“Life is more important than football”: Comparative analysis of Tweets and Facebook comments regarding the cancellation of the 2015 African Cup of Nations in Morocco
Bibliographic record
Abstract
This study analyzes comments from two major social media, Facebook and Twitter, regarding the controversial cancellation of the 2015 African Cup of Nations (CAN) in Morocco and its transfer to Equatorial Guinea, a move precipitated by the contemporaneous outbreaks of Ebola in West Africa. Using frame analysis methodology (frames being the central ideas structuring a narrative account of an issue, event or controversy), it investigates how the sporting and health worlds are understood and conceptualized on Twitter and Facebook, in the context of a specific event. We also investigated the extent to which these frames are platform-specific. Data were collected by keyword extraction and submitted to a qualitative thematic and frame analysis, from which we identified six frames (Epidemic management, Sporting event, Political, Skepticism, Religion, and Economic). Analysis of these frames identified a number of classic issues from the sociology of not only football and epidemics but also of African political issues. The cancellation of the CAN thus provides an excellent window into the complex links between sport, heath and politics. Indeed, the online comments of social media users expressed a rich range of pre-existing frustrations, beliefs and political positions. Our results show that, in the context of the cancellation of the 2015 CAN, tweets mostly framed the event as an epidemic management issue, while Facebook comments typically framed it as an epidemic management, sporting and political event. Some themes treated in a factual way on Twitter became politicized on Facebook where, in addition, new political themes emerged. We conclude that studying social media conversations relating to a mega-sporting event could provide sociologically valuable insights about topics not typically directly associated with sport or health.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".