Is social media a helpful communicative tool in combatting corruption in developing countries? Evidence from Ghana
Bibliographic record
Abstract
The traditional mainstream media is often considered part of the capital and power network involved in corruption, casting doubt on the traditional media’s watchdog functions. This study examines whether social media can be a positive communicative tool for addressing corruption, especially in developing countries such as Ghana. Drawing on in-depth semi-structured interviews and relevant secondary data, Habermas’s theory of the public sphere, and the notion of citizen journalism as a form of social accountability, the study addresses key questions: As a communicative platform/tool, does social media provide utility toward the fight against corruption? In what ways does social media support or undermine the fight against corruption? Findings indicate that social media is a valuable communicative instrument for combating corruption. It offers an alternative platform for exposing corruption; naming and shaming offenders; and mobilizing, organizing, protesting, and demanding accountability. Nevertheless, the study reveals that social media routinely spreads fake news, propaganda, and misinformation, undermining its credibility as an effective anti-corruption communicative tool. This article contributes to the debate on whether social media is a valuable communicative tool in the fight against corruption, especially in the developing country context.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".