Evidence map›Paper›PMID 36877833›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2023

On the rise of fear speech in online social media.

Punyajoy Saha, Kiran Garimella, Narla Komal Kalyan, Saurabh Kumar Pandey, Pauras Mangesh Meher, Binny Mathew, Animesh Mukherjee

Open access · greenAbstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
5.7field-weighted citation impact, top 3% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 34 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. GPT is an effective tool for multilingual psychological text analysis.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
  5. Article
  6. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 2 institutions in 2 countries.

Punyajoy SahaDepartment of Computer Science and Engineering, Indian Institute of Technology, Kharagpur 721302, India.ORCID 0000-0002-3952-2514
Kiran GarimellaSchool of Communication and Information, Rutgers University, New Brunswick, NJ 08901.
Narla Komal KalyanDepartment of Computer Science and Engineering, Indian Institute of Technology, Kharagpur 721302, India.
Saurabh Kumar PandeyDepartment of Computer Science and Engineering, Indian Institute of Technology, Kharagpur 721302, India.
Pauras Mangesh MeherDepartment of Computer Science and Engineering, Indian Institute of Technology, Kharagpur 721302, India.ORCID 0000-0002-0062-138X
Binny MathewDepartment of Computer Science and Engineering, Indian Institute of Technology, Kharagpur 721302, India.ORCID 0000-0003-4853-0345
Animesh MukherjeeDepartment of Computer Science and Engineering, Indian Institute of Technology, Kharagpur 721302, India.ORCID 0000-0003-4534-0044
Indian Institute of Technology Kharagpur · INRutgers, The State University of New Jersey · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recently, social media platforms are heavily moderated to prevent the spread of online hate speech, which is usually fertile in toxic words and is directed toward an individual or a community. Owing to such heavy moderation, newer and more subtle techniques are being deployed. One of the most striking among these is fear speech. Fear speech, as the name suggests, attempts to incite fear about a target community. Although subtle, it might be highly effective, often pushing communities toward a physical conflict. Therefore, understanding their prevalence in social media is of paramount importance. 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. Remarkably, users posting a large number of fear speech accrue more followers and occupy more central positions in social networks than users posting a large number of hate speech. They can also reach out to benign users more effectively than hate speech users through replies, reposts, and mentions. This connects to the fact that, unlike hate speech, fear speech has almost zero toxic content, making it look plausible. Moreover, while fear speech topics mostly portray a community as a perpetrator using a (fake) chain of argumentation, hate speech topics hurl direct multitarget insults, thus pointing to why general users could be more gullible to fear speech. Our findings transcend even to other platforms (Twitter and Facebook) and thus necessitate using sophisticated moderation policies and mass awareness to combat fear speech.

Indexed as

Social MediaFearFertilityHateHumansSpeechfear speechGabhate speechprevalencesocial media

Identifiers

PMID36877833
PMCPMC10089164
OpenAlexW4323306471

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.