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Microblogging Temporal Summarization: Filtering Important Twitter Updates for Breaking News

dc.contributor.advisorOard, Douglas Wen_US
dc.contributor.authorXu, Tanen_US
dc.date.accessioned2016-06-22T05:34:40Z
dc.date.available2016-06-22T05:34:40Z
dc.date.issued2015en_US
dc.identifierhttps://doi.org/10.13016/M2Q777
dc.identifier.urihttp://hdl.handle.net/1903/18139
dc.description.abstractWhile news stories are an important traditional medium to broadcast and consume news, microblogging has recently emerged as a place where people can dis- cuss, disseminate, collect or report information about news. However, the massive information in the microblogosphere makes it hard for readers to keep up with these real-time updates. This is especially a problem when it comes to breaking news, where people are more eager to know “what is happening”. Therefore, this dis- sertation is intended as an exploratory effort to investigate computational methods to augment human effort when monitoring the development of breaking news on a given topic from a microblog stream by extractively summarizing the updates in a timely manner. More specifically, given an interest in a topic, either entered as a query or presented as an initial news report, a microblog temporal summarization system is proposed to filter microblog posts from a stream with three primary concerns: topical relevance, novelty, and salience. Considering the relatively high arrival rate of microblog streams, a cascade framework consisting of three stages is proposed to progressively reduce quantity of posts. For each step in the cascade, this dissertation studies methods that improve over current baselines. In the relevance filtering stage, query and document expansion techniques are applied to mitigate sparsity and vocabulary mismatch issues. The use of word embedding as a basis for filtering is also explored, using unsupervised and supervised modeling to characterize lexical and semantic similarity. In the novelty filtering stage, several statistical ways of characterizing novelty are investigated and ensemble learning techniques are used to integrate results from these diverse techniques. These results are compared with a baseline clustering approach using both standard and delay-discounted measures. In the salience filtering stage, because of the real-time prediction requirement a method of learning verb phrase usage from past relevant news reports is used in conjunction with some standard measures for characterizing writing quality. Following a Cranfield-like evaluation paradigm, this dissertation includes a se- ries of experiments to evaluate the proposed methods for each step, and for the end- to-end system. New microblog novelty and salience judgments are created, building on existing relevance judgments from the TREC Microblog track. The results point to future research directions at the intersection of social media, computational jour- nalism, information retrieval, automatic summarization, and machine learning.en_US
dc.language.isoenen_US
dc.titleMicroblogging Temporal Summarization: Filtering Important Twitter Updates for Breaking Newsen_US
dc.typeDissertationen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.contributor.departmentLibrary & Information Servicesen_US
dc.subject.pqcontrolledInformation scienceen_US
dc.subject.pquncontrolledInformation Filteringen_US
dc.subject.pquncontrolledMachine Learningen_US
dc.subject.pquncontrolledMicroblogen_US
dc.subject.pquncontrolledSocial Mediaen_US
dc.subject.pquncontrolledTemporal Summarizationen_US


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