Influence of Hate Speech on Public Perception of Presidential Candidates’ Credibility During the 2015 Presidential Election in Nigeria
Bibliographic record
Abstract
The study examines the influence of hate speech on public perception of presidential candidates’ credibility during the 2015 presidential election in Nigeria. The study was guided by two research questions. A descriptive survey design was adopted for the study using 72,001, 204 eligible registered voters in the six geo-political zones of Nigeria. 600 questionnaires were distributed and 518 were returned for analysis through multi-stage sampling. The research questions were answered using mean and standard deviation, while the hypotheses were tested using Pearson – chi-square test at 0.05 level of significance. Findings reveal that the extent to which voters were aware of hate speech against a political opponent during the 2015 presidential election in Nigeria was high. The findings further show that one of the factors that influenced hate speech against a political opponent during the 2015 presidential election in Nigeria was the political affiliation of voters. Concerning the null hypotheses, findings indicated that there was no significant relationship among the responses of the electorate in the six geo-political zones on the factors that influenced hate speech during the 2015 presidential election in Nigeria. A significant relationship was also not found on how hate speech influenced public perception of presidential candidates during the 2015 presidential election in Nigeria. The study recommends that politicians, political parties as well as their supporters should be cautioned on using social media to post hate speech, inciting messages, attack opponents, spread false news. The Independent National Electoral Commission (INEC) should propose to the National Assembly to enact laws in the electoral act that will make the use of hate speech for campaign purposes a punishable offence in the country.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".