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Record W4388629422 · doi:10.3390/journalmedia4040072

The Networked Trolling of Critical Journalists and News Organizations in Iraq

2023· article· en· W4388629422 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournalism and Media · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicMisinformation and Its Impacts
Canadian institutionsUniversity of British ColumbiaSimon Fraser University
FundersGovernment of Canada
KeywordsDisinformationMisinformationPolitical scienceGovernment (linguistics)Action (physics)ArabicHuman rightsPublic relationsInternet privacyLawSocial mediaComputer science

Abstract

fetched live from OpenAlex

In this study, we have identified a Twitter network of bad actors mostly affiliated with Iraqi militias that are closely connected to the federal Iraqi government. Using disinformation and threats of legal action, these users often target journalists and news organizations that are critical of them. Three datasets were collected totaling about 16,000 tweets by using 6 Arabic hashtags. We found three major themes: public shaming and personal attacks; legal threats and misinformation accusations; and glorifying Shiite heroism and promoting conspiracies. These bad actors also created a coordinated attack against journalists, news organizations, and human rights activists and even the UN representative in Iraq, Jeanine Plasschaert, falsely accusing her of fabricating the 2021 federal election results.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.299
Threshold uncertainty score0.391

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.027
GPT teacher head0.345
Teacher spread0.318 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it