Disinformation under a networked authoritarian state: Saudi trolls’ credibility attacks against Jamal Khashoggi
Bibliographic record
Abstract
Abstract This paper deals with a case study that provides unique and original insight into social media credibility attacks against the Saudi journalist and activist, Jamal Khashoggi. To get the data, I searched all the state-run tweets sent by Arab trolls (78,274,588 in total), and I used Cedar, Canada’s supercomputer, to extract all the videos and images associated with references to Khashoggi. In addition, I searched Twitter’s full data archive to cross-examine some of the hashtag campaigns that were launched the day Khashoggi disappeared and afterwards. Finally, I used CrowdTangle to understand whether some of these hashtags were also used on Facebook and Instagram. I present here evidence that just a few hours after Khashoggi’s disappearance in the Saudi Consulate in Istanbul, Saudi trolls started a coordinated disinformation campaign against him to frame him as a terrorist, foreign agent for Qatar and Turkey, liar.... etc. The trolls also emphasized that the whole story of his disappearance and killing is a fabrication or a staged play orchestrated by Turkey and Qatar. The campaign also targeted his fiancée, Hatice Cengiz, alleging she was a spy, while later they cast doubt about her claims. Some of these campaigns were launched a few months after Khashoggi’s death. Theoretically, I argue that state-run disinformation campaigns need to incorporate the dimension of intended effect. In this case study, the goal is to tarnish the reputation and credibility of Khashoggi, even after he died, in an attempt to discredit his claims and political cause, influence different audiences especially the Saudi public, and potentially reduce sympathy towards him.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".