Fake News Detection Using Deep Learning: A Systematic Literature Review
Bibliographic record
Abstract
Nowadays, we witness rapid technological advancements in online communication platforms, with increasing volumes of people using a vast range of communication solutions. The fast flow of information and the enormous number of users opens the door to the publication of non-truthful news, which has the potential to reach many people. Disseminating this news through low- or no-cost channels resulted in a flood of fake news that is difficult to detect by humans. Social media networks are one of these channels that are used to quickly spread this fake news by manipulating it in ways that influence readers in many aspects. That influence appears in a recent example amid the COVID-19 pandemic and various political events such as the recent US presidential elections. Given how this phenomenon impacts society, it is crucial to understand it well and study mechanisms that allow its timely detection. Deep learning (DL) has proven its potential for multiple complex tasks in the last few years with outstanding results. In particular, multiple specialized solutions have been put forward for natural language processing (NLP) tasks. In this paper, we systematically review existing fake news detection (FND) strategies that use DL techniques.We systematically surveyed the existing research articles by investigating the DL algorithms used in the detection process. Our focus then shifts to the datasets utilized in previous research and the effectiveness of the different DL solutions. Special attention was given to the application of strategies for transfer learning and dealing with the class imbalance problem. The effect of these solutions on the detection accuracy is also discussed. Finally, our survey provides an overview of key challenges that remain unsolved in the context of FND.
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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 | 0.006 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".