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dc.contributor.authorDinakar, Karthik
dc.contributor.authorJones, Birago
dc.contributor.authorHavasi, Catherine
dc.contributor.authorLieberman, Henry A.
dc.contributor.authorPicard, Rosalind W.
dc.date.accessioned2013-08-21T17:04:31Z
dc.date.available2013-08-21T17:04:31Z
dc.date.issued2012-09
dc.date.submitted2011-12
dc.identifier.issn21606455
dc.identifier.urihttp://hdl.handle.net/1721.1/79894
dc.description.abstractCyberbullying (harassment on social networks) is widely recognized as a serious social problem, especially for adolescents. It is as much a threat to the viability of online social networks for youth today as spam once was to email in the early days of the Internet. Current work to tackle this problem has involved social and psychological studies on its prevalence as well as its negative effects on adolescents. While true solutions rest on teaching youth to have healthy personal relationships, few have considered innovative design of social network software as a tool for mitigating this problem. Mitigating cyberbullying involves two key components: robust techniques for effective detection and reflective user interfaces that encourage users to reflect upon their behavior and their choices. Spam filters have been successful by applying statistical approaches like Bayesian networks and hidden Markov models. They can, like Google’s GMail, aggregate human spam judgments because spam is sent nearly identically to many people. Bullying is more personalized, varied, and contextual. In this work, we present an approach for bullying detection based on state-of-the-art natural language processing and a common sense knowledge base, which permits recognition over a broad spectrum of topics in everyday life. We analyze a more narrow range of particular subject matter associated with bullying (e.g. appearance, intelligence, racial and ethnic slurs, social acceptance, and rejection), and construct BullySpace, a common sense knowledge base that encodes particular knowledge about bullying situations. We then perform joint reasoning with common sense knowledge about a wide range of everyday life topics. We analyze messages using our novel AnalogySpace common sense reasoning technique. We also take into account social network analysis and other factors. We evaluate the model on real-world instances that have been reported by users on Formspring, a social networking website that is popular with teenagers. On the intervention side, we explore a set of reflective user-interaction paradigms with the goal of promoting empathy among social network participants. We propose an “air traffic control”-like dashboard, which alerts moderators to large-scale outbreaks that appear to be escalating or spreading and helps them prioritize the current deluge of user complaints. For potential victims, we provide educational material that informs them about how to cope with the situation, and connects them with emotional support from others. A user evaluation shows that in-context, targeted, and dynamic help during cyberbullying situations fosters end-user reflection that promotes better coping strategies.en_US
dc.language.isoen_US
dc.publisherAssociation for Computing Machinery (ACM)en_US
dc.relation.isversionofhttp://dx.doi.org/10.1145/2362394.2362400en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alike 3.0en_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/en_US
dc.sourceMIT Web Domainen_US
dc.titleCommonsense Reasoning for Detection, Prevention, and Mitigation of Cyberbullyingen_US
dc.title.alternativeCommon Sense Reasoning for Detection, Prevention, and Mitigation of Cyberbullyingen_US
dc.typeArticleen_US
dc.identifier.citationKarthik Dinakar, Birago Jones, Catherine Havasi, Henry Lieberman, and Rosalind Picard. 2012. Common Sense Reasoning for Detection, Prevention, and Mitigation of Cyberbullying. ACM Trans. Interact. Intell. Syst. 2, 3, Article 18 (September 2012), 30 pages.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Media Laboratoryen_US
dc.contributor.departmentProgram in Media Arts and Sciences (Massachusetts Institute of Technology)en_US
dc.contributor.mitauthorDinakar, Karthiken_US
dc.contributor.mitauthorJones, Biragoen_US
dc.contributor.mitauthorHavasi, Catherineen_US
dc.contributor.mitauthorLieberman, Henry A.en_US
dc.contributor.mitauthorPicard, Rosalind W.en_US
dc.relation.journalACM Transactions on Interactive Intelligent Systemsen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dspace.orderedauthorsDinakar, Karthik; Jones, Birago; Havasi, Catherine; Lieberman, Henry; Picard, Rosalinden_US
dc.identifier.orcidhttps://orcid.org/0000-0001-7482-0237
dc.identifier.orcidhttps://orcid.org/0000-0002-5661-0022
dc.identifier.orcidhttps://orcid.org/0000-0002-5701-9824
mit.licenseOPEN_ACCESS_POLICYen_US


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