Evidence map›Paper›PMID 42213299›Full record

ArticleCanadian journal of public health = Revue canadienne de sante publique2026

Drivers of online abuse against Canadian public health officials: An LLM-based temporal analysis.

Samaneh Hosseini Moghaddam, Cheryl Regehr, Kelly Lyons, Vivek Goel, Kaitlyn Regehr

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Article in Canadian journal of public health = Revue canadienne de sante publique, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

5 authors.

Samaneh Hosseini MoghaddamFactor-Inwentash Faculty of Social Work, University of Toronto, Toronto, ON, Canada.ORCID http://orcid.org/0009-0005-5003-8917
Cheryl RegehrFactor-Inwentash Faculty of Social Work, University of Toronto, Toronto, ON, Canada. Cheryl.regehr@utoronto.ca.ORCID http://orcid.org/0000-0001-7814-7836
Kelly LyonsFaculty of Information, University of Toronto, Toronto, ON, Canada.ORCID http://orcid.org/0000-0001-6866-7301
Vivek GoelDalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.ORCID http://orcid.org/0000-0002-1389-2007
Kaitlyn RegehrDepartment of Information, University College London, London, UK.ORCID http://orcid.org/0000-0003-2285-5256

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesThis study identifies trends in abusive discourse towards public health professionals (PHPs) during the COVID-19 pandemic and explores associations between abusive digital content, case numbers, deaths, and major policy announcements.

methodsNatural language processing (NLP) and large language model (LLM) techniques were used to develop a computational model to detect abusive content on X (formerly Twitter). This model was applied to abusive posts targeting PHPs during COVID-19 by examining over 1.7 million posts from January 2020 to May 2021. Associations between spikes in abuse and the number of COVID-19 cases, deaths, and government monitoring updates were explored.

resultsPronounced surges in abusive posts coincided with rising case and death counts and the imposition of major federal COVID-19 policies, particularly during the pandemic's initial emergency response. Digital aggression increased at times of public health statements related to restrictions in social gatherings, testing criteria, vaccinations, and masking. However, during high-case periods, even statements providing case updates, expressing compassion, or urging collective responsibility coincided with higher numbers of abusive posts.

conclusionsThis work provides insights into the pressures faced by health officials online and offers implications for designing resilient public communication during future crises.

Indexed as

Abusive language detectionDigital abusePublic healthSocial mediaWorkplace violence

Identifiers

PMID42213299

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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.