Use of Twitter Among Political Office Holders for Public Communication in Nigeria: Prospects and Challenges
Bibliographic record
Abstract
The explosion of the internet in recent times, has dramatically transformed the way information is created, disseminated and distributed. In an age when information equates to power, a diversity of opinion can actually lead to more creative problem solving and more equitable outcomes in a society. This paper appraises the use of Twitter among political office holders for public communication looking at its prospects and challenges. The paper adopted textual analysis and interview as method of gathering data for the study to discover the role Twitter plays in breaking the news and how traditional media channels are now picking up tweets of political office holders and treating it as press releases for public consumption. Anchored on social presence theory, which is one of the most popular constructs used to describe and understand how people socially interact in an online learning environment, this study found that Twitter is a great platform for public communication, but access to internet is still limited to urban areas and among the elites, unlike the US where 62 percent of adults get news from Twitter, and a president has over 42 million followers, but in Nigeria, we are not yet there. We should not discard the use of the traditional press releases and regular engagement with the media and stakeholders.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| 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".