A Survey on Automated Fact-Checking (2022)

Abstract: Fact-checking has become increasingly important due to the speed with which both information and misinformation can spread in the modern media ecosystem. Therefore, researchers have been exploring how fact-checking can be automated, using techniques based on natural language processing, machine learning, knowledge representation, and databases to automatically predict the veracity of claims. In this paper, we survey automated fact-checking stemming from natural language processing, and discuss its connections to related tasks and disciplines. In this process, we present an overview of existing datasets and models, aiming to unify the various definitions given and identify common concepts. Finally, we highlight challenges for future research.

## introduction fact-checking is the task of assessing whether claims made in written or spoken language are true. this is an essential task in journalism, and is commonly conducted manually by dedicated organizations such as politifact. in addition to external fact-checking, internal fact-checking is also performed by publishers of newspapers, magazines, and books prior to publishing in order to promote truthful reporting. figure 1 shows an example from politifact, together with the evidence (summarized) and the verdict. fact-checking is a time-consuming task. to assess the claim in figure 1 , a journalist would need to search through potentially many sources to find job gains under trump and obama, evaluate the reliability of each source, and make a comparison. this process can take professional factcheckers several hours or days @xcite . compounding the problem, fact-checkers often work under strict and * equal contribution. automating the fact-checking process has been discussed in the context of computational journalism @xcite @xcite , and has received significant attention in the artificial intelligence community. @xcite proposed structuring it as a sequence of components-identifying claims to be checked, finding appropriate evidence, producing verdicts -that can be modeled as natural language processing (nlp) tasks. this motivated the development of automated pipelines consisting of subtasks that can be mapped to tasks well-explored in the nlp community. advances were made possible by the development of datasets, consisting of either claims collected from fact-checking websites, for example liar @xcite , or purposemade for research, for example, fever @xcite . a growing body of research is exploring the various tasks and subtasks necessary for the automation of fact checking, and to meet the need for new methods to address emerging challenges. early developments were surveyed in @xcite , which remains the closest to an exhaustive overview of the subject. however, their proposed framework does not include work on determining which claims to verify (i.e., claim detection), nor does their survey include the recent work on producing explainable, convincing verdicts (i.e., justification production). several recent papers have surveyed research focusing on individual components of the task. @xcite and @xcite focus on identifying rumors on social media, @xcite and @xcite on detecting the stance of a given piece of evidence towards a claim, and @xcite on producing explanations and justifications for fact-checks. finally, @xcite surveyed automated approaches to assist factchecking by humans. while these surveys are extremely useful in understanding various aspects of fact-checking technology, they are fragmented and focused on specific subtasks and components; our aim is to give a comprehensive and exhaustive birds-eye view of the subject as a whole. a number of papers have surveyed related tasks. @xcite and @xcite surveyed work on fake news, including descriptive work on the problem, as well as work seeking to counteract fake news through computational means. a comprehensive review of nlp approaches to fake news detection was also provided in @xcite . however, fake news detection differs in scope from fact checking, as the former focuses on assessing news articles, and includes labeling items based on aspects not related to veracity, such as satire detection @xcite . furthermore, other factors-such as the audience reached by the claim, and the intentions and forms of the claim-are often considered. these factors also feature in the context of propaganda detection, recently surveyed by @xcite . unlike these efforts, the works discussed in this survey concentrate on assessing veracity of general-domain claims. finally, @xcite and da @xcite surveyed research on fake news detection and fact checking with a focus on social media data, while this survey covers fact checking across domains and sources, including newswire, science, etc. in this survey, we present a comprehensive and up-to-date survey of automated fact-checking, unifying various definitions developed in previous research into a common framework. we begin by defining the three stages of our fact-checking framework-claim detection, evidence retrieval, and claim verification, the latter consisting of verdict prediction and justification production. we then give an overview of the existing datasets and modeling strategies, taxonomizing these and contextualizing them with respect to our framework. we finally discuss key research challenges that have been addressed, and give directions for challenges that we believe should be tackled by future research. we accompany the survey with a repository, foot_0 which lists the resources mentioned in our survey. ## task definition figure 2 shows a nlp framework for automated fact-checking consisting of three stages: (i) claim detection to identify claims that require verification; (ii) evidence retrieval to find sources supporting or refuting the claim; and (iii) claim verification to assess the veracity of the claim based on the retrieved evidence. evidence retrieval and claim verification are sometimes tackled as a single task referred to as factual verification, while claim detection is often tackled separately. claim verification can be decomposed into two parts that can be tackled separately or jointly: verdict prediction, where claims are assigned truthfulness labels, and justification production, where explanations for verdicts must be produced. ## datasets datasets can be analyzed along three axes aligned with three stages of the fact-checking framework (figure 2 ): the input, the evidence used, and ver-dicts and justifications that constitute the output. in this section we bring together efforts that emerged in different communities using different terminologies, but nevertheless could be used to develop and evaluate models for the same task. ## modeling strategies we now turn to surveying modeling strategies for the various components of our framework. the most common approach is to build separate models for each component and apply them in pipeline fashion. nevertheless, joint approaches have also been developed, either through end-to-end learning or by modeling the joint output distributions of multiple components. ## research challenges choice of labels the use of fine-grained labels by fact-checking organizations has recently come under criticism @xcite . in-between labels like ''mostly true'' often represent ''meta-ratings'' for composite claims consisting of multiple elementary claims of different veracity. for example, a politician might claim improvements to unemployment and productivity; if one part is true and the other false, a fact-checker might label the full statement ''half true''. noisy labels resulting from composite claims could be avoided by intervening at the dataset creation stage to manually split such claims, or by learning to do so automatically. the separation of claims into truth and falsehood can be too simplistic, as true claims can still mislead. examples include cherry-picking, where evidence is chosen to suggest a misleading trend (asudeh et al., 2020), and technical truth, where true information is presented in a way that misleads (e.g., ''i have never lost a game of chess'' is also true if the speaker has never played chess). a major challenge is integrating analysis of such claims into the existing frameworks. this could involve new labels identifying specific forms of deception, as is done in propaganda detection @xcite , or a greater focus on producing justifications to show why claims are misleading @xcite . sources and subjectivity not all information is equally trustworthy, and sometimes trustworthy sources contradict each other. this challenges the assumptions made by most current fact-checking research relying on a single source considered authoritative, such as wikipedia. methods must be developed to address the presence of disagreeing or untrustworthy evidence. recent work proposed integrating credibility assessment as a part of the fact-checking task @xcite . this could be done, for example, by assessing the agreement between evidence sources, or by assessing the degree to which sources cohere with known facts @xcite @xcite . similarly, check-worthiness is a subjective concept varying along axes including target audience, recency, and geography. one solution is to focus solely on objective checkability @xcite . however, the practical limitations of fact-checking (e.g., the deadlines of journalists and the time-constraints of media consumers) often force the use of a triage system @xcite . this can introduce biases regardless of the intentions of journalists and system-developers to use objective criteria @xcite . addressing this challenge will require the development of systems allowing for real-time interaction with users to take into account their evolving needs. dataset artefacts and biases synthetic datasets constructed through crowd-sourcing are common @xcite @xcite . it has been shown that models tend to rely on biases in these datasets, without learning the underlying task @xcite @xcite . for fact-checking, @xcite showed that the predictions of models trained on fever @xcite were largely driven by indicative claim words. the fever 2.0 shared task explored how to generate adversarial claims and build systems resilient to such attacks @xcite . alleviating such biases and increasing the robustness to adversarial examples remains an open question. potential solutions include leveraging better modeling approaches @xcite @xcite , collecting data by adversarial games @xcite , or context-sensitive inference @xcite . multimodality information (either in claims or evidence) can be conveyed through multiple modalities such as text, tables, images, audio, or video. though the majority of existing works have focused on text, some efforts also investigated how to incorporate multimodal information, including claim detection with misleading images @xcite , propaganda detection over mixed images and text @xcite , and claim verification for images @xcite . @xcite argued that rumors should be seen as signals propagating through a social network. rumor detection is therefore inherently multimodal, requiring analysis of both graph structure and text. available multimodal corpora are either small in size @xcite or constructed based on distant supervision @xcite . the construction of large-scale annotated datasets paired with evidence beyond metadata will facilitate the development of multimodal fact-checking systems. multilinguality claims can occur in multiple languages, often different from the one(s) evidence is available in, calling for multilingual fact-checking systems. while misinformation spans both geographic and linguistic boundaries, most work in the field has focused on english. a possible approach for multilingual verification is to use translation systems for existing methods @xcite , but relevant datasets in more languages are necessary for testing multilingual models' performance within each language, and ideally also for training. currently, there exist a handful of datasets for factual verification in languages other than english @xcite @xcite @xcite , but they do not offer a cross-lingual setup. more recently, @xcite introduced a multilingual dataset covering 25 languages, but found that adding training data from other languages did not improve performance. how to effectively align, coordinate, and leverage resources from different languages remains an open question. one promising direction is to dis-till knowledge from high-resource to low-resource languages @xcite . faithfulness a significant unaddressed challenge in justification production is faithfulness. as we discuss in section 4.3, some justifications -such as those generated abstractively @xcite )-may not be faithful. this can be highly problematic, especially if these justifications are used to convince users of the validity of model predictions @xcite . faithfulness is difficult to evaluate for, as human evaluators and human-produced gold standards often struggle to separate highly plausible, unfaithful explanations from faithful ones @xcite ). in the model interpretability domain, several recent papers have introduced strategies for testing or guaranteeing faithfulness. these include introducing formal criteria that models should uphold @xcite , measuring the accuracy of predictions after removing some or all of the predicted non-salient input elements @xcite @xcite , or disproving the faithfulness of techniques by counterexample @xcite . further work is needed to develop such techniques for justification production. ## conclusion we have reviewed and evaluated current automated fact-checking research by unifying the task formulations and methodologies across different research efforts into one framework comprising claim detection, evidence retrieval, verdict prediction, and justification production. based on the proposed framework, we have provided an extensive overview of the existing datasets and modeling strategies. finally, we have identified vital challenges for future research to address.