Exploring the use of #MyAnglophoneCrisisStory on Twitter to understand the impacts of the Cameroon Anglophone Crisis
Bibliographic record
Abstract
Since October 2016, Cameroon has been involved in a violent conflict known as the Anglophone Crisis. This study examines the impact of the hashtag #MyAnglophoneCrisisStory on Twitter in capturing and amplifying the stories of people affected by the crisis. Using R, the authors extracted and analyzed tweets using this hashtag that were posted between 21 October 2020 and 3 November 2020. Only tweets posted in English and French languages were included. To understand the content of the tweets, the authors inductively coded and manually analyzed a total of 1064 tweets, replies, and comments. A categorical analysis revealed the presence of three different types of tweets: ‘Story’, ‘Response to Story’, and ‘Awareness and Advocacy’. The ‘Story’ category had four distinct themes: (1) Senseless Loss of Life: Shot and Killed; (2) The Disappeared: Lost and Kidnapped; (3) On the Move/Elusive Safety: Escape, Displacement; and (4) Prevention and Trauma, Mental Health, and Post Traumatic Stress Disorder. This study supports the concept that even short tweets can have a significant impact and signals the need for more attention and research on this overlooked conflict. Future work can involve the use of more advanced analysis tools to conduct a more thorough examination of tweets.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".