Source-linked AI summary
COVID-Fact: Fact Extraction and Verification of Real-World Claims on COVID-19 Pandemic
Arkadiy Saakyan, Tuhin Chakrabarty, Smaranda Muresan
TL;DR
COVID-Fact addresses the need for scalable automated fact-checking of COVID-19 misinformation by providing claims with supporting or refuting evidence across scientific and general domains. The paper constructs the dataset semi-automatically, selecting real-world claims and evidence and generating counter-claims without human-written false claims. The resulting resource is presented as a challenging domain-specific testbed, while its counter-claim generation approach reduces dataset-construction costs within its controlled scope.
Problem
Web misinformation, especially around COVID-19, creates a need for automated fact-checking tools that can evaluate real-world claims and evidence across scientific and general language.
Method
The paper semi-automatically selects trustworthy claims and evidence, generates counter-claims by replacing salient words with opposites, and identifies evidence sentences using similarity and crowdsourcing.
Results
COVID-Fact contains 4,086 claims, and models trained on FEVER and SciFact do not perform well on its data; fine-tuning on COVID-Fact improves gold-evidence F1 by 25 and 35 points over SciFact and FEVER, respectively.
Takeaways & Limitations
COVID-Fact provides a challenging domain-specific benchmark for fact-checking systems and a simple, scalable, cost-efficient approach to creating such datasets.
Takeaways & Limitations
The counter-claim generator performs controlled word-level replacement and is not suited to generating entirely new claims, false evidence, or full articles of false information.
Abstract
from arXiv · showhide
We introduce a FEVER-like dataset COVID-Fact of $4,086$ claims concerning the COVID-19 pandemic. The dataset contains claims, evidence for the claims, and contradictory claims refuted by the evidence. Unlike previous approaches, we automatically detect true claims and their source articles and then generate counter-claims using automatic methods rather than employing human annotators. Along with our constructed resource, we formally present the task of identifying relevant evidence for the claims and verifying whether the evidence refutes or supports a given claim. In addition to scientific claims, our data contains simplified general claims from media sources, making it better suited for detecting general misinformation regarding COVID-19. Our experiments indicate that COVID-Fact will provide a challenging testbed for the development of new systems and our approach will reduce the costs of building domain-specific datasets for detecting misinformation.
1 Introduction
COVID-Fact addresses the growing need to automate fact-checking by introducing a semi-automatic dataset construction method for real-world COVID-19 claims. It combines trustworthy evidence selection, automatic counter-claim generation, and evidence-sentence selection for claims spanning scientific and general language.
- Motivation and contributions: The paper proposes a semi-automatic method to construct a COVID-19 fact-checking dataset supporting claim retrieval, evidence selection, and veracity prediction.The method is intended to facilitate the main stages of an end-to-end fact-checking pipeline.
- Dataset construction: True claims and trustworthy evidence documents are selected automatically from the moderated r/COVID19 subreddit and related source materials.The source rules require linked evidence from peer-reviewed research, established preprint servers, governments, or other reputable agencies.
- Dataset construction: Counter-claims are generated by selecting salient claim words with BERT attention and replacing them with opposites through masked-language-model infilling.The generated counter-claims undergo entailment-based quality control.
- Dataset construction: Evidence sentences are selected using sentence-BERT similarity to retrieve five candidates, followed by crowdsourced validation.The resulting evidence sentences support true claims and refute their corresponding counter-claims.
- Motivation and contributions: COVID-Fact contains 4,086 real-world claims with sentence-level evidence and a baseline for fact-checking research.The dataset includes claims, evidence documents, and evidence sentences supporting or refuting the claims.
2 COVID-Fact Dataset Construction
COVID-Fact is constructed semi-automatically from moderated real-world claims and trustworthy evidence, then expanded with automatically generated counter-claims. The resulting dataset combines scientific and lay-language claims with evidence selected through filtering and crowdsourcing.
- 4,086 claims comprise 1,296 supported claims and 2,790 automatically generated refuted claims, each paired with evidence documents and sentences.
- Real-world claim selection: Moderated r/COVID19 posts supply candidate claims and linked sources from peer-reviewed research, preprints, governments, and reputable agencies.
- Real-world claim selection: 1,407 true claims remained after reducing 22,646 posts through syntactic filtering, trustworthiness filtering, and manual validation.
- Counter-claim generation: Counter-claims replace salient words with context-appropriate opposites generated by a CORD-19-finetuned RoBERTa masked language model and entailment-based quality control.
- Counter-claim generation: At a contradiction threshold of 0.9, the replacement process achieved 76% precision and generated 4,042 additional false claims from 1,407 originals.
- Evidence sentence selection: The method changes only one word or multi-word expression, while final evidence labels use majority votes and show moderate independent re-annotation agreement with Cohen’s kappa of 0.5.
3 Experimental setup
The experimental setup evaluates evidence retrieval, veracity prediction, and their joint COVID-FEVER score using baseline search, sentence-embedding, and transformer models. Additional experiments test transfer from existing datasets and COVID-Fact’s usefulness for scientific fact-checking.
- Tasks and metrics: COVID-Fact follows FEVER with subtasks for evidence retrieval, veracity prediction, and joint evidence-plus-veracity evaluation.
- Tasks and metrics: Evidence retrieval is evaluated with precision, recall, and F1, while veracity prediction uses F1 score.
- Tasks and metrics: COVID-FEVER requires correct veracity prediction and at least one predicted evidence sentence matching gold evidence, making it stricter than veracity accuracy alone.
- Baseline pipeline: The baseline retrieves five Google Search documents, ranks candidate sentences with SBERT cosine similarity, and predicts veracity with RoBERTa.
- Additional experiments: Experiments compare RoBERTa-large trained on FEVER, SciFact, MNLI, COVID-Fact, and combinations of COVID-Fact with FEVER or SciFact.
- Additional experiments: A zero-shot experiment tests whether COVID-Fact supports scientific fact-checking despite not being explicitly designed for that task.
4 Results and Analysis
COVID-Fact provides strong evidence-retrieval and veracity-prediction baselines, while exposing difficulty for models trained on existing datasets and on complex causal, commonsense, and scientific relations.
- Evidence Retrieval: 32 F1 is achieved for evidence retrieval, exceeding the FEVER baseline of 18.26 F1.The pipeline retrieves evidence with Google Search and SBERT, including automatically negated claims.
- Veracity Prediction: 25 F1-score and 35 F1-score improvements arise from COVID-Fact fine-tuning over SciFact-only and FEVER-only training, respectively, with gold evidence.Retrieved-evidence settings remain difficult to outperform, and the claim-only baseline performs poorly.
- Zero-Shot Transfer: COVID-Fact training generalizes strongly to SciFact scientific-claim verification, whereas SciFact-only training does not generalize well to COVID-Fact.The authors attribute this asymmetry to COVID-Fact containing both scientific and non-scientific claims.
- Error Analysis: Errors reflect unresolved cause-and-effect, commonsense, and domain-specific scientific relationships in claim-evidence pairs.Examples require counterfactual reasoning, linking references across text, and recognizing equivalent scientific concepts.
5 Related Work
Prior fact-checking resources draw on journalist-checked statements, news, community forums, Wikipedia, and fact-checking websites, while COVID-19 NLP work develops pandemic corpora and misinformation resources.
- Fact-Checking: Fact-checking research has used journalist or organizational statements, news articles, community forums, and mixed-domain datasets.Examples include UKP Snopes, MultiFC, and FEVER, which rely on Wikipedia and fact-checking websites for evidence.
- Scope: The related work establishes a landscape spanning general fact-checking resources and COVID-19-specific NLP datasets and systems.The cited work covers both evidence-based verification and pandemic information processing.
- COVID-19 Related NLP Tasks: COVID-19 NLP resources include CORD-19, the COVIDEX neural search engine, a misinformation-perplexity hypothesis, and COVIDLIES.These resources support pandemic literature retrieval and misinformation-oriented analysis.
6 Conclusion
The paper releases COVID-Fact as a large resource of real-world COVID-19 claims with supporting and refuting evidence, and presents automatic methods for scalable domain-specific fact-checking data creation.
- Dataset: 4,086 COVID-19 claims are released with supporting and refuting evidence, including real-world true claims and automatically generated counter-claims.The dataset is intended as a challenging testbed for end-to-end COVID-19 fact-checking.
- Findings: COVID-Fact outperforms zero-shot baselines trained on SciFact and FEVER in the reported experiments.The conclusion connects this result to the impact of domain-specific vocabulary on popular NLP benchmarks.
- Implications: Automatic counter-claim generation offers a simple, scalable, and cost-efficient route to creating domain-specific fact-checking datasets.The paper provides a detailed evaluation of the COVID-Fact task and dataset.
7 Ethics
The dataset collection and counter-claim generation were designed with privacy, worker compensation, source quality, and misuse concerns in mind. The authors limit the generation system to controlled word replacement rather than entirely new false claims or false evidence.
- Privacy: The dataset excludes user-identifying information and collects only post titles, public links, and limited metadata from Reddit.The metadata includes flairs, upvote ratio, and posting date.
- Worker compensation: Crowdsourcing workers were compensated according to local minimum-wage guidelines.
- Generation scope: The counter-claim model performs controlled word-level replacement and cannot generate entirely new claims, false-information articles, or false evidence.
- Intended use: The model is intended to improve entailment and veracity prediction rather than the generative quality of false-claim systems.The authors also note that generated biased claims could serve as examples for studying bias in disinformation.
A Model implementation details
The implementation uses uncased BERT and RoBERTa-based models with FAIRSEQ configurations, fixed training schedules, and two RTX 2080 GPUs. Training takes substantially longer for FEVER models than for the other models.
- BERT training: The uncased BERT model is trained on SciFact for 15 epochs with batch size 16 and training loss 7.15e −03.
- Model: The experiments use a 355M-parameter RoBERTa-large checkpoint with the FAIRSEQ implementation.
- Training schedule: RoBERTa models are fine-tuned for 10 epochs, with the best checkpoint selected using COVIDFact validation accuracy.
- Compute: Training takes 30 minutes per model except for FEVER models, which require around 10 hours, using 2 RTX 2080 GPUs.
- Hyperparameters: The training configuration follows FAIRSEQ's RoBERTa RTE setup, using a 1024-token mini-batch size.
B Dataset statistics
The dataset statistics are presented through figures showing the most frequent Reddit flairs and word-cloud distributions of salient and replaced words. These visualizations summarize the dataset’s post categories and vocabulary patterns.
- Overview: The figures visualize both dataset flairs and vocabulary associated with keyword replacement.
- Flairs: Figure 3 displays the most frequent flairs in the dataset.
- Word clouds: Figure 4 compares word clouds of salient words with word clouds of replaced words.
B.1 Word replacement statistics
The word-replacement statistics report the most frequent salient words, replacement words, and part-of-speech tags for replacements. These figures characterize which terms are selected and how they are replaced.
- Overview: The statistics cover both replacement vocabulary and the part-of-speech patterns produced by the replacement process.
- Salient words: Figure 5 shows the most frequent salient words selected in the dataset.
- Replacement words: Figure 6 shows the most frequent words used to replace salient words.
- Part-of-speech tags: Figure 7 shows the most frequent part-of-speech tags assigned to replacement words.
B.2 Alexa threshold
The authors use an Alexa Site Rank boxplot to select a 50,000 threshold for filtering initially scraped claims. Outliers in the 2K-claim sample all lie above this threshold.
- The boxplot shows Alexa Site Ranks for 2K initially scraped claims.
- The analysis selects 50,000 as the Alexa siterank threshold.The threshold was chosen using the boxplot.
- All outliers fall above the 50,000 threshold.Outliers are defined as points outside the plot whiskers.