An entity matching-based image topic verification framework for online fact-checking
Bibliographic record
Abstract
The last decade has witnessed an unprecedented growth in online multimedia data. However, the manipulated and fake images have created fertile grounds for sowing online fake news. Consequently, online fact-checking has drawn more attention from academia and industry to detect and mitigate online fake news. Nevertheless, most of the online fact-checking task focus on textual content. Although multimedia information like images can provide promising potentials for identifying misinformation, it has not been adequately studied. Besides, traditional information retrieval techniques, e.g., image caption generation, typically lack high-quality training data or their computation costs are very high. Aiming to address the above issues, we proposed an image topic verification framework based on named entity matching. Particularly, the proposed framework can effectively check if a targeted image is related to a specific topic or not. In addition, it can also retrieve helpful contextual background and knowledge about the targeted image. We conduct extensive experiments and analyses. The results validate the effectiveness and practicality of our framework.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".