Automated Fact-Checking for Assisting Human Fact-Checkers (2103.07769v2)

Abstract: The reporting and the analysis of current events around the globe has expanded from professional, editor-lead journalism all the way to citizen journalism. Nowadays, politicians and other key players enjoy direct access to their audiences through social media, bypassing the filters of official cables or traditional media. However, the multiple advantages of free speech and direct communication are dimmed by the misuse of media to spread inaccurate or misleading claims. These phenomena have led to the modern incarnation of the fact-checker -- a professional whose main aim is to examine claims using available evidence and to assess their veracity. As in other text forensics tasks, the amount of information available makes the work of the fact-checker more difficult. With this in mind, starting from the perspective of the professional fact-checker, we survey the available intelligent technologies that can support the human expert in the different steps of her fact-checking endeavor. These include identifying claims worth fact-checking, detecting relevant previously fact-checked claims, retrieving relevant evidence to fact-check a claim, and actually verifying a claim. In each case, we pay attention to the challenges in future work and the potential impact on real-world fact-checking.

Automated Fact-Checking for Assisting Human Fact-Checkers

The landscape of information dissemination has rapidly evolved due to advancements in digital communication, particularly through social media. This transition brings about new forms of journalism, extending from traditional frameworks to citizen-based reporting avenues. Such transformation, while democratizing information access, has also amplified the propagation of misinformation and disinformation—a considerable issue concerning today's communication channels. The discussed paper explores automated fact-checking mechanisms, designed to aid human fact-checkers in managing and verifying the overwhelming flux of information across these platforms.

Research Objectives

The paper serves to identify and evaluate intelligent technologies that can complement human efforts in fact-checking processes. Embedded within the scope of text forensics, the paper advocates for automated systems capable of tackling several key tasks: identifying claims that warrant verification, detecting claims that have been previously fact-checked, retrieving pertinent evidence, and facilitating the verification of claims.

Key Challenges and Proposed Solutions

1. Finding Claims Worth Fact-Checking: The paper highlights that discerning which claims are significant enough to verify presents a primary challenge due to the sheer volume of information. The integration of classification models like BERT and the development of scoring mechanisms to prioritize based on check-worthiness aim at alleviating the strain on humans.
2. Detecting Previously Fact-Checked Claims: Employing machine learning and natural language processing to segregate novel claims from those already verified can significantly enhance fact-checking efficiency. The paper outlines methodologies for indexing claims and their frequent checks in multiple languages, tailored to identify reposts across various contexts and mediums.
3. Evidence Retrieval: Automated retrieval systems are designed to seek and present supporting evidence to back or refute claims. This involves leveraging information retrieval techniques optimized through relevance metrics like BM25 or employing transformer models to cross-validate data with external structured sources such as Wikipedia entries.
4. Automated Verification: The prospect of fully automated verification remains speculative due to credibility and comprehensiveness issues inherent in the current state of AI. The paper insists on the necessity of explainable systems that provide evidence-based judgments to assist rather than replace human oversight.

Implications and Future Directions

The progression of automated fact-checking technologies holds substantial implications for political communication, media literacy, and public confidence in information authenticity. On a practical level, establishing efficient frameworks for processing fact-check activities can enhance the scope and speed of verification practices across multiple languages and media types.

The authors advocate for a collaborative approach involving AI practitioners and fact-checking professionals to bridge technological gaps and develop more credible automated systems. Challenges such as system bias, multilingual claim verification, and incorporation of multimodal data indicate rich areas for future research and application. Besides expanding the accuracy and transparency of such systems, integrating AI fact-checking tools seamlessly into existing media platforms remains a formidable challenge, one that necessitates advancements in user interface design to support user-friendly deployment.

In conclusion, this paper underscores an evolving research domain, suggesting practical implementations and identifying barriers that necessitate further academic and industry efforts to refine automated fact-checking solutions that can bolster human capabilities in sifting through the torrent of information shaping public discourse today.