Automatic Detection of Entity-Manipulated Text using Factual Knowledge
Bibliographic record
Abstract
In this work, we focus on the problem of distinguishing a human written news article from a news article that is created by manipulating entities in a human written news article (e.g., replacing entities with factually incorrect entities).Such manipulated articles can mislead the reader by posing as a human written news article.We propose a neural network based detector that detects manipulated news articles by reasoning about the facts mentioned in the article.Our proposed detector exploits factual knowledge via graph convolutional neural network along with the textual information in the news article.We also create challenging datasets for this task by considering various strategies to generate the new replacement entity (e.g., entity generation from GPT-2).In all the settings, our proposed model either matches or outperforms the state-of-the-art detector in terms of accuracy.Our code and data are available at https://github.com/UBC-NLP/ manipulated_entity_detection.Human written text PubNub, a startup that develops the infrastructure to power key features in real-time applications (...) has raised $23 million in a series D round of funding from Hewlett Packard Enterprise (HPE), Relay Ventures, Sapphire Ventures, Scale Venture Partners, Cisco Investments, Bosch, and Ericsson.Manipulated text using GPT-2 PubNub, a startup that develops the infrastructure to power key features in real-time applications (...) has raised $23 million in a series D round of funding from
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| 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".