Evidence map›Paper›PMID 42894769›Full record

ArticleData in brief2026

OMANISHA: A benchmark dataset for identifying and categorizing Bengali misogynistic text.

Fatama Jannat Tisha, Bibhas Roy Chowdhury Piyas, Nurjahan Afrose, Sabbir Ahmed, Shahrin Islam, Shazzad Hossen, Jafrin Iqbal Chowdhury, Afsana Begum, Imran Mahmud

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In one paragraph

Article in Data in brief, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

9 authors.

Fatama Jannat TishaDepartment of Software Engineering, Daffodil International University, Dhaka, 1216, Bangladesh.
Bibhas Roy Chowdhury PiyasDepartment of Software Engineering, Daffodil International University, Dhaka, 1216, Bangladesh.
Nurjahan AfroseDepartment of Software Engineering, Daffodil International University, Dhaka, 1216, Bangladesh.
Sabbir AhmedDepartment of Software Engineering, Daffodil International University, Dhaka, 1216, Bangladesh.
Shahrin IslamDepartment of Software Engineering, Daffodil International University, Dhaka, 1216, Bangladesh.
Shazzad HossenDepartment of Computer Science and Engineering, Daffodil International University, Dhaka, 1216, Bangladesh.
Jafrin Iqbal ChowdhuryDepartment of Software Engineering, Daffodil International University, Dhaka, 1216, Bangladesh.
Afsana BegumDepartment of Software Engineering, Daffodil International University, Dhaka, 1216, Bangladesh.
Imran MahmudDepartment of Software Engineering, Daffodil International University, Dhaka, 1216, Bangladesh.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Misogynistic discourse in online spaces has profound psychological, social, and institutional consequences for women, contributing to persistent gender inequalities, the normalization of gender-based violence, and the suppression of female voices in digital communities. The unchecked proliferation of such hostile content across social media platforms has made automated misogyny detection a pressing social imperative. Despite Bengali's global prominence, computational resources for detecting gender-based online abuse remain limited. To address this gap, we introduce OMANISHA (

Indexed as

Bengali misogynistic text detectionGender-based online violenceHate speech detectionLow-resource languageNatural language processingOnline abuse detectionOnline misogynySocial media analysis

Identifiers

PMID42894769
PMCPMC13677231

What OpenQuestion holds

Textmetadata
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Registered trials

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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.