Graduate School and Research Center in Digital Sciences

Social event discovery by topic inference

Liu, Xueliang; Huet, Benoit

WIAMIS 2012, 13th International Workshop on Image Analysis for Multimedia Interactive Services, 23-25 May 2012, Dublin City University, Ireland

With the keen interest of people for social media sharing websites the multimedia research community faces new challenges and compelling opportunities. In this paper, we address the problem of discovering specific events from social media data automatically. Our proposed approach assumes that events are conjoint distribution over the latent topics in a given place. Based on this assumption, topics are learned from large amounts of automatically collected social data using a LDA model. Then, event distribution estimation over a topic is solved using least mean square optimization. We evaluate our methods on locations scattered around the world and show via our experimental results that the proposed framework offers promising performance for detecting events based on social media.

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Title:Social event discovery by topic inference
Department:Data Science
Eurecom ref:3699
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Bibtex: @inproceedings{EURECOM+3699, doi = {}, year = {2012}, title = {{S}ocial event discovery by topic inference }, author = {{L}iu, {X}ueliang and {H}uet, {B}enoit}, booktitle = {{WIAMIS} 2012, 13th {I}nternational {W}orkshop on {I}mage {A}nalysis for {M}ultimedia {I}nteractive {S}ervices, 23-25 {M}ay 2012, {D}ublin {C}ity {U}niversity, {I}reland}, address = {{D}ublin, {IRELAND}}, month = {05}, url = {} }
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