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Detecting Inappropriate Comments to News

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AI*IA 2018 – Advances in Artificial Intelligence (AI*IA 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11298))

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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.

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Author information

Authors and Affiliations

  1. University of Trento, CIMeC, Trento, Italy

    Patrizio Bellan

  2. FBK-irst, Trento, Italy

    Carlo Strapparava

Authors
  1. Patrizio Bellan
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  2. Carlo Strapparava
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Corresponding author

Correspondence to Carlo Strapparava .

Editor information

Editors and Affiliations

  1. Fondazione Bruno Kessler, Povo (TN), Italy

    Chiara Ghidini

  2. Fondazione Bruno Kessler, Povo (TN), Italy

    Bernardo Magnini

  3. University of Trento, Povo (TN), Italy

    Andrea Passerini

  4. Fondazione Bruno Kessler, Povo (TN), Italy

    Paolo Traverso

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Bellan, P., Strapparava, C. (2018). Detecting Inappropriate Comments to News. In: Ghidini, C., Magnini, B., Passerini, A., Traverso, P. (eds) AI*IA 2018 – Advances in Artificial Intelligence. AI*IA 2018. Lecture Notes in Computer Science(), vol 11298. Springer, Cham. https://doi.org/10.1007/978-3-030-03840-3_30

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  • DOI: https://doi.org/10.1007/978-3-030-03840-3_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-03839-7

  • Online ISBN: 978-3-030-03840-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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