Distributional similarity model for multi-modality clustering in social media

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

User generated content (UGC) has become the fastest growing sector of the WWW. Data mining from UGC presents challenges not typically found in text mining from documents. UGC can be semi-structured and its content can be very short and informal, containing relatively little content similar to a chat or an email conversation. In addition UGC can be viewed as a multi-modality data. These characteristics pose big challenges and research questions for scholars to cope with. To cluster UGC data, we can construct multiple contingency tables of modalities and employ the multi-way distributional clustering (MDC) algorithm. However, by considering a contingency table which summarizes the co-occurrence statistics of two modalities, it is not robust to represent the information entropy between two modalities in UGC data. In this paper, we propose a novel similarity measurement, called distributional similarity model (DSM), to solidify the graph model in the MDC algorithm to deal with the unique characteristics of the UGC data.
Original languageEnglish
Title of host publication2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology Workshops, 5-12 November 2007, Silicon Valley, CA
PublisherIEEE
Pages268-271
Number of pages4
ISBN (Print)0769530281
DOIs
Publication statusPublished - 2007
EventIEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology [WI-IAT] -
Duration: 1 Jan 2007 → …

Conference

ConferenceIEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology [WI-IAT]
Period1/01/07 → …

Keywords

  • Social Media AnalysisMulti-Modality ClusteringDistributional Features

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