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Detecting Inappropriate Comments to News

  • Patrizio Bellan
  • Carlo Strapparava
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11298)

Abstract

Inappropriate comments, defined as deliberately offensive, off-topic, troll-like, or direct attacks based on religious, sexual, racial, gender, or ethnic posts, are becoming increasingly problematic in user-generated content on the internet, because they can either derail the conversation or spread out harassment. Furthermore, the computational analysis of this kind of content, posted in response to professional news-papers, is not well investigated yet. To such an extent, the most predictive linguistic and cognitive features were seldom been addressed, and inappropriateness was not investigated deeply. After collecting a new dataset of inappropriate comments, three classic machine learning models were tested over two possible representations for the data to fed in: normal and distorted. Text distortion technique, thanks to its ability to mask thematic information, enhanced classification performance resulting in the valuable ground in which extract features from. Lexicon based features showed to be the most valuable characteristics to consider. Logistic regression turned out to be the most efficient algorithm.

Keywords

Journal news comments Inappropriateness Off-topic Text distortion 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.University of Trento, CIMeCTrentoItaly
  2. 2.FBK-irstTrentoItaly

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