, A., Edwards, L., & Leatherman, A. (2010). Text mining and cybercrime. In M. W. Berry & J. Kogan (Eds.), Text mining: Applications and theory (pp. 1–14). Chichester, UK: Wiley.
14. Kowalski, R. M., Giumetti, G. W., Schroeder, A. N., & Lattanner, M. R. (2014). Bullying in the digital age: A critical review and meta-analysis of cyberbullying research among youth. Psychological Bulletin, 140, 1073–1137. doi:10.1037/a0035618
15. Latham, A., Crockett, K., & Bandar, Z. (2010). A conversation expert system supporting bullying and harassment policies. In The 2nd ICAART Proceedings (pp. 163–168). Valencia, Spain: doi:10.5220/0002697801630168
16. Lehmann, J., Gonçalves, B., Ramasco, J. J., & Cattuto, C. (2012). Dynamic classes of collective attention in Twitter. In Proceedings of the 21st ACM International World Wide Web Conference (pp. 251–260). New York, NY: ACM. Retrieved from http://dl.acm.org/citation.cfm?id=2187836&picked=prox&preflayout=tabs
17. Liu, B. (2012). Opinion mining and sentiment analysis. Synthesis Lectures on Human Language Technologies, 5, 1–167. doi:10.1007/978-3-642-19460-3_11
18. Lovejoy, K., & Saxton, G. D. (2012). Information, community, and action: How nonprofit organizations use social media. Journal of Computer-Mediated Communication, 17, 337–353. doi:10.1111/j.1083-6101.2012.01576.x
19. Macbeth, J., Adeyema, H., Lieberman, H., & Fry, C. (2013). Script-based story matching for cyberbullying prevention. In CHI ‘13 extended abstracts on human factors in computing systems (pp. 901–906). New York, NY: ACM. Retrieved from http://doi.acm.org/10.1145/2468356.2468517
20. Miles, M., & Huberman, A. M. (1994). Qualitative data analysis: An expanded sourcebook. Beverly Hills, CA: Sage.
21. Mitchell, A., Holcomb, J., & Page, D. (2013, November 14). News use across social media platforms. Pew Research Center. Retrieved from http://www.journalism.org/2013/11/14/news-use-across-social-media-platforms/
22. Mitchell, A., & Page, D. (2013, November 4). Twitter news consumers: Young, mobile, and educated. Pew Research Center. Retrieved from http://www.journalism.org/2013/11/04/twitter-news-consumers-young-mobile-and-educated/
23. Pappacharissi, Z., & La Fatima Oliveira, M. (2012). Affective news and networked publics: The rhythms of news storytelling on #Egypt. Journal of Communication, 62, 266–282. doi:10.1111/j.1460-2466.2012.01630.x
24. Ptaszynski, M., Dybala, P., Matsuba, T., Masui, F., Rzepka, R., & Araki, K. (2010). Machine learning and affect analysis against cyber-bullying. In Proceedings of 36th annual convention of the Society for the Study of Artificial Intelligence and the Simulation of Behaviour (pp. 7–16). Leicester, UK: Society for the Study of Artificial Intelligence & Simulation of Behaviour. Retrieved from http://www.proceedings.com/21426.html
25. Thelwall, M., Buckley, K., Paltoglou, G., Cai, D., & Kappas, A. (2010). Sentiment strength detection in short informal text. Journal of the American Society for Information Science and Technology, 61, 2544–2558. doi:10.1002/asi.21416
26. Wasserman, L. (2003). All of statistics: A concise course in statistical inference. New York, NY: Springer.
27. Xu, J.-M., Jun, K.-S., Zhu, X., & Bellmore, A. (2012). Learning from bullying traces in social media. In Proceedings of the conference of the North American Chapter of the Association for Computational Linguistics: Human language technologies (pp. 656–666). Montreal, Quebec, Canada: Association for Computational Linguistics. Retrieved from http://www.proceedings.com/15547.html
28. Xu, J.-M., Zhu, X., & Bellmore, A. (2012). Fast learning for sentiment analysis on bullying. In Proceedings of the first international workshop on issues of sentiment discovery and opinion mining (WISDOM 12ʹ; pp. 1–6). New York, NY: ACM. Retrieved from http://dl.acm.org/citation.cfm?id=2346676
29. Zhu, X., & Goldberg, A. B. (2009). Introduction to semi-supervised learning: Synthesis Lectures on Artificial Intelligence and Machine Learning. San Rafael, CA: Morgan & Claypool.