A weakly-supervised Bayesian model for violence detection from social media

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Abstract

Social streams have proven to be the mostup-to-date and inclusive information on cur-rent events. In this paper we propose a novelprobabilistic modelling framework, called violence detection model (VDM), which enables the identification of text containing violent content and extraction of violence-related topics over social media data. The proposed VDM model does not require any labeled corpora for training, instead, it only needs the in-corporation of word prior knowledge which captures whether a word indicates violence or not. We propose a novel approach of deriving word prior knowledge using the relative entropy measurement of words based on the in-tuition that low entropy words are indicative of semantically coherent topics and therefore more informative, while high entropy words indicates words whose usage is more topical diverse and therefore less informative. Our proposed VDM model has been evaluated on the TREC Microblog 2011 dataset to identify topics related to violence. Experimental results show that deriving word priors using our proposed relative entropy method is more effective than the widely-used information gain method. Moreover, VDM gives higher violence classification results and produces more coherent violence-related topics compared toa few competitive baselines.

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Publication date2013
Publication titleThe 6th International Joint Conference on Natural Language Processing (IJCNLP)
Place of PublicationNagoya (JP)
Pages109-117
Number of pages9
Original languageEnglish
Event6th International Joint Conference on Natural Language Processing - Nagoya, Japan

Conference

Conference6th International Joint Conference on Natural Language Processing
Abbreviated titleIJCNLP 2013
CountryJapan
CityNagoya
Period14/10/1318/10/13

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