Detecting Threats of Violence in Online Discussions Using Bigrams of Important Words
Chapter, Peer reviewed
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Original versionHammer, H. L. (2014, September). Detecting Threats of Violence in Online Discussions Using Bigrams of Important Words. In Intelligence and Security Informatics Conference (JISIC), 2014 IEEE Joint (pp. 319-319). IEEE. http://dx.doi.org/10.1109/JISIC.2014.64
Making violent threats towards minorities like immigrants or homosexuals is increasingly common on the Internet. We present a method to automatically detect threats of violence using machine learning. A material of 24,840 sentences from YouTube was manually annotated as violent threats or not, and was used to train and test the machine learning model. Detecting threats of violence works quit well with an error of classifying a violent sentence as not violent of about 10% when the error of classifying a non-violent sentence as violent is adjusted to 5%. The best classification performance is achieved by including features that combine specially chosen important words and the distance between those in the sentence.