{"id":148324,"date":"1997-01-01T00:00:00","date_gmt":"1997-01-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/smokey-automatic-recognition-of-hostile-messages\/"},"modified":"2018-10-16T21:03:56","modified_gmt":"2018-10-17T04:03:56","slug":"smokey-automatic-recognition-of-hostile-messages","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/smokey-automatic-recognition-of-hostile-messages\/","title":{"rendered":"Smokey: Automatic Recognition of Hostile Messages"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Abusive messages (flames) can be both a source of frustration and a waste of time for Internet users. This paper describes some approaches to flame recognition, including a prototype system, Smokey. Smokey builds a 47-element feature vector based on the syntax and semantics of each sentence, combining the vectors for the sentences within each message. A training set of 720 messages was used by Quinlan&#8217;s C4.5 decision-tree generator to determine featurebased rules that were able to correctly categorize 64% of the flames and 98% of the non-flames in a separate test set of 460 messages. Additional techniques for greater accuracy and user customization are also discussed.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Abusive messages (flames) can be both a source of frustration and a waste of time for Internet users. This paper describes some approaches to flame recognition, including a prototype system, Smokey. Smokey builds a 47-element feature vector based on the syntax and semantics of each sentence, combining the vectors for the sentences within each message. 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