You Too?! Mixed-Initiative LDA Story Matching to Help Teens in Distress
Name
Picard_You too.pdf
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528.93 KB
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Checksum (MD5)
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Author(s)
Dinakar, Karthik
Jones, Birago
Picard, Rosalind W.
Rose, Carolyn
Thoman, Matthew
Reichart, Roi
Lieberman, Henry A
Date Issued
June 2012
Journal
Proceedings of the Sixth International AAAI Conference on Weblogs and Social Media (ICWSM 2012)
Publisher
Association for the Advancement of Artificial Intelligence
Citation
Dinakar, Karthik, et al. "You Too?! Mixed-Initiative LDA Story Matching to Help Teens in Distress." Proceedings of the Sixth International AAAI Conference on Weblogs and Social Media (ICWSM 2012).
Version
Author's final manuscript
Abstract
Adolescent cyber-bullying on social networks is a phenomenon that has received widespread attention. Recent work by sociologists has examined this phenomenon under the larger context of teenage drama and it's manifestations on social networks. Tackling cyber-bullying involves two
key components – automatic detection of possible cases, and interaction strategies that encourage reflection and emotional support. Key is showing distressed teenagers that they are not alone in their plight. Conventional topic spotting and document classification into labels like "dating" or "sports" are not enough to effectively match stories for this task. In this work, we examine a corpus of 5500 stories from distressed teenagers from a major youth social network. We combine Latent Dirichlet Allocation and human interpretation of its output using principles from sociolinguistics to extract high-level themes in the stories and use them to match new stories to similar ones. A user evaluation of the story matching shows that theme-based retrieval does a better job of finding relevant and effective stories for this application than conventional approaches.
MIT Department
Massachusetts Institute of Technology. Media Laboratory
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
http://www.aaai.org/ocs/index.php/ICWSM/ICWSM12/paper/view/4604/4969
