Content-based prediction of temporal boundaries for events in Twitter
| dc.contributor.author | Iyengar, Akshaya | |
| dc.contributor.author | Finin, Tim | |
| dc.contributor.author | Joshi, Anupam | |
| dc.date.accessioned | 2018-11-15T15:27:58Z | |
| dc.date.available | 2018-11-15T15:27:58Z | |
| dc.date.issued | 2011-10-09 | |
| dc.description | Proceedings of the Third IEEE International Conference on Social Computing | en |
| dc.description.abstract | Social media services like Twitter, Flickr and YouTube publish high volumes of user generated content as a major event occurs, making them a potential data source for event analysis. The large volume and noisy content of social media makes automatic preprocessing essential. Intuitively, the eventrelated data falls into three major phases: the buildup to the event, the event itself, and the post-event effects and repercussions. We describe an approach to automatically determine when an anticipated event started and ended by analyzing the content of tweets using an SVM classifier and hidden Markov model. We evaluate our performance by predicting event boundaries on Twitter data for a set of events in the domains of sports, weather and social activities. | en |
| dc.description.sponsorship | This work was done with partial support from the Office of Naval Research. We thank Ross Pokorny and Will Murnane for Tweet Collector and Professor Tim Oates for machine learning advice. | en |
| dc.description.uri | https://ieeexplore.ieee.org/document/6113113 | en |
| dc.format.extent | 6 pages | en |
| dc.genre | conference papers and proceedings preprints | en |
| dc.identifier | doi:10.13016/M21G0HZ7X | |
| dc.identifier.citation | Akshaya Iyengar, Tim Finin, and Anupam Joshi, Content-based prediction of temporal boundaries for events in Twitter, Proceedings of the Third IEEE International Conference on Social Computing, 2011, DOI: 10.1109/PASSAT/SocialCom.2011.196 | en |
| dc.identifier.uri | 10.1109/PASSAT/SocialCom.2011.196 | |
| dc.identifier.uri | http://hdl.handle.net/11603/11995 | |
| dc.language.iso | en | en |
| dc.publisher | IEEE | en |
| dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
| dc.relation.ispartof | UMBC Computer Science and Electrical Engineering Department Collection | |
| dc.relation.ispartof | UMBC Faculty Collection | |
| dc.rights | This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author. | |
| dc.rights | © 2011 IEEE | |
| dc.subject | social media | en |
| dc.subject | Content-based prediction | en |
| dc.subject | en | |
| dc.subject | temporal boundaries | en |
| dc.subject | SVM classifier | en |
| dc.subject | hidden Markov model | en |
| dc.subject | UMBC Ebiquity Research Group | en |
| dc.title | Content-based prediction of temporal boundaries for events in Twitter | en |
| dc.type | Text | en |