You Too?! Mixed-Initiative LDA Story Matching to Help Teens in Distress
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 component...
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| Format: | Article |
| Language: | en_US |
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Association for the Advancement of Artificial Intelligence
2014
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| Online Access: | http://hdl.handle.net/1721.1/92440 https://orcid.org/0000-0001-7482-0237 https://orcid.org/0000-0002-5661-0022 |
| _version_ | 1826207802530463744 |
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| author | Dinakar, Karthik Jones, Birago Picard, Rosalind W. Rose, Carolyn Thoman, Matthew Reichart, Roi Lieberman, Henry A |
| author2 | Massachusetts Institute of Technology. Media Laboratory |
| author_facet | Massachusetts Institute of Technology. Media Laboratory Dinakar, Karthik Jones, Birago Picard, Rosalind W. Rose, Carolyn Thoman, Matthew Reichart, Roi Lieberman, Henry A |
| author_sort | Dinakar, Karthik |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T13:55:11Z |
| format | Article |
| id | mit-1721.1/92440 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T13:55:11Z |
| publishDate | 2014 |
| publisher | Association for the Advancement of Artificial Intelligence |
| record_format | dspace |
| spelling | mit-1721.1/924402022-10-01T17:58:56Z You Too?! Mixed-Initiative LDA Story Matching to Help Teens in Distress Dinakar, Karthik Jones, Birago Picard, Rosalind W. Rose, Carolyn Thoman, Matthew Reichart, Roi Lieberman, Henry A Massachusetts Institute of Technology. Media Laboratory Program in Media Arts and Sciences (Massachusetts Institute of Technology) Dinakar, Karthik Jones, Birago Lieberman, Henry A. Picard, Rosalind W. Reichart, Roi 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. 2014-12-22T18:20:57Z 2014-12-22T18:20:57Z 2012-06 Article http://purl.org/eprint/type/ConferencePaper http://hdl.handle.net/1721.1/92440 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). https://orcid.org/0000-0001-7482-0237 https://orcid.org/0000-0002-5661-0022 en_US http://www.aaai.org/ocs/index.php/ICWSM/ICWSM12/paper/view/4604/4969 Proceedings of the Sixth International AAAI Conference on Weblogs and Social Media (ICWSM 2012) Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Association for the Advancement of Artificial Intelligence MIT web domain |
| spellingShingle | Dinakar, Karthik Jones, Birago Picard, Rosalind W. Rose, Carolyn Thoman, Matthew Reichart, Roi Lieberman, Henry A You Too?! Mixed-Initiative LDA Story Matching to Help Teens in Distress |
| title | You Too?! Mixed-Initiative LDA Story Matching to Help Teens in Distress |
| title_full | You Too?! Mixed-Initiative LDA Story Matching to Help Teens in Distress |
| title_fullStr | You Too?! Mixed-Initiative LDA Story Matching to Help Teens in Distress |
| title_full_unstemmed | You Too?! Mixed-Initiative LDA Story Matching to Help Teens in Distress |
| title_short | You Too?! Mixed-Initiative LDA Story Matching to Help Teens in Distress |
| title_sort | you too mixed initiative lda story matching to help teens in distress |
| url | http://hdl.handle.net/1721.1/92440 https://orcid.org/0000-0001-7482-0237 https://orcid.org/0000-0002-5661-0022 |
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