Multimodal Automated Fact-Checking: A Survey (2023)

Abstract: Misinformation is often conveyed in multiple modalities, e.g. a miscaptioned image. Multimodal misinformation is perceived as more credible by humans, and spreads faster than its text-only counterparts. While an increasing body of research investigates automated fact-checking (AFC), previous surveys mostly focus on text. In this survey, we conceptualise a framework for AFC including subtasks unique to multimodal misinformation. Furthermore, we discuss related terms used in different communities and map them to our framework. We focus on four modalities prevalent in real-world fact-checking: text, image, audio, and video. We survey benchmarks and models, and discuss limitations and promising directions for future research

## introduction motivated by the challenges presented by misinformation in the modern media ecosystem, previous research has commonly modelled automated factchecking (afc) as a pipeline consisting of different stages, surveyed in a variety of axes @xcite @xcite @xcite . however, these surveys focus on a single modality, text. this is different to real-world misinformation that often occurs via several modalities. in afc, the term multimodal has been used to refer to cases where the claim and/or evidence are expressed through different or multiple modalities @xcite @xcite . examples of multimodal misinformation include: (i) claims about digitally manipulated content @xcite such as photos depicting former us president trump's arrest (figure 1 ); (ii) combining content from different modalities and contexts, e.g. using video footage in a misleading context @xcite @xcite ; (iii) embedding a claim in another modality, e.g. a meme, an image with embedded text @xcite , with notable real-world examples including a brexit vote leave poster foot_0 and tiktok videos with covid misinformation @xcite ; (iv) verifying a claim with evidence from a different modality than the input claim, e.g. verifying images against text @xcite , audio against textual metadata @xcite , and text against images @xcite . fact-checking multimodal misinformation is important for a number of reasons. first, multimodal content is perceived as more credible compared to text containing a similar claim @xcite . for example, previous research shows that visual content exhibits a "photo truthiness"effect @xcite , biasing readers to believe a claim is true. second, multimodal content spreads faster and has a higher engagement than text-only posts @xcite . third, with recent advances in generative machine learning models @xcite , the generation of multimodal misinformation has been simplified. to validate the importance of multimodal factchecking, we manually annotated 9,255 claims from the averitec dataset @xcite , which were collected with the google factcheck claimreview api foot_1 . for each claim, we identified the modalities present in it and evidence strategies (e.g. identification of geolocation) used for fact-checking. we find that more than 2, 600 (28.68%) claims either contain multimodal data or require multimodal reasoning for verification, with 20.07% involving images, 8.06% videos, and 0.55% audios (see in this survey, we introduce a three-stage framework for multimodal automated fact-checking: claim detection and extraction, evidence retrieval, and verdict prediction encompassing veracity, manipulation and out-of-context classification, as well as justification production. the input and output data of each stage can have different or multiple modalities. for each stage, we discuss related terms and definitions developed in different research communities. in contrast to previous surveys on multimodal fact-checking that focus on individual subtasks @xcite @xcite , we consider all subtasks surveying benchmarks and modeling approaches for them. we focus on four prevalent modalities of realworld fact-checking identified in our annotations: text, image, audio, and video. while tables and knowledge graphs are increasingly used as evidence for benchmarks @xcite @xcite , they have been covered in previous surveys @xcite @xcite . finally, we discuss the extent to which current approaches to afc work for multimodal data, and promising directions for further research (section 4). we accompany the survey with a repository, foot_3 which lists the resources mentioned in our survey. ## task formulation this section introduces a conceptualisation of multimodal afc as a three-stage process, including claim detection and extraction, evidence retrieval, and production of verdicts and justifications for various types of misinformation (figure 2 ). compared to the text-only pipeline presented in @xcite , our framework extends their first stage with a claim extraction stage, and generalises their third stage to cover tasks that fall under multimodal afc, thus accounting for its particular challenges. terminology. a number of works @xcite use the term multimedia, which is also more common in public discussions instead of multimodal @xcite . however in in this survey we adopt the latter, following other surveys that use multimodal data @xcite . adopting the terminology of previous surveys @xcite and following advice from institutions such as the uno @xcite , we avoid multimodal fake news @xcite @xcite due to the term's ambiguous use. stage 1: claim detection and extraction. the first pipeline stage aims to find checkable (i.e. factually-verifiable) and check-worthy (i.e. important factual claims @xcite ) claims. debunking a typical claim and writing the factchecking article takes approximately one day for a human fact-checker @xcite . this stage aims to focus the afc process on claims which are verifiable and most impactful. multimodal claims can be diverse and include: (1) a written claim embedded in another modality @xcite such as an image or a spoken claim in an audio or video; (2) a claim that a piece of content is authentic, e.g. that a video footage is from a specific geographic location @xcite ; (3) a claim for which the evidence is manipulated to support it, e.g. through lip-syncing @xcite . while in some cases the claim is clearly specified (e.g. in form of a headline), in often multiple modalities are required to understand and extract a claim at this stage. simply detecting potentially misleading content is often not enough -it is necessary to extract the claim before fact-checking it in the subsequent stages. for example, detecting text in images or videos and understanding it given the context @xcite or verifying audios by transcribing and extracting claims @xcite . stage 2: evidence retrieval. similarly to factchecking with text, multimodal fact-checking often relies on evidence to make judgments, similar to the process followed by human fact-checkers @xcite . two main approaches have been used in the past: (i) using the claim to-be-checked as evidence itself, e.g. to detect manipulation @xcite ; this can be seen as the multimodal version of evidence-free fact-checking of text claims by checking logical fallacies in the text @xcite , and (ii) retrieving additional evidence @xcite . in multimodal fact-checking, the evidence modality can be different from the claim modality. for example, to retrieve evidence for image or audio fact-checking, previous works have also used text e.g. metadata, social media comments, or captions @xcite @xcite . stage 3: verdict prediction and justification production. following the fact-checking process of professional fact-checkers, the final stage comprises verdict prediction and the production of justification that explains the fact-check to humans @xcite . verdict prediction is decomposed into three tasks considering prevalent multimodal misinformation types: manipulation, using content out-of-context, and veracity classification. stage 3.1: manipulation classification. manipulation classification commonly addresses (i) misinformative claims with manipulated content; stage 3.2: out-of-context classification. using unchanged content out-of-context is one of the most common and easiest methods to create multimodal misinformation @xcite , and involves (possibly misinformative) textual claims paired with content (e.g. a video) taken out of context @xcite @xcite . recent work has also studied the applicability of traditional multimodal misinformation detection methods to identify out-of-context content @xcite . other terms used for combining multimodal content in a misleading way include cross-modal (in-) consistency @xcite and repurposing @xcite . stage 3.3: veracity classification. this task is the multimodal counterpart to classifying the veracity of textual claims given retrieved evidence @xcite . veracity classification of claims embedded in audio is also commonly referred to as deception detection @xcite . while earlier work considered mostly claims recorded in staged setups @xcite or from court trials @xcite , more recently real-world political debates have become popular. @xcite . stage 3.4: justification production. different to previous research on automated justification production @xcite , human factcheckers also give justifications for fact-checks involving images, audios, or videos @xcite . justifications for multimodal misinformation can be grouped in three categories: (i) identifying which part of the claim input is misleading (e.g. specific areas in a visual claim or words in a textual one) @xcite @xcite ; (ii) providing natural language justifications following human fact-checkers @xcite ; (iii) selecting and highlighting evidence parts used for verification @xcite . justifications serve purposes beyond explaining veracity classification, e.g. human fact-checkers also use them to discuss uncertainties and potential errors -especially needed in fact-checking for rapidly developing events @xcite . ## challenges and future directions claim extraction from multimodal content. multimodal claims, e.g. manipulated videos, are often embedded in specific contexts and framed as (part of) larger stories. for example, countering the misinformation in figure 1 requires not only classifying if the image is manipulated, but understanding that it depicts the arrest of the former president in one of the cases he is being charged in. only then can relevant evidence data be extracted and used to verify the story of trump's arrest.to determine what is being claimed is a challenging first step in multimodal automated fact-checking. however, current efforts for multimodal claim extraction are limited to text extraction from visual content or transcribing audios and videos @xcite @xcite . addressing this challenge will require modeling approaches to effectively align and integrate all modalities present in and around the claim. for example, methods for pixel-based language modeling have recently been introduced to better align visually situated language with image content @xcite . such approaches considering modalities beyond text and vision for multimodal data alignment can be useful for claim extracting from multimodal input. ## conclusion we survey research on multimodal automated factchecking and introduce a framework that combines and organizes tasks introduced in various communities studying misinformation. we discuss common terms and definitions in context of our framework. we further study popular benchmarks and model-ing approaches, and discuss promising directions for future research.