Evidence map›Paper›PMID 37006948›Full record

ArticleArray (New York, N.Y.)2022

Natural language model for automatic identification of Intimate Partner Violence reports from Twitter.

Mohammed Ali Al-Garadi, Sangmi Kim, Yuting Guo, Elise Warren, Yuan-Chi Yang, Sahithi Lakamana, Abeed Sarker

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Array (New York, N.Y.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
–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

9 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Detecting Patterns of Intimate Partner Violence Using Qualitative Analyses and Machine Learning Algorithms.Prevention science : the official journal of the Society for Prevention Research · 2026
    Article
  3. Article
  4. Review
  5. Violence against women and girls research: Leveraging gains across disciplines.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  6. Article
  7. Article
  8. Automatic Detection of Intimate Partner Violence Victims from Social Media for Proactive Delivery of Support.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2023
    Article
  9. Generalizable Natural Language Processing Framework for Migraine Reporting from Social Media.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2023
    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.

Mohammed Ali Al-GaradiDepartment of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA, United States.
Sangmi KimSchool of Nursing, Emory University, Atlanta, GA, United States.
Yuting GuoDepartment of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA, United States.
Elise WarrenRollins School of Public Health, Emory University, Atlanta, GA, United States.
Yuan-Chi YangDepartment of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA, United States.
Sahithi LakamanaDepartment of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA, United States.
Abeed SarkerDepartment of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA, United States.

Funding

Race/Ethnicity-Specific Algorithms of Chronic Stress Exposures for Preterm Birth Risk: Machine Learning ApproachK01NR019651 · NINR · EMORY UNIVERSITY · PI KIM, SANGMI · 2022 to 2024
$441k
NINR NIH HHS K01 NR019651
6 · The paper itself

Abstract

Intimate partner violence (IPV) is a preventable public health problem that affects millions of people worldwide. Approximately one in four women are estimated to be or have been victims of severe violence at some point in their lives, irrespective of age, ethnicity, and economic status. Victims often report IPV experiences on social media, and automatic detection of such reports via machine learning may enable improved surveillance and targeted distribution of support and/or interventions for those in need. However, no artificial intelligence systems for automatic detection currently exists, and we attempted to address this research gap. We collected posts from Twitter using a list of IPV-related keywords, manually reviewed subsets of retrieved posts, and prepared annotation guidelines to categorize tweets into IPV-report or non-IPV-report. We annotated 6,348 tweets in total, with the inter-annotator agreement (IAA) of 0.86 (Cohen's kappa) among 1,834 double-annotated tweets. The class distribution in the annotated dataset was highly imbalanced, with only 668 posts (~11%) labeled as IPV-report. We then developed an effective natural language processing model to identify IPV-reporting tweets automatically. The developed model achieved classification F

Indexed as

Domestic violenceIntimate partner violenceMachine learningNaturel language processingSocial media

Identifiers

PMID37006948
PMCPMC10065459

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.