Abstract
Language is not only a tool for information transfer but also a vehicle of the producer’s subjectivity which adds to the persuasive power of the message. Given the proliferation of discriminatory and hateful content online, it is important to understand the mechanisms of such messages to curb its propagation as efficiently as possible. Looking into a number of factors, such as the characteristics of hate speech producers, contributes to properly addressing the destructive phenomenon of hate speech. This study aims to explore the role of gender in the production of online hate speech by focusing on the use of linguistic markers conveying affect. We analyse selected typographical, grammatical and lexical features in English and Slovene Facebook comments which were identified as conveying socially unacceptable propositions. The results show statistically significant differences in the use of linguistic markers of affect between English-speaking or Slovene-speaking male and female commenters with regard to their hate speech production. This study shows that men are more likely to post shorter and violent comments as opposed to offensive ones, while women tend to include more linguistic markers of affect in their comments on all studied levels.
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Notes
- 1.
Each comment was annotated by several annotators, and comments were tagged according to the modal value. For certain comments, however, the mode could not be defined. This study uses only the comments for which the modal value was available both at the level of type and target, meaning that these comments were judged as socially unacceptable by the majority of annotators.
- 2.
The FRENK corpus contains several categories of SUD (Violent, Threatening, Offensive, Inappropriate). In general terms, the Inappropriate tag was used when no target of SUD could be specified and the Violent, Threatening tag was chosen over Offensive if the comment included a threat or a call or an allusion to physical violence. For a detailed description of the annotation schema, see Ljubešić et al. (2019). In this study, we are not interested in the fine-grained distinctions between the inappropriate and offensive comments on the one hand and the violent and threatening ones on the other, so we merged them into two major categories Offensive and Violent.
- 3.
The information about the number of deleted comments is not available for our dataset. For current data regarding Facebook content moderation, see https://transparency.fb.com/data/community-standards-enforcement/hate-speech/facebook/
- 4.
Calculator provided by Stangroom (2022).
- 5.
Code by Bedre (2021).
- 6.
In the case of expressive punctuation, the unit can include more than one punctuation mark.
- 7.
It is important to bear in mind that typographical characteristics could also be influenced by the commenter’s device and its settings (e.g. autocorrect function), leading to differences which are more a reflection of technical affordances than of sociocultural or linguistic aspects.
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Acknowledgements
The work described in this chapter was funded by the Slovenian Research Agency research programme P6-0436: Digital Humanities: resources, tools and methods (2022-2027), the DARIAH-SI research infrastructure, and the national research project N6-0099: LiLaH: Linguistic Landscape of Hate Speech.
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Pahor de Maiti, K., Franza, J., Fišer, D. (2023). Linguistic Markers of Affect and the Gender Dimension in Online Hate Speech. In: Ermida, I. (eds) Hate Speech in Social Media. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-031-38248-2_13
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