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

TL;DR

This paper introduces a distributional semantics approach to classify malicious intent in Twitter posts about sexual assault, aiming to understand and counteract harmful narratives and rape myths.

Contribution

It proposes a novel malicious intent typology based on social construction theory and develops a CNN-based semantic classification model for Twitter data.

Findings

Effective classification of malicious intents in tweets

Identification of narrative contexts of harmful messages

Implications for gender violence policy design

Abstract

The recent surge in women reporting sexual assault and harassment (e.g., #metoo campaign) has highlighted a longstanding societal crisis. This injustice is partly due to a culture of discrediting women who report such crimes and also, rape myths (e.g., 'women lie about rape'). Social web can facilitate the further proliferation of deceptive beliefs and culture of rape myths through intentional messaging by malicious actors. This multidisciplinary study investigates Twitter posts related to sexual assaults and rape myths for characterizing the types of malicious intent, which leads to the beliefs on discrediting women and rape myths. Specifically, we first propose a novel malicious intent typology for social media using the guidance of social construction theory from policy literature that includes Accusational, Validational, or Sensational intent categories. We then present and evaluate a malicious intent classification model for a Twitter post using semantic features of the intent senses learned with the help of convolutional neural networks. Lastly, we analyze a Twitter dataset of four months using the intent classification model to study narrative contexts in which malicious intents are expressed and discuss their implications for gender violence policy design.