Harnessing Big Data for Soft Power: A Sentiment Analysis of FIFA Qatar 2022
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.
Bibliographic record
Abstract
This study explores the shifting perspectives and controversies surrounding the FIFA World Cup Qatar 2022 through the lens of Twitter (X) data and how Qatar used the World Cup as a platform for sports diplomacy, enhancing its soft power through infrastructure investments, cultural initiatives, and reforms aimed at addressing security and geopolitical concerns. The study uses advanced data analytics and sentiment analysis techniques to measure the shift in perceptions about the FIFA World Cup Qatar 2022. A theme-based analysis was adopted, in addition to using the pretrained word-embedding model. A total data set of 30,935,069 unique tweets was examined. Three of the four hypotheses were supported. Specifically, the hypotheses regarding shifting perceptions of cultural inclusion and alcohol, transportation infrastructure, and weather received backing. This study offers insights into how nations navigate global scrutiny during mega-sporting events to enhance their image and influence.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it