Nigeria EndSARS Protest: False Information Mitigation Hybrid Model
Bibliographic record
Abstract
False Information can lead to chaos and destruction of lives and properties, as was the case during the EndSARS protests in October 2020. Young people took to social media to campaign for the scrapping of the Special Anti-Robbery Squad (SARS) and for better governance in the country. Social media, especially Twitter, Facebook, and Instagram were used to garner the support of people to join the protests across the country. The use of social media platforms gave rise to a proliferation of unverified information in the social space. As events unfolded, some reports were confirmed as fake and some were quoted out of context. Different scholars have proffered solutions to mitigating the spread of false information but less attention has been paid to the combined role of the mainstream and social media. This study content analysed available reports of the EndSARS protests that were tagged fake and found out that rumour accounted for the most reported false information type. This implies that the mainstream media scale up their reporting to enable people get more authentic information. This study proposed a hybrid model factoring in the reporting role of the mainstream media as a potential strategy to mitigate false information in the media.
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.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".