Content-based prediction of temporal boundaries for events in Twitter

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dc.contributor.authorIyengar, Akshaya
dc.contributor.authorFinin, Tim
dc.contributor.authorJoshi, Anupam
dc.date.accessioned2018-11-15T15:27:58Z
dc.date.available2018-11-15T15:27:58Z
dc.date.issued2011-10-09
dc.descriptionProceedings of the Third IEEE International Conference on Social Computingen_US
dc.description.abstractSocial 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_US
dc.description.sponsorshipThis 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_US
dc.description.urihttps://ieeexplore.ieee.org/document/6113113en_US
dc.format.extent6 pagesen_US
dc.genreconference papers and proceedings preprintsen_US
dc.identifierdoi:10.13016/M21G0HZ7X
dc.identifier.citationAkshaya 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.2011.196en_US
dc.identifier.uri10.1109/PASSAT/SocialCom.2011.196
dc.identifier.urihttp://hdl.handle.net/11603/11995
dc.language.isoen_USen_US
dc.publisherIEEEen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.rightsThis 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.subjectsocial mediaen_US
dc.subjectContent-based predictionen_US
dc.subjectTwitteren_US
dc.subjecttemporal boundariesen_US
dc.subjectSVM classifieren_US
dc.subjecthidden Markov modelen_US
dc.subjectUMBC Ebiquity Research Groupen_US
dc.titleContent-based prediction of temporal boundaries for events in Twitteren_US
dc.typeTexten_US

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