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Neural Network Hate Deletion: Developing a Machine Learning Model to Eliminate Hate from Online Comments

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Book cover Internet Science (INSCI 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11193))

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Abstract

We propose a method for modifying hateful online comments to non-hateful comments without losing the understandability and original meaning of the comments. To accomplish this, we retrieve and classify 301,153 hateful and 1,041,490 non-hateful comments from Facebook and YouTube channels of a large international media organization that is a target of considerable online hate. We supplement this dataset by 10,000 Reddit comments manually labeled for hatefulness. Using these two datasets, we train a neural network to distinguish linguistic patterns. The model we develop, Neural Network Hate Deletion (NNHD), computes how hateful the sentences of a social media comment are and if they are above a given threshold, it deletes them using a language dependency tree. We evaluate the results by comparing crowd workers’ perceptions of hatefulness and understandability before and after transformation and find that our method reduces hatefulness without resulting in a significant loss of understandability. In some cases, removing hateful elements improves understandability by reducing the linguistic complexity of the comment. In addition, we find that NNHD can satisfactorily retain the original meaning on average but is not perfect in this regard. In terms of practical implications, NNHD could be used in social media platforms to suggest more neutral use of language to agitated online users.

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Notes

  1. 1.

    We compiled the list of hate words by combining open coding done on our dataset and lists of profanity or swear words available online: http://www.bannedwordlist.com/lists/swearWords.txt; https://github.com/LDNOOBW/List-of-Dirty-Naughty-Obscene-and-Otherwise-Bad-Words/blob/master/en; https://www.frontgatemedia.com/a-list-of-723-bad-words-to-blacklist-and-how-to-use-facebooks-moderation-tool/; http://onlineslangdictionary.com/lists/most-vulgar-words/.

  2. 2.

    fastText is a library for learning of word embeddings and sentence classification created by Facebook.

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Authors and Affiliations

  1. Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar

    Joni Salminen, Hind Almerekhi, Bernard J. Jansen & Soon-gyo Jung

  2. University of Turku, Turku, Finland

    Joni Salminen & Juhani Luotolahti

Authors
  1. Joni Salminen
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  2. Juhani Luotolahti
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  3. Hind Almerekhi
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  4. Bernard J. Jansen
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  5. Soon-gyo Jung
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Corresponding author

Correspondence to Joni Salminen .

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Editors and Affiliations

  1. School of Journalism and Mass Communications, Saint Petersburg State University, St. Petersburg, Russia

    Svetlana S. Bodrunova

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Salminen, J., Luotolahti, J., Almerekhi, H., Jansen, B.J., Jung, Sg. (2018). Neural Network Hate Deletion: Developing a Machine Learning Model to Eliminate Hate from Online Comments. In: Bodrunova, S. (eds) Internet Science. INSCI 2018. Lecture Notes in Computer Science(), vol 11193. Springer, Cham. https://doi.org/10.1007/978-3-030-01437-7_3

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  • DOI: https://doi.org/10.1007/978-3-030-01437-7_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-01436-0

  • Online ISBN: 978-3-030-01437-7

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