mlsanityData to 7 Oct 2026

"Short is the Road that Leads from Fear to Hate": Fear Speech in Indian WhatsApp Groups

arXiv 2102.03870 ↗First posted 7 Feb 2021cs.SI cs.AI cs.CLcited by 13

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Abstract (arXiv)

WhatsApp is the most popular messaging app in the world. Due to its popularity, WhatsApp has become a powerful and cheap tool for political campaigning being widely used during the 2019 Indian general election, where it was used to connect to the voters on a large scale. Along with the campaigning, there have been reports that WhatsApp has also become a breeding ground for harmful speech against various protected groups and religious minorities. Many such messages attempt to instil fear among the population about a specific (minority) community. According to research on inter-group conflict, such `fear speech' messages could have a lasting impact and might lead to real offline violence. In this paper, we perform the first large scale study on fear speech across thousands of public WhatsApp groups discussing politics in India. We curate a new dataset and try to characterize fear speech from this dataset. We observe that users writing fear speech messages use various events and symbols to create the illusion of fear among the reader about a target community. We build models to classify fear speech and observe that current state-of-the-art NLP models do not perform well at this task. Fear speech messages tend to spread faster and could potentially go undetected by classifiers built to detect traditional toxic speech due to their low toxic nature. Finally, using a novel methodology to target users with Facebook ads, we conduct a survey among the users of these WhatsApp groups to understand the types of users who consume and share fear speech. We believe that this work opens up new research questions that are very different from tackling hate speech which the research community has been traditionally involved in.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 16 of its 72 references that are in the map. First posted on arXiv 7 Feb 2021.

Builds on

16 of 72 references.

References · 16

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4 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 2; 2023: 5; 2024: 3; 2025: 0; 2026: 2.

13 papers.

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