Title
Detecting Misogynous Tweets
Publication Date
2018
Document Type
Conference Proceeding
Abstract
Social media companies struggle to control the quality of the content on their platforms. The sheer amount of user-generated content uploaded on a daily basis far exceeds what can be screened by human curators, fuelling the need for intelligent detection algorithms that can automatically flag inappropriate content. In this paper, we present machine learning models that can identify instances of aggression and hate speech towards women in tweets. In particular, we present the system that we submitted for the shared task on automatic misogyny identification at IberEval 2018. © 2018 CEUR-WS. All Rights Reserved.
Volume
2150
First Page
242
Last Page
248
Recommended Citation
Ahluwalia, R.; Shcherbinina, E.; Callow, E.; Nascimento, Anderson; and De Cock, Martine, "Detecting Misogynous Tweets" (2018). School of Engineering and Technology Publications. 265.
https://digitalcommons.tacoma.uw.edu/tech_pub/265
