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The recent surge in women reporting sexual assault and harassment (e.g.,#metoo campaign) has highlighted a longstanding societal crisis. This injusticeis partly due to a culture of discrediting women who report such crimes andalso, rape myths (e.g., 'women lie about rape'). Social web can facilitate thefurther proliferation of deceptive beliefs and culture of rape myths throughintentional messaging by malicious actors. This multidisciplinary studyinvestigates Twitter posts related to sexual assaults and rape myths forcharacterizing the types of malicious intent, which leads to the beliefs ondiscrediting women and rape myths. Specifically, we first propose a novelmalicious intent typology for social media using the guidance of socialconstruction theory from policy literature that includes Accusational,Validational, or Sensational intent categories. We then present and evaluate amalicious intent classification model for a Twitter post using semanticfeatures of the intent senses learned with the help of convolutional neuralnetworks. Lastly, we analyze a Twitter dataset of four months using the intentclassification model to study narrative contexts in which malicious intents areexpressed and discuss their implications for gender violence policy design.

Distributional Semantics Approach to Detect Intent in Twitter Conversations on Sexual Assaults