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

• URL: http://arxiv.org/abs/2102.03870v1
• Date: Sun, 7 Feb 2021 18:14:16 GMT
• Title: "Short is the Road that Leads from Fear to Hate": Fear Speech in Indian WhatsApp Groups
• Authors: Punyajoy Saha, Binny Mathew, Kiran Garimella, Animesh Mukherjee
• Abstract summary: We perform the first large scale study on fear speech across thousands of public WhatsApp groups discussing politics in India. We build models to classify fear speech and observe that current state-of-the-art NLP models do not perform well at this task.
• Score: 8.682669903229165
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Silent Signals, Loud Impact: LLMs for Word-Sense Disambiguation of Coded Dog Whistles [47.61526125774749]
  A dog whistle is a form of coded communication that carries a secondary meaning to specific audiences and is often weaponized for racial and socioeconomic discrimination. We present an approach for word-sense disambiguation of dog whistles from standard speech using Large Language Models (LLMs) We leverage this technique to create a dataset of 16,550 high-confidence coded examples of dog whistles used in formal and informal communication.
  arXiv  Detail & Related papers  (2024-06-10T23:09:19Z)
• Analysis and Detection of Multilingual Hate Speech Using Transformer Based Deep Learning [7.332311991395427]
  As the prevalence of hate speech increases online, the demand for automated detection as an NLP task is increasing. In this work, the proposed method is using transformer-based model to detect hate speech in social media, like twitter, Facebook, WhatsApp, Instagram, etc. The Gold standard datasets were collected from renowned researcher Zeerak Talat, Sara Tonelli, Melanie Siegel, and Rezaul Karim. The success rate of the proposed model for hate speech detection is higher than the existing baseline and state-of-the-art models with accuracy in Bengali dataset is 89%, in English: 91%, in German
  arXiv  Detail & Related papers  (2024-01-19T20:40:23Z)
• Analyzing Norm Violations in Live-Stream Chat [49.120561596550395]
  We study the first NLP study dedicated to detecting norm violations in conversations on live-streaming platforms. We define norm violation categories in live-stream chats and annotate 4,583 moderated comments from Twitch. Our results show that appropriate contextual information can boost moderation performance by 35%.
  arXiv  Detail & Related papers  (2023-05-18T05:58:27Z)
• On the rise of fear speech in online social media [7.090807766284268]
  Fear speech, as the name suggests, attempts to incite fear about a target community. This article presents a large-scale study to understand the prevalence of 400K fear speech and over 700K hate speech posts collected from Gab.com.
  arXiv  Detail & Related papers  (2023-03-18T02:46:49Z)
• Automated Sentiment and Hate Speech Analysis of Facebook Data by Employing Multilingual Transformer Models [15.823923425516078]
  We analyse the statistical distribution of hateful and negative sentiment contents within a representative Facebook dataset. We employ state-of-the-art, open-source XLM-T multilingual transformer-based language models to perform sentiment and hate speech analysis.
  arXiv  Detail & Related papers  (2023-01-31T14:37:04Z)
• Addressing the Challenges of Cross-Lingual Hate Speech Detection [115.1352779982269]
  In this paper we focus on cross-lingual transfer learning to support hate speech detection in low-resource languages. We leverage cross-lingual word embeddings to train our neural network systems on the source language and apply it to the target language. We investigate the issue of label imbalance of hate speech datasets, since the high ratio of non-hate examples compared to hate examples often leads to low model performance.
  arXiv  Detail & Related papers  (2022-01-15T20:48:14Z)
• The Spread of Propaganda by Coordinated Communities on Social Media [43.2770127582382]
  We analyze the spread of propaganda and its interplay with coordinated behavior on a large Twitter dataset about the 2019 UK general election. The combination of the use of propaganda and coordinated behavior allows us to uncover the authenticity and harmfulness of the different communities.
  arXiv  Detail & Related papers  (2021-09-27T13:39:10Z)
• Hate speech detection using static BERT embeddings [0.9176056742068814]
  Hate speech is emerging as a major concern, where it expresses abusive speech that targets specific group characteristics. In this paper, we analyze the performance of hate speech detection by replacing or integrating the word embeddings. In comparison to fine-tuned BERT, one metric that significantly improved is specificity.
  arXiv  Detail & Related papers  (2021-06-29T16:17:10Z)
• PoliWAM: An Exploration of a Large Scale Corpus of Political Discussions on WhatsApp Messenger [1.2301855531996841]
  WhatsApp Messenger is one of the most popular channels for spreading information with a current reach of more than 180 countries and 2 billion people. In the recent past, several countries have witnessed its effectiveness and influence in political and social campaigns. We observe a high surge in information and propaganda flow during election campaigning.
  arXiv  Detail & Related papers  (2020-10-26T00:35:57Z)
• Racism is a Virus: Anti-Asian Hate and Counterspeech in Social Media during the COVID-19 Crisis [51.39895377836919]
  COVID-19 has sparked racism and hate on social media targeted towards Asian communities. We study the evolution and spread of anti-Asian hate speech through the lens of Twitter. We create COVID-HATE, the largest dataset of anti-Asian hate and counterspeech spanning 14 months.
  arXiv  Detail & Related papers  (2020-05-25T21:58:09Z)
• Echo Chambers on Social Media: A comparative analysis [64.2256216637683]
  We introduce an operational definition of echo chambers and perform a massive comparative analysis on 1B pieces of contents produced by 1M users on four social media platforms. We infer the leaning of users about controversial topics and reconstruct their interaction networks by analyzing different features. We find support for the hypothesis that platforms implementing news feed algorithms like Facebook may elicit the emergence of echo-chambers.
  arXiv  Detail & Related papers  (2020-04-20T20:00:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.