Qatar as Full Island Overnight: Psychological and Social Consequences of Blockade as Reflected in the Social Media
Bibliographic record
Abstract
The GCC crisis in which Saudi Arabia, UAE, and Bahrain have closed land, sea and air borders going into and from Qatar imposed a host of psychosocial stressors on both Qatari and non-Qatari citizens. This ongoing crisis is contributing to psychological and social problems at both the individual and community levels. Furthermore, pressures are placed on psychological, social, and mental health resources available in Qatar. Therefore, the current economic and political situation calls for a need to assess the resultant psychosocial effects at different levels (individuals, families, and communities) and types (emotional, psychological, and social). One method to empirically identify such psychosocial changes emergent from the crisis is to test individuals' reactions in social media, such as Twitter. Methodology: A total of 1238 tweets were collected over a period of 90 days using https://birdiq.net , 10 thematic representations were codified from 780 tweets. All tweets posted since the blockade started in June 5, 2017 contained a combination of 131 negative words. The ‘bad words’ were grouped into 10 clusters representing negative emotions. Findings: The results indicated that since the blockade, Qatari residents have begun to use an increasing amount of negative words in their Twitter profiles, indicating adverse tendencies towards the Gulf crisis. Using ten-day intervals up to the point in which this data was collected, significant changes have been observed in the ways in which people expressed themselves on Twitter. These findings are analyzed in the light of literature on the psychological impact of siege, blockade and isolation on populations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".