Examining Misinformation and Deep Fakes
Bibliographic record
Abstract
Misinformation in the form of deep fakes and phishing links can not only spread false information but can only be used a weapon in the hands of cyber criminals. To combat this problem, the authors investigate fake news and misinformation, in a South African context. In the paper, the use of cyber scams that contain misinformation will also be unpacked. This aims to create an awareness and defensive approach to tackling emerging cyber threats that prey on misinformation. This paper tackles a growing concern by examining the pervasiveness of fake news by looking into the extent that fake news infiltrates various media channels and its potential impact on public perception and decision-making. The paper will also explore the anatomy of fake news by dissecting the common tactics and strategies employed by purveyors of fake news and highlight red flags that can help the public identify misinformation. Maintaining academic integrity is pivotal to the research and publication community. This paper will also promote the use of trusted sources and verification of information. The paper aims to promote media literacy by sharing strategies to enhance media literacy and critical thinking skills, empowering individuals to discern credible information from misleading content. This paper proposes a human-centric framework to empower individuals in South Africa to become discerning consumers of information. Recognizing the limitations of Artificial Intelligence (AI)-based detection methods and the unique challenges of the South African context (multilingualism, resource constraints), the framework emphasizes critical thinking and media literacy skills. It outlines a step-by-step process for evaluating information sources, including source credibility analysis, content verification, and cross-referencing. The effectiveness of the framework is demonstrated by a relevant use-case.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 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".