MétaCan
Menu
Back to cohort
Record W2996589734 · doi:10.1017/rms.2019.45

A Storm of Tweets: Social Media Manipulation During the Gulf Crisis

2019· article· en· W2996589734 on OpenAlex
Andrew Leber, Alexei Abrahams

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.

Bibliographic record

VenueReview of Middle East Studies · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Media and Politics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSocial mediaInfluencer marketingCoercion (linguistics)ExploitPoliticsPolitical scienceStormMedia studiesState (computer science)Fake newsPublic relationsPolitical economySociologyAdvertisingLawBusinessComputer scienceComputer securityMarketing

Abstract

fetched live from OpenAlex

Abstract Social media platforms like Facebook and Twitter were heralded circa 2009–2011 as ‘liberation technology’ that would facilitate mass mobilization against Middle Eastern authoritarians. In this article, however, we present evidence from the ongoing Gulf Crisis (2017-present) that regimes can now exploit Twitter as an outlet for political propaganda. Drawing in part on novel data collected by the authors, we present strong evidence of state actors manipulating discourse on Twitter through direct intervention, offline coercion or co-optation of existing social-media “influencers,” and the mass production of online statements via automated “bot” accounts. We further present evidence that this manipulation is aimed at securing organic participation from supportive publics.

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)0.001
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 score0.135
Threshold uncertainty score0.251

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.118
GPT teacher head0.355
Teacher spread0.238 · 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