Detecting Fake News on Social Media via Multimodal Semantic Understanding and Enhanced Transformer Architectures
Bibliographic record
Abstract
With the rapid development of social media, multimodal news combining text and images has become a primary vehicle for the spread of misinformation due to its strong dissemination power and broad audience reach.Existing fake news detection methods overly rely on textual analysis, making it difficult to effectively capture the deep semantic relationships between visual and textual modalities.Moreover, traditional neural networks often suffer from limited robustness and weak noise resistance when handling heterogeneous multimodal data.Although multimodal learning has recently been introduced into this field, challenges such as insufficient text-image semantic matching and a lack of architectural innovation persist.To address these issues, this study proposes a detection framework that integrates multimodal semantic understanding with an enhanced Transformer architecture.Specifically, a cross-modal attention mechanism is constructed to strengthen the interactive representation of visual and textual features through semantic alignment.Additionally, a hierarchical Transformer structure with a dynamic gating mechanism is designed to optimize the multimodal information fusion strategy, significantly improving the model's adaptability to complex social media scenarios.Experiments conducted on public datasets demonstrate that the proposed method achieves a noticeable improvement in detection accuracy compared to traditional models, while also exhibiting superior recall and noise robustness.This research offers a more efficient technological pathway for fake news detection on social media and promotes the broader application of multimodal learning in the domain of information content security.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".