Social media discourses amidst ethnopolitical extremism and conflict: The case of Ethiopia
Bibliographic record
Abstract
This article examines how influencers and political armies in Ethiopia use social media specifically Facebook to propagate sectarian rhetoric and ethnopolitical extremism within a society grappling with protracted internal conflicts, prolonged ethnopolitical extremism, and armed clashes. The study employs qualitative research methodology, using critical discourse analysis (CDA) alongside facets of rhetorical analysis, to examine Facebook posts and comments produced in English and Amharic languages during the post-Tigray War politically tense period (the final quadrimester of 2023) amidst armed conflicts in the Amhara region. By purposively sampling content from pages of political influencers and elites, the study investigates the rhetorical patterns and discursive constructions shaping ethnopolitical narratives. The analysis reveals that the rhetorical and discursive patterns and textual trajectories on Facebook in Ethiopia are characterized by hostilities and animus, which are prone to promoting ethnopolitical radicalization and deepening the schism among ethnic and political groups.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".