Detecting Credible, Unreliable, and Leaked Evidence for Robust Automated Fact-Checking

Core Concepts

Automated fact-checking systems often rely on external evidence from the web, but this evidence can be unreliable or leaked from existing fact-checking articles, undermining the effectiveness of such systems. This work proposes a comprehensive approach to evidence verification and filtering to address these challenges.

Abstract

The content discusses the problem of unreliable and leaked evidence in automated fact-checking (AFC) systems. It highlights that while AFC systems leverage external information from the web to help examine the veracity of claims, they often overlook the importance of verifying the source and quality of the collected "evidence". The key points are: Reliance on "leaked evidence" (information gathered directly from fact-checking websites) and inclusion of information from unreliable sources can undermine the effectiveness of AFC systems. To address these challenges, the authors propose a comprehensive approach to evidence verification and filtering. They create the "CREDible, Unreliable or LEaked" (CREDULE) dataset, which consists of 91,632 articles classified as Credible, Unreliable and Fact-checked (Leaked). The authors also introduce the EVidence VERification Network (EVVER-Net), trained on CREDULE to detect leaked and unreliable evidence in both short and long texts. EVVER-Net can be used to filter evidence collected from the web, thus enhancing the robustness of end-to-end AFC systems. Experiments show that EVVER-Net can demonstrate impressive performance of up to 91.5% and 94.4% accuracy, while leveraging domain credibility scores along with short or long texts, respectively. The authors assess the evidence provided by widely-used fact-checking datasets, including LIAR-PLUS, MOCHEG, FACTIFY, NewsCLIPpings+ and VERITE, and identify concerning rates of leaked and unreliable evidence.

Stats

"Obama, Out of Office 10 Days, Speaks Out Against Immigration Ban" "Obama Rejects Trump Immigration Orders, Backs Protests" "Did President Obama Ban Muslims from Entering the United States in 2011?" "MORE HYPOCRISY: Obama Banned all Iraqi Refugees for 6 Months in 2011 – Liberals SAID NOTHING"

Quotes

"Automated fact-checking (AFC) is garnering increasing attention by researchers aiming to help fact-checkers combat the increasing spread of misinformation online." "One overlooked challenge involves the reliance on "leaked evidence", information gathered directly from fact-checking websites and used to train AFC systems, resulting in an unrealistic setting for early mis-information detection." "Similarly, the inclusion of information from unreliable sources can undermine the effectiveness of AFC systems."

Key Insights Distilled From

Credible, Unreliable or Leaked?: Evidence Verification for Enhanced Automated Fact-checking

by Zacharias Ch... at arxiv.org 05-01-2024

https://arxiv.org/pdf/2404.18971.pdf

Deeper Inquiries

How can the EVVER-Net model be extended to handle multimodal evidence, such as images and videos, in addition to textual information

To extend the EVVER-Net model to handle multimodal evidence, such as images and videos, alongside textual information, a few key modifications and enhancements can be implemented: Multimodal Fusion Techniques: Incorporate methods for fusing information from different modalities, such as text, images, and videos. Techniques like late fusion, early fusion, or attention mechanisms can be utilized to combine features extracted from each modality effectively. Multimodal Pre-trained Models: Utilize pre-trained models that are capable of processing multiple modalities simultaneously, such as CLIP (Contrastive Language-Image Pre-training) or ViLBERT (Vision-and-Language BERT). These models can handle both textual and visual inputs, enabling comprehensive analysis of evidence. Data Preprocessing: Develop preprocessing pipelines to extract features from images and videos, such as using image embeddings from pre-trained models like ResNet or video embeddings from models like I3D. These features can then be integrated with textual embeddings for a holistic representation of evidence. Model Architecture: Modify the architecture of EVVER-Net to accommodate multiple input modalities. This may involve creating parallel branches for processing different types of data and integrating them at later stages for classification. Training and Evaluation: Train the extended EVVER-Net model on a multimodal dataset that includes textual, visual, and possibly audio information. Evaluate the model's performance on a diverse range of evidence types to ensure robustness and generalization. By incorporating these strategies, EVVER-Net can be enhanced to effectively handle multimodal evidence, providing a more comprehensive and accurate verification process.

What are the potential biases and limitations of the CREDULE dataset, and how can they be addressed to further improve the robustness of the evidence verification process

The CREDULE dataset, while comprehensive and balanced, may still exhibit potential biases and limitations that could impact the evidence verification process. Some of these biases and limitations include: Labeling Bias: The manual labeling of articles as Fact-checked, Credible, or Unreliable may introduce subjective biases based on the annotators' interpretations. Implementing multiple annotators and inter-annotator agreement checks can help mitigate this bias. Domain Selection Bias: The sources from which articles are collected may introduce bias based on the domains included. To address this, a more diverse set of sources should be considered to ensure a representative sample. Temporal Bias: The dataset's composition over time may introduce temporal biases, where certain periods or events are overrepresented or underrepresented. Balancing the dataset across different time frames can help mitigate this bias. Topic Bias: The distribution of articles across topics may not be uniform, leading to biases in certain thematic areas. Ensuring an even distribution of topics can help reduce this bias. To address these biases and limitations and enhance the robustness of the evidence verification process, the following steps can be taken: Bias Analysis: Conduct a thorough analysis of potential biases in the dataset and document them transparently. Understanding these biases is crucial for developing mitigation strategies. Augmentation and Balancing: Augment the dataset with additional samples to balance out biases in terms of labels, domains, time frames, and topics. This can help create a more representative dataset. Regular Updates: Continuously update and refine the dataset to reflect evolving trends and ensure ongoing relevance and accuracy in evidence verification. External Validation: Validate the dataset with external experts or fact-checkers to ensure the quality and reliability of the labeled articles. By addressing these biases and limitations, the CREDULE dataset can be further improved to enhance the effectiveness of the evidence verification process.

How can the insights from this work be applied to enhance the overall performance and reliability of end-to-end automated fact-checking systems, beyond just the evidence verification stage

The insights from this work can be applied to enhance the overall performance and reliability of end-to-end automated fact-checking systems in several ways: Improved Evidence Filtering: Implement the EVVER-Net model or similar evidence verification mechanisms to filter out leaked and unreliable information during the evidence retrieval stage. This ensures that only credible evidence is considered in the fact-checking process, enhancing the accuracy of the system. Enhanced Claim Verification: Integrate the findings from EVVER-Net into the claim verification stage of automated fact-checking systems. By incorporating verified evidence, the system can make more informed decisions when assessing the veracity of claims. Real-time Misinformation Detection: Utilize the insights from this work to develop real-time misinformation detection systems that can proactively identify and flag potentially false information as it emerges online. This can help combat the rapid spread of misinformation on social media platforms. Continuous Model Training: Regularly update and retrain the automated fact-checking models with new data and insights to adapt to evolving misinformation tactics and improve performance over time. By leveraging the findings and methodologies from this study, automated fact-checking systems can become more robust, reliable, and effective in combating misinformation in the digital landscape.