Multimodal Automated Fact-Checking: A Survey
Authors: Mubashara Akhtar
Michael Sejr Schlichtkrull
Zhijiang Guo
Oana Cocarascu
Elena Simperl
Andreas Vlachos
Venue: EMNLP 2023 Findings (2023)
Keywords: fact checking
multimodality
survey
TL;DR: This survey on multimodal automated fact-checking introduces a framework for multimodal fact-checking and studies datasets and modeling approaches for fact-checking with text, image audio, and video.

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

Multimodal Automated Fact-Checking: A Survey
Mubashara Akhtar Michael Schlichtkrull", Zhijiang Guo?, Oana Cocarascu', Elena Simperl' and Andreas Vlachos
'Department of Informatics, King's College London Depaatmentt of Computer Science and Technology, University of Cambridge
{mubashara. oana. cocarascu, elena. simper j@kl. j ac. {mss84, zg283, 3088 @aam ac.
Abstract
Misinformation often conveyed in multiple modalities, c.g, miscaptioned image. Mul- timodal misinformation is perceived as more credible by humans, and spreads faster than its text-only counterparts. While increas- ing body of research investigates automated fact-checking (AFC), previous surveys mostly focus on text. In this survey, we conceptualise framework for AFC including subtasks unique multimodal misinformation. Furthermore, we discuss related terms used in different com- munities and map them our framework. We focus on four modalities prevalent in real-world fact-checking: text, image, audio, and video. We survey benchmarks and models, and dis- cuss limitations and promising directions for future research.
FAKE
Figure 1: Manipulated image depicting arrest of former US president Donald Trump (source: BBC!).
content from different modalities and contexts, e.g. using video footage in misleading context (Aneja et al., 2021; Biamby et al., 2022; Abdelnabi et al., 2022); (fii) embedding claim in another modal- ity, e.g. meme, an image with embedded text (Qu et al., 2022a), with notable real-world examples including a Brexit Vote Leave poster" and Tik Tok videos with COVID misinformation (Shang et al., 2021); (iv) verifying a claim with evidence from a different modality than the input claim, e.g. verify- ing images against text (Shao et al., 2023), audio against textual metadata (Kopev al., 2019), and text against images (Akhtar et al., 2023).
1 Introduction
Motivated by the challenges presented by misinfor- mation the modern media ecosystent stem, previous research has commonly modelled automated fact- checking (AFC) as pipeline consisting of different stages, surveyed in variety of axes (Thorne and Vlachos, 2018; Kotonya and Toni, 2020a; Zeng al., 2021; Nakov et al., 2021; Guo al., 2022). However, these surveys focus on single modality, text. This different to real-world misinformation that often occurs via several modalities.
Fact-checking multimodal misinformation is im- portant for a number of reasons. First, multimodal content is perceived as more credible compared to text containing a similar claim (Newman et al., 2012). For example, previous research shows that visual content exhibits "photo truthiness". effect (Newman and Zhang, 2020), biasing readers to believe a claim true. Second, multimodal con- tent spreads faster and has a higher engagement than text-only posts (Li and Xie, 2020). Third, with recent advances in generative machine learn- ing models (Rombach et al., 2022), the generation of multimodal misinformation has been simplified. To validate the importance of multimodal fact-
In AFC, the term multimodal has been used to refer to cases where the claim and/or evidence are expressed through different or multiple modali- ties (Hameleers et al., 2020; Alam et al., 2022; Biamby al., 2022). Examples of multimodal misinformation include: (i) claims about digitally manipulated content (Agarwal al., 2019; Rössler et al., 2018) such as photos depicting former US president Trump's 's arrest (Figure 1); (fi) combining
This work was partially done during Mubashara's re- search visit at Cambridge.
'tttps:/ /www. bbc. com/news/ wor world-us- uaald--u--caada---e canada-65069311
Ptttps: //ts:://wwww. itv wwt.ccm//ewws//0019000000 boris- jownson-under--aeeeeee hainssoo-umdee-attttt------eeeee atw.catt--------rrkeeeee clani/

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Claim Modality
Percentage
survey with repository. which lists the resources mentioned in our survey.
Image
20.07%
Video
8.06%
Audio
0.55%
Task Formulation
Total
28.68%
This section introduces conceptualisation of mul- timodal AFC as a three-stage process, including claim detection and extraction, evidence retrieval, and production of verdicts and justifications for var- ious types of misinformation (Figure 2). Compared to the text-only pipeline presented in Guo et al. (2022), our framework extends their first stage with claim extraction stage, and generalises their third stage to cover tasks that fall under multimodal AFC, thus accounting for its particular challenges.
Table I: Non-textual modalities present and/or used in addition to text in manually annotated snapshot of real-world claims from the Google ClaimReview API.
checking, we manually annotated 9,255 claims from the AVeriTeC dataset (Schlichtkrull et al.. 2023), which were collected with the Google FactCheck ClaimReview API 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 Table 1).* These claims can neither be fact-checked by text-only model, nor by a model with no text capabilities.
Terminology. A number of works (Singhal et al., 2022; Fung et al., 2021) use the term multimedia, which is also more common in public discussions instead of multimodal (Lauer, 2009). However in in this survey we adopt the latter, following other surveys that use multimodal data (Liang et al., 2022: Guo et al., 2019). Adopting the terminology of previous surveys (Thorne and Vlachos, 201 8; Alam et al., 2022) and following advice from insti- tutions such the UNO (Treton and Posetti, 2018) 8), we avoid multimodal fake news (Meel and Vish- wakarma, 2021; Amri al., 2021; Patwa et al., 2022) due to the term's ambiguous use.
In this survey, we introduce a shree-stage frame- work for multimodal automated fact-checking: claim detection and extraction, evidence retrieval, and verdict prediction encompassing veracity, ma- nipulation and out-of-context classification, well 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 com- munities. In contrast to previous surveys on multi- modal fact-checking that focus on individual sub- tasks (Cao et al., 2020; Alam et al., 2022; Abdali, 2022), we consider all subtasks surveying bench- marks and modeling approaches for them.
Stage 1: Claim Detection and Extraction. The first pipeline stage aims find checkable (i.e. factually-verfffablee) and check-worthy (i.e. impor- tant factual claims (Hassan et al., 2015b)) claims. Debunking a typical claim and writing the fact- checking article takes approximately one day for a human fact-checker (Hassan et al., 2015a). This stage aims to focus the AFC process on claims which are verifiable and most impactful. Multi- modal claims written claim embedded in another modality (Prab- hakar et al., 2021: Maros et al., 2021) such as an image or spoken claim in an audio or video; (2) a claim that piece of content is authentic, e.g. that a video footage is from a specific geographic loca- tion (Zhang et al., 2018; Heller et al., 2018); (3) claim for which the evidence is manipulated to support it, e.g. through lip-syncing (Rössler et al., 2018). While in some cases the claim is clearly specified (e.g. in form of a headline), often mul- tiple modalities are required to understand and ex-
We focus on four prevalent modalities of real- world fact-checking identified in our annotations: text, image, audio, and video. While tables and knowledge graphs are increasingly used as evi- dence for benchmarks (Chen et al., 2020; Aly et al., 2021; Akhtar al., 2022), they have been covered in previous surveys (Thorne and Vlachos, 2018; Zeng et al., 2021; Guo et al., 2022). Finally, we dis- cuss the extent to which current approaches to AFC work for multimodal data, and promising directions for further research (Section 4). We accompany the
be diverse and include: (1)
'Annotations at AVeriTeC.
htttps: //gi thub. com/Cartus/ Automated- Fact-Checkk -eeoucneee-eeesureeeee

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Input
Stage Claim Detection & Extraction
Stage 2: Evidence Retrieval
Stage Verdict Prediction Justification Production
Text
Evidence
Image
Factually-Verifabble
Visual Evidence
Veracity
Video
Check-Worthy
Audible Evidence
Evidence
Manipuistion
Audio
Out-of-context
Figure 2: Multimodal fact-checking pipeline.
tract claim this stage. Simply detecting poten- tially misleading content is often not enough 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 (Qu et al., 2022b) or verifying audios by transcribing and extracting claims (Maros et al., 2021).
ods exist manipulate text, visual and audio con- tent. While some require more knowledge to use (e.g. speech synthesis), other manipulations can be achieved with simple tools (c.g. changing speed of videos) (Paris and Donovan, 2019). Different terms have been used for manipulated content in recent years. A deepfake is commonly defined as "the product of artificial intelligence (Al) applications that [-..] create fake videos that appear authen- tic" (Maras and Alexandrou, 2019), with popular examples including realistic-looking videos where the speaker's voice or face is modified (Paris and Donovan, 2019). On the other hand, cheap fake defines manipulated content created through more accessible methods (Paris and Donovan, 2019), e.g. changing captions or speed of videos (La et al., 2022). The term fauxtography was first coined in journalism for images manipulated to "convey a questionable (or outright false) sense of the events they seem depict" (Cooper, 2007; Kalb and Saivetz, 2007). Other terms used in the litera- ture for manipulated content are fake (Cheema et al., forgery (Cozzolino et al., 2021), and splice (Zampoglou et al., 2015).
Stage 2: Evidence Retrieval. Similarly fact- checking with text, multimodal fact-checking of- ten relies on evidence to make judgments, similar to the process followed by human fact-checkers (Silverman, 2013; Nakov et al., 2021). Two main approaches have been used in the past: (i) using the claim to-be-checked evidence itself, c.g. to detect manipulation (Qi al., 2019; Bonettini et al., 2020): this can be seen as the multimodal version of evidence-fres fact-checking of text claims by checking logical fallacies in the text (Jin et al., 2022), and (ti) retrieving additional evidence (Ab- delnabi et al., 2022). In multimodal fact-checking, the evidence modality can be different from the claim modality. For example, retrieve evidence for image audio fact-checking, previous works have also used text text e.g. metadata, social media com- ments, or captions (Gupta et al., 2013; Huh al., 2018; Müller-Budack et al., 2020; Kopev et al., 2019).
Stage 3.2: Out-of-context Classification. Us- ing unchanged content out-of-context is one of the most common and casiest methods to cre- ate multimodal misinformation (Luo et al., 2021; Aneja et al., 2021), and involves (possibly mis- informative) textual claims paired with content (e.g. a video) taken out of context (Zhang et al., 2018; 8; Abdelnabi et al., 2022: Garimella and Eck- les, 2020). Recent work has also studied the ap- plicability of traditional multimodal misinforma- tion detection methods to identify out-of-contex content (Zhang et al., 2023). Other terms used for combining multimodal content in a misleading way include cross-modal (in-) consistency (Müller- Budack et al., 2020) and repurposing (Luo et al., 2021).
Stage 3: Verdict Prediction and Justification Production. Following the fact-checking pro- cess of professional fact-checkers, the final stage comprises verdict prediction and the production of justification that explains the fact-check to hu- mans (Graves, 2018). Verdict prediction is decom- posed into three tasks considering prevalent multi- modal misinformation types: manipulation, using content out-of-context, and veracity classification.
Manipulation Classification. Ma- nipulation classification commonly addresses (i) misinformative claims with manipulated content; (ii) correct claims accompanied by manipulated content (e.g. to increase credibility). Many meth-
Stage 3.1:
Stage 3.3: Veracity Classification. This task is the multimodal counterpart to classifying the ve- racity of textual claims given retrieved evidence

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(Thorne and Vlachos, 2018), Veracity classification of claims embedded in audio is also commonly re- ferred to as deception detection (Kopev et al., 2019; Kamboj et al., 2021). While earlier work consid- ered mostly claims recorded in staged setups (New- man et ul., 2003) or from court trials iPérez-Rosas et al., 2015), more recently real-world political de- bates have become popular. (Kopev et al., 2019; Kamboj et al., 2021).
tual claims, Cheema et al. (2022) extend the binary labels for textual check-worthiness (Hassan et al., 2b) with images to be considered as well. A tweet considered check-worthy if is potentially harmful, breaking news, or up-to-date.
Modeling Approaches. Detecting claims is chal- lenging task due to the vast number of posts that are published every day. Existing claim detection meth- ods primarily rely on input content since the large volume of potentially check-worthy inputs makes it difficult to retrieve and use evidence. The early multimodal method directly concatenated visual and textual features for detection (Jin al., 2017; Wang al., 2018). However, simple modality fu- sion may not be sufficient capture the complex relationships among multimodal information. As a result, later efforts focused on jointly learning representations across modalities. For instance, Khattar et al. (2019) leverage a variational auto- encoder (Kingma and Welling, 2014) to learn a shared representation of visual and textual content. Various attention mechanisms have also been de- veloped to fuse multimodal representations (Qian et al., 2021; Wu et al., 2021; ;; Liu et al., 2023b; Qi et al., 2023). Another popular approach is to use graph neural networks (Kipf and Welling, 2017) to model the interactions among different modali- ties (Zheng et al., 2022; Sun et al., 2023).
Stage 3.4: Justification Production. Different to previous research on automated justification pro- duction (Kotonya and Toni, 2020a), human fact- checkers also give justifications for fact-checks involving images, audios, or videos (Silverman, 2013). Justifications for multimodal misinforma- tion can be grouped in three categories: (1) identi- fying which part of the claim input is misleading (e.g. specific areas in a visual claim or words in a textual one) (Kou al., 2020; Purwanto al., 2021; Lourenço and Pacs, 2022); (it) providing natural language justifications following human fact-checkers (Yao et al., 2022); (zii) selecting and highlighting evidence parts used for verifica- tion (Atanasova et al., 2020; Shang et al., 2022). Justifications serve purposes beyond explaining veracity classification, e.g. human fact-checkers also use them to discuss uncertainties and poten- tial errors especially needed in fact-checking for rapidly developing events (Silverman, 2013).
Multimodal content can implicitly provide claims, as seen images and videos on social media that often have accompanying text. To ex- tract claims from visual input, OCR systems are commonly used (Garimella and Eckles, 2020; Prab- hakar et al., 2021). Qu et al. (2022b) use Google Vision API to identify text in memes. Claim ex- traction becomes more challenging when dealing with video inputs. Shang al. (2021) address this challenge by extracting captions and audio chunks after sampling vidco frames. These captions and audio chunks were then encoded into representa- tions to guide the visual feature extraction process. For audio inputs, Maros et al. (2021) use Google's Speech-to- Speech-to-Text API to produce transcripts.
3 Datasets and Modeling Approaches
3.1 Stage 1: Claim Detection and Extraction
Input. Typical inputs to claim detection are uni- modal, including image (Garimella and Eckles, 2020; Qu et al., 2022a), audio (Maros et al., 2021), and video (Shang et al., 2021; Qi et al., 2022). which are collected from social media platforms such WhatsApp and TikTok (see Table 2). The written spoken claim is extracted from the input at this stage before fact-checking it.
Output. Claim detection is typically framed as a classification task. Models predict if a claim is checkable or check- (Prabhakar et al., 2021; Cheema et al., 2022; Barrón-Cedeñno et al., 2023). The verdiet for ten binary (Jin et al., 2017; Shang al., 2021).
3.2 Stage 2: Evidence Retrieval
is of-
Previous work uses different types of evidence and retrieval methods given the modalities involved. Evidence data and retrieval approaches can be grouped into (i) content-based and (ii) retrieval- based (see column evidence in Table 3).
For check-worthiness, Prabhakar al. (2021) defines three categories of multimodal claims:
statistical/nummrical claims, claims about world events/laaee/noteworrty individuals, and other fac-
Content-based. Content-based approaches use the

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Dataset Weiho (Jin et al., 2017)
Input
Context
Oulput
#Inpul
Lang
Source
Txg/Txt
Meta
2
9,528
Zh Weibo/News
FauxBuster (Zhang al., 2018)
Ixt/Txt
TxuMeta
2
917
En Twitte/RRedit
Exfaux (Kou al., 2020)
Ixg/Txt
Txt
2/4
263
En TwitterReedit
MuMIN (Nielsen and McConville, 2022)
Txg/Txt
Meta
12,914
Mul Twitter
MMClaims (Cheema et al., 2022)
Txg/Txt
4
3,400
En Twitter
ContrastFaux (Zong et al., 2023)
Ixg/Txt
2
1,841
En Twiter/Reddit
CIEFF203 (Banrón-Codeñlo al., 2023)
Img/Txt
6,000
Mul Twitter
MR2 (Hu et al., 2023)
Tmg/Txt
Txt/Img/Meta
3
14,700
Mul Twitter/Weibs
IndiaWApp (Garimella and Eckles, 2020)
Img
Meta
2 2,500
Mul WhatsApp
DisinfoMeme (Qu ul., 2022a)
Img
2
1,170
En Reddit
WhatsApp (Maros et ul., 2021)
Aud
Meta
2
42,689
Pt
WhatsApp
Tok (Shang et al., 2021)
Vid
TxUMeta
2
891
En TikTok
COVID VTS (Liu et ul., 2023a)
Vid
Txt/Aud
2
10,000
En Twitter
FakeSV (Qi ul., 2022)
Vid
TxUMeta
2
3,654
Zh
TikTok/Kuai
MisDissem (Resende et al., 2019)
Vid/Aud//mm/Teex Vid/Img/Text
Meta
2
121,781
PL
WhatsApp
CheckMate (Prabhakar et al., 2021)
Meta
3
2,200
Hi
Sharechat
Table 2: Datasets for claim detection. Img, Txt, Vid, Aud, and Meta denote image, text, video, audio, and metadata, respectively. Output indicates the number classification labels. Mul indicates that the input has multiple languages.
claim and its context (i.c. the same information that is used for claim detection and extraction) as evidence instead of retrieving additional data. This is particularly common for audio and video misinformation (Table 3). Acoustic or visual fea- tures extracted from the input are used as evidence for verdict prediction (Wu al., 2015; Yi et al., 2021: Ismael Al Al-Sanjary al., 2016; Jiang et al., 2020). Most approaches use audio (or video) fea- tures and accompanying data (e.g. metadata, tran- scripts if available) as evidence to identify incon- sistencies (Kopev et al., 2019; Rössler et al., 2018; Li et al., 2020b). Several datasets with image/text claims (Tan et al., 2020; Luo et al., 2021; Aneja et al., 2021) also do not retrieve additional evi- dence (Table 3) but rely on the given claim input accompanying metadata (Jaiswal et al., 2017: Sabir al., 2018). Metadata is also often used as evidence for verdict prediction with images as input (Table 3). Jaiswal et al. (2017) and Sabir et al. (2018) use metadata (c.g. image timestamps) to provide additional information. Similarly, Huh et al. (2018) incorporate EXIF metadata (e.g. cam- era version, focal length, resolution settings) to detect manipulation. Image captions are also used evidence sometimes (Shao et al., 2023).
and images. They extract entities/relations from this knowledge graph with an Information Extrac- tion system (Li et al., 2020a; Lin et al., 2020) and map the entities to Freebase (Bollacker et al., 2008) as their background knowledge base. Two recent datasets scrape claims from fact-checking websites, and include textimage/video from those articles as evidence (Singhal et al., 2022; Yao et al., 2022). Akhtar al. (2023) used chart images as evidence to verify textual claims. To determine if an image is used out-of-context, previous works also use (re- verse) image search (Müller-Budce Abdelnabi et al., 2022), to find evidence sources with images similar to or same as the claim image. Müller-Budack et al. (2020) query search engines and the WikiData knowledge graph using named entities from the claim text. Abdelnabi et al. (2022) use the claim image caption and the image itself query.
et al., 2020;
3.3 Stage 3: Verdict Prediction
As introduced in Section 2, the verdict prediction stage includes manipulation, out-oo--ontext, and veracity classification sub-tasks.
Input. As shown in Table 3, inputs of manipula- tion classification datasets usually focus on one modality. For dataset creation, manipulated images are often collected from social media platforms such as Twitter, Reddit, and YouTube (Gupta et al., 2013; Heller et al., 2018). For verdict prediction datasets with videos, in addition to social media (Is- mael Al-Sanjary al., 2016), film clips (Guera and Delp, 2018), facial expressions (Rössler et al., 2018), and interviews (Li et al., 2020b) are used. Some works record videos to simulate worl-world
Retrieval-based. Retrieved evidence external to the claim is mostly used for fact-checking text claims, text/image and image claims while audio and video fact-checks often don't retrieve addi- tional evidence data (Table 3) but rely on the con- tent of the video/audio input. Fung et al. (2021) leverage knowledge base for additional back- ground knowledge. They first construct a knowl- edge graph of the input news article using its text

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Dataset
Input
Evidence
Output
Tasks
#input
Lang
Source
MAIM (Jaiswal at, 2017)
Img/Txr
Metn
2
239,968
Pn
Flickr
MRIR (Sabir et al., 2018)
Img/xxr
Met
2
140.096
Fn
Flickr
TNews (Mulllr-Budher et al., 2020)
Img/To
Img
2
72,561
News
New:400 (Muller- Budack et al., 2020)
Txg
Img
2
400
En/De
News
NeuralNews (Tan et al., 2020)
Ing/Ta
4
128,00 200000
En GrovesGooddNews
COSMOS (Aneja et al., 2021)
Img/Xxt Txt
2
201,700
En
News/Snopes
NowsCLIPPngs (Lao ol., 2021)
Txg/Xtt
988,23 288
En
CLIPIVisuaNNews
InfoSurgeon (Fung et al., 2021)
Txg/
KB/Meta
2
30,000 En
VoA
Factify (Saryavardan et al., 2023b)
Ixg/Ttt
Txt
5
50,000
En Twitter
FakingSandy (Gupts al., 2013)
Img
Txt/Meta
16,117
Twitter
EediaEval (Roididen al. 2014)
Txt/Meta
13,924
Twitter
In-the- Wild (Huh et al., 2018)
Img
Meta
2
M
201
Reddit/Onion
PS. Bartles (Heller al., 2018)
Img
Txt/Mett
2
103.028
Reddir
DGM (Shao et 2023)
Img
Txt
230,000
News
VTD (Ismael Al Sanjary al., 2016)
Vid
33
En
YouTube
Faceforensics (Rösslr et al., 2018)
Vid
2
M
1,004
En
You Tube
DeepfakeDeteet (Guern and Delp. 2018)
600
En
Vid Webs./HOH
DFDC (Dolhansky
2019)
Vid
154
En
Recorded
DesperForensics-11 1.0 (Jiang et al., 2020)
Vid
2
M
60,000
En
Recorded
Celeb-DF (Ii et al., 2020h)
Vid
2 6,229
En
YouTuhe
KoDF (Kwon al., 2021)
237,942
Recerted
DF-Platter (Narayan et al., 2023)
Vid
2
133,260
En
YouTube
ASVspool (Wu et 2015)
Aud
2
M
16,375
En
SAS
Phonespool (Lavrentyeva et al., 2019)
Aud
2
34,407
En
ASVspoof
FoR (Reimao and I'errpos 2019)
Aud
$3,868
Systems
DeepSonar (Wang et al. 2020)
Aud
2
M
18,614
En/Zh
TTS Systems/VCC
HAD (Yi al., 2021)
Aud
M
88,035
Zh
AISHELL-3
FakeAVCeleb (Khalid al., 2021)
Vid/And
20,000
VoxColeb2
MedVideo (Hou
2019)
Vid
VC
250
En
You Tuhe
CLFFF2018 Andio (Kopev et ol., 2019)
Aud
Meta
3
VC
286
Fn
Dehates
FactDril (Singhal al., 2022)
Txt
Vid/Audd/mg/TTiMMet
5
VC
22,435
Mul
PC wehs.
MMM (Gupta 2022)
Img/Meta
10,473
wehs.
ChartFC (Akhiar et al., 2023)
Txt
Img
VC
15,886
En
TabFact
Pauxtography (Zlatkvva at ol., 2019)
Dxt
Meta
2
VC
1,233
En
Snepes/Reuters
MOCHEG Yaa al., 2022)
Img Txt
Img/Txt
3
21,184
En
webs.
d'Fakeddit (Nakamura 2020)
Img/Txt
Meta
2/3/6
OM/VC
1,063,106
En Reddit
Table 3: Datasets for manipulation, out cottee-, and veracity classification. O. M and VC denote oft-of-context,
manipulation and veracity classification, respectively. Mul indicates the input has multiple languages.
scenarios (Dolhansky et al., 2019; Jiang et al., 2020; Kwon et al., 2021). To create datasets of manip- ulated content, altering methods based on GANs have also been applied in earlier works (Zakharov et al., 2019; Nirkin et al., 2019; Karras et al., 2019). For audio manipulations, most benchmarks (Wu al., 2015; Kinnunen al., 2017; Reimao and Tzerpos, 2019; Wang et al., 2020; Yi et al., 2021) use speech synthesis and voice conversion algo- rithms to collect manipulated audios. To assess real-world audio manipulations, Lavrentyeva et al. (2019) emulate realistic telephone channels.
claims include Flickr and news/fact-checking web- sites (Aneja et al., 2021; Jaiswal et al., 2017; Sabir et al., 2018).
The primary input to multimodal veracity clas- sification is the content-to-be-checked itself typ- ically text, audio or video in past benchmarks. Kopev et al. (2019) include verified speeches from the CLEF-2018 Task 2 (Nakov et al., 2018) while Hou et al. (2019) collect videos about prostate can- cer verified by urologists. Zlatkova et al. (2019) and Yao et al. (2022) collect viral images with texts verified by dedicated agencies. Nakamura et al. (2020) collect image-text pairs from Reddit via distant supervision, c.g. labeling a post from the subreddit "fakefacts" as misleading and from "photoshopbattles" as manipulated. For veracity classification of spoken claims, real-world political debates are popular sources for claims (Kopev et al., 2019; Kamboj et al., 2021). For example, Kopev et al. (2019) and Kamboj et al. (2021) use claims labelled by fact checking organizations, and video recordings as well transcripts of the respective political debates.
Most out-of-context classification datasets have image-caption pairs as input (Table 3). Jaiswal et al. (2017) replace captions of Flickr images to get mis- matched pairs. As replacing the entire caption can be easy to detect, later efforts (Sabir et al., 2018; Küillr-Buudcck et al., 2020) propose to change spe- cific entities in them. Luo et al. (2021) show that such text manipulations introduce linguistic biases and can be solved without the images. They use CLIP (Radford et al., 2021) to filter out pairs that do not require multimodal modeling. Popular sources for out of context datasets with text and image
Output. Most manipulation and out-of-context

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classification datasets use binary labels: "out-of- context/not out-of-context" (Müller-Budacck et al., 2020; Luo et al., 2021), "pristine/falsified" (Boi- didou et al., 2014; Heller et al., 2018), "manipula- tion/no manipulation" (aaooon"""DDaaaaaa (Dolhansky et al., 2019; Li et al., 2020b). Following fact-checkers, veracity classification datasets (Singhal et al., 2022; Naka- mura et al., 2020) sometimes employ multi-class labels to represent degrees of truthfulness (e.g. true, mostly-true, half-true) (see Table 3). Mishra al. (2022) adopt labels to denote the entailment be- tween different claim and evidence modalities, e.g. the label support text denotes that only the textual part of the evidence supports the claim but not the accompanying image while support multimodal includes both modalities.
first detect objects in visual content using a Mask R-CNN model (He al., 2017) before extracting visual features (Aneja et al., 2021; La et al., 2022: Shang et al., 2022). Visual representations for im- ages and videos are commonly obtained using CNN models such as ResNet (He et al., 2016; Garimella and Eckles, 2020; Abdelnabi et al., 2022), VGG (Si- monyan and Zisserman, 2015; Jin et al., 2017; Sabir et al., 2018), and Inception (Szegedy al., 2015; Guera and Delp, 2018; Roy and Ekbal, 2021). To obtain audio features for voice quality, loudness, and tonality, Shang et al. (2021) extract the Mcl- frequency cepstral coefficient, Kopev et al. (2019) use the INTERSPEECH 2013 ComParEi feature set (Eyben et al., 2013), and Hou et al. (2019) use the openEAR toolkit (Eyben et al., 2009). Var- ious approaches have been used to obtain mul- timodal representations. Early fusion, which joins representations immediately after the encod- ing step (Baltrusaitis et al., 2019) is more com- mon (Aneja et al., 2021; Tanwar and Sharma, 2020; La et al., 2022) than late fusion (Yao et al., 2022). Moreover, model-agnostic methods (e.g. concate- nation and dot product) are more prevalent (Aneja et al., 2021; Kopev et al., 2019; Jin et al., 2017; La et al., 2022) than model-based approaches (e.g. neural networks) (Jaiswal et al., 2017: Shang et al., 2022). Also popular for out-of-context classifica- tion are pross-modality checks that compare modal- ities present in a claim to each other, e.g. a video and its caption (Müller-Budack et al., 2020; Fung et al., 2021).
Modeling Approaches. To detect visual manip- ulations, early approaches mostly use CNN mod- els, such as VGG16 (Amerini et al., 2019; Dang al., 2020), ResNet (Amerini al., 2019; Sabir et al., 2019), and Inception (Guera and Delp, 2018). Some works extend them to capture tempo- ral aspects of video manipulation classification. Amerini et al. (2019) adopt optical flow fields to capture the correlation between consequent video frames and detect dissimilarities caused by manip- ulation. Guera and Delp (2018) model temporal information with an LSTM model and sequence of features vectors per video frame classify ma- nipulated videos. Sabir et al. (2019) similarly ex- tract features for video frames and detect discrep- ancies between frames using a recurrent convolu- tion network. Some recent models also integrate transformer- based components (Vaswani et al., 2017: Zheng et al., 2021). For example, Wang et al. (2022) combine CNNs and vision transform- ers (ViTs) (Dosovitskiy et al., 2021) while Wodajo and Amafu (2021) introduce multi-scale ViT with variable patch sizes.
3.4 Stage 3: Justification Production
A small number of datasets is available for mul- timodal justification production. Previous work can be grouped into two categories: (1) highlight- ing parts of the input, and (2) generating natural language justifications.
Models for out-of-context and veracity clas- sification typically consist of unimodal encoders, a fusion component to obtain joint, multimodal representations, and a classification component. To obtain text representations, early approaches used combinations of word2vec models (Mikolov et al., 2013), LSTMs (Hochreiter and Schmidhuber, 1997), and TF-IF scores for n-grams (Jin al., 2017; Tanwar and Sharma, 2020: Hou et al., 2019). More recent efforts use pretrained language mod- cls (Fung et al., 2021; Aneja et al., 2021; La et al., 2022). To encode visual data, many approaches
Highlighting Input. The first category highlights input parts as justification which contribute to mod- els' results. A popular approach for this are Graph Neural Networks (Kipf and Welling, 2017). Sev- eral papers encode multimodal data as graph ele- ments, combining entities and their relations in and between modalities. Models are trained to detect inconsistencies between different modalities, or detect relations (i.e., between entities) that may be misinformative. This detection could be based on the local graph structure, or on an external knowl- edge base (Fung et al., 2021; Shang et al., 2022;

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Kou al., 2020). Highlighted entities and relations serve as explanations for the potential misinforma- tiveness of the entire graph. Conversely, Zhou et al. (2018) and Wu et al. (2019) use a multitask model for manipulation classification and identification of manipulated regions. Rather than labeled data, some papers rely on attention mechanisms to high- light areas as explanations. Bonettini et al. (2020); Dang et al. (2020) use this approach to highlight manipulated image regions; Purwanto et (2021) also include captions.
they cannot be easily searched on the web or social media networks (Silverman, 2013). Fact-checkers often use text accompanying the videos to find evi- dence (Silverman, 2013). Reverse image search en- gines, e.g. Google Lens or TinEye, require screen- shots from the video as input and thus require the correct timeframe, which can be challenging to extract. A dedicated adversary can render current tools very difficult to use. Very often evidence for image or audio fact-checking is retrieved using text accompanying them, e.g. metadata, social media comments, or captions (Gupta et al., 2013; Huh et al., 2018; Müller-Budade et al., 2020; Kopev et al.. 2019). While incorporating the textual infor- mation and the other modality (e.g. audio/image) in retrieval would provide more information, this is missing currently. How to best retrieve evidence data that is non-textual or has different modality than the claim, also remains a challenge.
Natural Language Justifications. Yao et al. (2022) recently introduced multimodal dataset with natural language justifications. They scrape text and visual content from web pages referenced by fact-checking articles. The dataset includes sum- maries in the fact-checking articles as gold justifica- tions for the verdicts. However, such a setting not realistic, as fact-checking articles are not available when verifying a new claim.
Multilinguality and multimodality. While there is increasing work on multilingual fact- checking (Gupta and Srikumar, 2021; Shahi and Nandini, 2020; Hammouchi and Ghogho, 2022), it is mostly limited to text-only benchmarks and models. Surveying benchmarks for different pipeline stages (Figure 2), we found limited multimodal datasets for non-English languages (see Table 3). Previous work on multilingual multimodality shows that training and testing on English data alone introduces biases, as models fail to capture concepts and images prevalent in other languages and cultures (Liu et al.. 2021). Moreover, some types of multimodal misinformation exploit cross-lingual sources to mislead, e.g. images or videos from non-English newspapers appearing as out-of-context data for English multimodal misinformation (Silverman, 2013). To prevent false conclusions and biases, it is thus necessary to take approaches that are both multimodal and multilingual (Ruder et al., 2022). Construction of large-scale multimodal, multilingual AFC datasets would facilitate futures research in this direction, similar to benchmarks and shared tasks created for automated fact-checking tasks in English (Thorn et al.. 2018: Suryavardan et al., 2023a).
4 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, counter- ing the misinformation in Figure I 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 ex- traction are limited to text extraction from visual content or transcribing audios and vidcos (Qu et al., 2022b; Garimella and Eckles, 2020; Maros et al., 2021). Addressing this challenge will require mod- cling approaches to effectively align and integrate all modalities present in and around the claim. For example, methods for pixel- language mod- eling have recently been introduced to better align visually situated language with image content (Lee et al., 2022). Such approaches considering modal- ities beyond text and vision for multimodal data alignment can be useful for claim extracting from multimodal input.
Generalizing detection of visual manipula- tions. The recent popularity of diffusion models (DMs) for visual manipulation have raised ques- tions regarding the generalizability of manipula- tion detectors developed for earlier models (e.g.
Multimodal evidence retrieval. Evidence re- trieval for audio and video fact-checking remains a major challenge. Different to other modalities,

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GANs (Goodfellow al., 2020)). Detection mod- els are biased towards specific manipulation mod- els and struggle to generalize (Wu et al., 2023a; Ricker et al., 2022). A recent study (Ricker et al., 2022) shows that detectors initially developed for GANs, have average performance drops of around 15% for image by DMs. While new detection ap- already being
ing approaches, and discuss promising directions for future research.
Limitations
While we cite many datasets and modeling ap- proaches for multimodal fact-checking, we de- scribe most of them only briefly due to space con- straints. Our aim was to provide an overview of multimodal fact-checking and organise previous works in framework. Moreover, the presented survey focuses primarily on four modalities. While there are other modalities could have included, concentrated on those prevalent in real-world fact-checking that have not been discussed as part of a fact-checking framework in previous surveys.
proaches for DM manipulations developed (Guarnera et al., 2023; Wu et al., 2023b), the question how to generalize and increase ro- bustness of manipulation detectors for potential fu- ture manipulation models remains open. Potential solutions can include evidence-basee where the manipulated content is used to retrieve evidence data (e.g. the original video or counter- factual evidence) to prove the manipulation.
approaches,
Ethics Statement
Justifications for multimodal fact-checking. While explainable fact-checking has received atten- tion recently (Kotonya and Toni, 2020b; Atanasova et al., 2020), there is limited work on producing justifications for multimodal content. Previous ef- forts on multimodal justification production have mostly focused on highlighting parts of the input to increase interpretability (Kou al., 2020; Shang al., 2022). Natural language justifications that explain the fact-check of multimodal claims so that it is accessible to non-technical have not been de- veloped yet. To develop solutions, we first need ap- propriate benchmarks to measure progress, More- over, with the recent advances of neural models for visual and audio generation and editing, an- other far unexplored direction presents itself: editing input images/videossaudios or generating entirely content to explain fact-checking results. This could include, for example, the generation of infographics or video clips to explanation fact- checks. Such a system, especially if guided by human fact-checkers (Nakov et al., 2021), would be a potent tool. As noted in Lewandowsky et al. (2020), "well-designed graphs, videos, photos, and other semantic aids can be helpful to convey correc- tions involving complex or statistical information clearly and concisely".
As we mention in Section 4, most datasets for mul- timodal fact-checking tasks are available only in English. Thus, models are evaluated based on their performance on English benchmarks only. This can lead to distorted view about advancements on multimodal automated fact-checking it is lim- ited to single language out of more than 7000 world languages. While we call for future work on variety of languages, this survey provides an overview on the state-of-the-an art mostly-Englh rescarch efforts. Finally, we want to point out that multimodal fact-checking works we cite in this sur- vey might include misleading statements or images given as examples.
Acknowledgements
Zhijiang Guo, Michael Schlichtkrull and Andreas Vlachos are supported by the ERC grant AVeriTeC (GA 865958). This paper is produced part of the MuselT project which has been co-funded by the EU under the Grant Agreement number (01061441, MuselT has supported the work of Mubashara Akhtar. Views and opinions expressed are how- ever those of the author(s) only and do not neces- sarily reflect those of the European Union or the European Research Executive Agency, REA. Nei- ther the EU nor the granting authority can be held responsible for them.
5 Conclusion
We survey research on multimodal automated fact- checking and introduce framework that combines and organizes tasks introduced in various communi- ties studying misinformation. We discuss common terms and definitions in context of our framework. We further study popular benchmarks and model-
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Figure 3: Example from the Forensii video ma- nipulation dataset (Rössler et al., 2018) showing the manipulation generation approach.
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Figure 4: An entry from the MAIM dataset (Jaiswal et al., 2017) showing an image/text claim with metadata.
We manually screened and filtered the papers based on abstracts and introduction sections, before creating an overview of papers across the follow- ing dimensions: (1) modality; (2) fact-checking task; (3) contribution type (i.e. dataset, modeling approach, demo); (4) paper type (i.e. survey, posi- tion paper, solution paper (c.g. introducing a new benchmark or modeling approach), or evaluation paper (e.g. investigating previously proposed ap- proaches)). Papers were mostly excluded because they focused on other tasks than fact-checking (e.g. hate speech detection) used modalities out of our scope (e.g. tables). Moreover, during the screening process we found and added further related works. and concluded the screening with 84 unique papers.
A Methodology
We applied the following methodological approach to find and select relevant research papers for the survey.
First, after defining the research scope, we col- lected pivotal, highly-cited work (e.g. Nakamura et al. (2020)) and related surveys (e.g. (Alam et al., 2022)), resulting in 25 papers, as well as papers citing or cited by these works. We collected further works using the scholarly search engines Google Scholar Semantic Scholar?, DBLP8 and ACL An- thology and keyword-baaed -based search with Cartesian products of following keyword sets: ("fact check- ing". "fact verification", "misinformation". "disin- formation", "fake news"), ("multimodal", "text", "image"" "audio", "vidco"), and { ("machine learn- ing", "automated"). The databases were queried primarily during the time frame July 26, 2022 and August 10, 2022. This step resulted in collection of 123 papers.
The taxonomy of tasks (Section 2) was created an iterative manner starting with the task labels we assigned to works during screening. As starting point we also used taxonomies of text-only fact- checking surveys (Guo et al., 2022: Thome and Vlachos, 2018) and adapted them for multimodal fact-checking works.
B Examples: multimodal misinformation
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Figure 5: An entry from the Factify dataset (Suryavar- dan al., 2023b) depicting image/text claim and supporting image/text evidence document.
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Figure 6: Left misleading, right non non- misleadiin video screenshot from the Shang et al. (2021) dataset on COVID-19 TikTok Short Videos.


Reviews:
[{"review_id": "viEKDQWXPz", "reviewer": "Reviewer_p4Yf", "summary": "", "questions": "1. What are the difficulties and challenges of multimodal fact checking compared to traditional fact checking\n\n2. The authors should further clarify which benchmarks support which stages of checking.\n\n", "limitations": "", "rating": 2, "confidence": 3, "soundness": 2, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer very much for their review and questions, which we aim to address in detail below. \n\n**Regarding comparative analysis of the surveyed models**\n\nWe thank the reviewer for bringing these points to our attention. Our method, which involves comparing various models through the outcomes documented in their papers and discussing their similarities/ contrasts, is in line with prior surveys of the field. To illustrate, both [1] and [2], which are commonly-cited surveys in the domain of automated fact-checking, employ a similar strategy. \nWe agree that comparative evaluations are useful, and indeed these occur in the context of shared tasks that conduct them. Many of them have proceedings such as FEVER and FACTIFY which have overview papers for this purpose. We will cite those that are related to multi-modality if accepted.\n\n**Regarding practical implication of deploying multimodal automated fact-checking models in real-world settings**\n\nStudying applicability of automated fact-checking in real-world settings is important for understanding the usability of models developed in research settings. \n\nFor example, a recent paper by [2] (which we refer to at different places in our survey e.g. L681) focuses specifically on this aspect. The authors study available automated fact-checking tools that can support the human expert in the different steps of her fact-checking. One of the insights gained is the need to further develop multimodal datasets and technologies for automated fact-checking. \n\nThis aspect is also studied and discussed in previous work on automated fact-checking from the journalism/fact-checking community. For example, in [3], misinformation verification in the real-world is discussed through multiple articles written by professional fact-checkers and journalists. The articles discuss real-world implications of fact-checking and give practical advice and guidelines to fact-checkers. For example, an article by Trushar Barot, the paper highlights the role of user generated content in news reporting, as demonstrated by BBC News during the July 2005 bombings in London. The article underscores the importance of verifying images received from the public, detailing a comprehensive process that includes establishing the image's originator, corroborating the location, date, and time of the image, ensuring its contextual accuracy, and obtaining necessary permissions. \n\nWe will include a more in-depth discussion on this point (while also referring to previous work e.g. from [2] and the fact-checking community) in Section 4. \n\n**Regarding coverage and rapid development of the field**\n\nWe agree with the reviewer that multimodal automated fact-checking is a rapidly evolving field. We believe our list of paper is up-to-date considering the time of submission, but we will search the literature for more recent papers, and we welcome suggestions. \n\n**Question:** *What are the difficulties and challenges of multimodal fact checking compared to traditional fact checking?*\n\nThere are multiple challenges specific to multimodal fact-checking. We discuss this in Section 4 in more detail. Some of them are the following:\n\nMultimodal claims, such as manipulated videos, are deeply contextual and often form part of more extensive narratives. Effective fact-checking involves not only identifying manipulations but also understanding the communicated claim in a broader context. Current methods for extracting such claims from multimodal content while incorporating the context, focus strongly on text and audio-video transcription. (Sec 4.1)\n\nEvidence retrieval for modalities other than text and image (e.g. audio and video) is a significant challenge, as these modalities aren't easily searchable in various platforms and the web. Currently, fact-checkers frequently rely on accompanying text, such as metadata, comments, or captions, to gather evidence. While tools like reverse image search engines exist, they require precise inputs, like specific video screenshots, which can be hard to determine. (Sec 4.2)\n\nDespite the growing interest in multilingual fact-checking, most efforts remain constrained to text-only benchmarks and models. A survey of benchmarks showed a small number of multimodal datasets for non-English languages. Relying solely on English data when training and testing introduces biases, as it might not encompass concepts unique to other languages and cultures. (Sec 4.3)\n\nFor further details, we refer to Section 4 of the survey. \n\n**Question:** *The authors should further clarify which benchmarks support which stages of checking.*\n\nWe include this information in Table 1 under column “Tasks”, but to make this point clearer for the reader we can highlight it for each paper we mention in the main text. Thanks for the suggestion!\n\n**References**\n\n[1] James Thorne and Andreas Vlachos. 2018. Automated Fact Checking: Task Formulations, Methods and Future Directions. In Proceedings of the 27th International Conference on Computational Linguistics, pages 3346–3359, Santa Fe, New Mexico, USA. Association for Computational Linguistics.\n\n[2] Nakov, Preslav, et al. \"Automated fact-checking for assisting human fact-checkers.\" arXiv preprint arXiv:2103.07769 (2021).\n\n[3] Craig Silverman. (2014). Verification Handbook: An Ultimate Guideline on Digital Age Sourcing for Emergency Coverage, European Journalism Centre.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "The paper conducts a comparative analysis of different methods and approaches for multimodal fact checking.", "reasons_to_reject": "1. There is no any specific comparative analysis of the surveyed models or their performance on the benchmark datasets. The absence of such analysis weakens the survey's ability to provide insights into the relative effectiveness of different approaches. \n\n2. A more in-depth discussion on the challenges and practical implications of deploying multimodal automated fact-checking in real-world settings would enhance the paper's significance.\n\n3. Given the complexity and rapid development in the field of multimodal fact-checking, the paper appears to lack sufficient coverage of various multimodal approaches.\n", "questions_to_authors": "1. What are the difficulties and challenges of multimodal fact checking compared to traditional fact checking\n\n2. The authors should further clarify which benchmarks support which stages of checking.\n\n", "paper_topic_and_main_contributions": "The paper presents a detailed survey on the emerging field of multimodal automated fact-checking, filling a critical gap in the existing research landscape.", "excitement": 2, "reproducibility": null, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "EMnxCX67wE", "reviewer": "Reviewer_4pKp", "summary": "", "questions": "", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the review for their detailed review, feedback, and questions which we address below in more detail. \n\n**Conceptualization of the multimodal AFC (the framework): relation to previous work e.g. Thorne et al. (2019) and why this is the ideal approach.**\n\nOur multimodal automated fact-checking framework is an extension of the three-step fact-checking pipeline introduced in fact-checking surveys for textual data (Thorne and Vlachos, 2018; Guo et al., 2022). \n\nWhile we include additional tasks specific to multimodal fact-checking (e.g. claim extraction, manipulation, out-of-context detection), we tried to align multimodal fact-checking to standard fact-checking. This allows readers familiar to standard fact-checking literature understand fact-checking in context of multiple modalities. Moreover, this allowed us to find synergies (e.g. tasks in multimodal fact-checking which overlap to some extent with standard fact-checking, such as evidence retrieval) and refer to previous surveys and literature. \nFinally, we discussed stages of the pipeline which are different from standard fact-checking pipelines, e.g. the first and final stage, in more detail and highlighted differences specific to multiple modalities (more details on that in Sec 4). \n\n**Necessity of the first stage for all multimodal tasks.**\n\nAs we briefly mention in L146, the extraction of claims is often necessary from a context but we definitely agree with the review that this is not always the case (for example in the headline case mentioned by the reviewer). We will state this more clearly in the discussion of “Stage 1” in Section 2. Thank you for pointing this out! \n\n**Details on benchmark creation.**\n\nWhile we include various pieces of  information on the benchmarks in Table 1, such as the data source, the language, etc. - more details on how the benchmarks were created can be very insightful. \n\nWe have started extracting the following benchmark creation attributes for the 36 benchmarks listed in Table 1: rationale for dataset creation, preprocessing steps conducted on data collected from “source” (last column of Table 1 currently), annotation process and demographic of annotators, limitations and potential artifacts/biases of the benchmark mentioned by their authors. A subset of the new columns is provided below, we will update accordingly in the revised paper.\n\n| Dataset Name | Rationale | Preprocessing Steps | Annotation Process and Demographic of Annotators | Limitations & Biases |\n|--------------|-----------|---------------------|------------------------|----------------------|\n| NewsCLIPpings| Explore techniques for creating challenging image-caption matches. | Derived from VisualNews. Used spaCy for named entities, REL for entity linking, SBERT-WK & CLIP for text embeddings, Faster R-CNN & CLIP for image embeddings. | Unknown | Reliance on specific pre-trained models like CLIP; Pre-determined thresholds for similarity. |\n| Factify      | Determine similarity between news tweets to classify them based on text and image similarity. | Data collected from Twitter handles; Used Sentence BERT for text similarity, Histogram and ResNet50 for image similarity. Text and images preprocessed and converted to embeddings and similarity metrics. | Unknown | Dependence on pre-decided thresholds for similarity; Reliance on specific pre-trained models. |\n| Fauxtography | Address the truthfulness of claims related to images. | Images and claims collected from Snopes' Fauxtography and Reuters' Pictures of the Year. | Images labeled based on veracity. | Imbalance in the class distribution; Variation in claim text length. |\n \nWe concentrate on these benchmark attributes inspired by previous work on dataset documentation, e.g. Datasheets for Datasets (Gebru et al., 2018) and Data Cards by Huggingface.\n\nWe will add a separate subsection to Section “Datasets and Modeling Approaches” discussing this information. \n\n**Large LM-based verification for multimodal data**\n\nWe thank the reviewer for pointing this out to us. We would appreciate it if the reviewer can point towards some particular paper they would like us to add. \n\nWhile we discuss in L495-L525 different approaches for representation extraction and multimodal fusion and include LMs for text data L496, we will add more references on the recent work on LLM-based visual feature extraction (Yao et al. (2023)) and LLM-based modality fusion (e.g. Zhou et al. (2022) and Jiang et al., (2023)) for fact-checking those mentioned already.  \n\n**References:**\n\nJiang et al. (2023) Similarity-Aware Multimodal Prompt Learning for Fake News Detection\n\nZhou et al. (2022): Multimodal Fake News Detection via CLIP-Guided Learning\n\nThorne and Vlachos(2018): Automated fact checking: Task formulations, methods and future directions.\n\nGuo et al. (2022): A Survey on Automated Fact-Checking\n\nGebru et al. (2018): Datasheets for Datasets\n\nYao, Barry Menglong, et al. \"End-to-end multimodal fact-checking and explanation generation: A challenging dataset and models.\" Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2023.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "This paper well presents the motivation for multimodal (including text) automated fact-checking research and clearly organizes the paper. The citations and references are informative and comprehensive enough to represent the sub-tasks. ", "reasons_to_reject": "The authors point out a conceptualization of multimodal AFC as their contribution. As far as I am concerned, the three-stage framework of claim detection, evidence retrieval, and verdict prediction is not new to the fact checking field (https://arxiv.org/abs/1803.05355) and has been studied. Is there a justification for multimodal fact-checking should be done in this fashion? Please provide reasons why the authors consider this as the most ideal approach. \n\nFor extracting claims to be verified, this may not be the case for some data in the wild. For example, for fact-checking text associated with video content (e.g., youtube videos and their headlines), the headlines themselves are the verifiable claims that need not be verified. Is the first stage absolutely necessary for all multimodal tasks? \n\nIt is tricky to collect datasets from the wild or create artifacts that represent the natural phenomena of multimodal fact-checking due to inherent subjectivity and expensive crowdsourcing. Thus, mentioning how the benchmarks are created might be worthwhile to discuss to explain the characteristics of each dataset. \n\nIt might be worthwhile to include LLM-based detection models in the model section to reflect the current trend of fact verification and multimodal fact checking. ", "questions_to_authors": "", "paper_topic_and_main_contributions": "This paper conceptualises a framework for multimodal automated fact-checking. The authors talk about four modalities (e.g., text, image, audio, and video) that misinformation can be spread in the wild and presents existing benchmarks and models.", "excitement": 4, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "EySRzHBxfU", "reviewer": "Reviewer_jMRp", "summary": "", "questions": "The paper presents an extensive review of multimodal automated fact-checking tasks.\nOne of the most outstanding aspects of the manuscript is the vast amount of revised papers, on both, variety and quality.\n\nNevertheless, I think the paper lacks the structure to be clear, there are a lot of sections about specific aspects, for instance, those works related to multilinguality and multimodality, and others. This way turns into a complicated reading. Also, in line 64, the authors state \"To validate the importance of multimodal fact-checking, we manually annotated 9,255 claims from..\" in the rest of the paper there is no table comparing quantitative aspects of all the described and consulted works. Table 1 resume many works, but there is no comparison in terms of some experiment of quantitative comparison. \n\nThe manuscript would be more relevant if any quantitative analysis could be done, and maybe being more concise in the conclusions or define the principal insights as results of this revision. ", "limitations": "", "rating": 4, "confidence": 5, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for their feedback and questions raised which we aim to address below in much detail. \n\n**Structure and sections about different aspects**\n\nWe tried to organize the survey in a structured way by doing the following: \n\n    * Providing a figure with all stages and the (sub-)tasks per stage\n    * Splitting task formulation according to the stage in Fig 2. and numbering subtasks accordingly e.g. 3.1, 3.2, etc, for tasks of stage 3.\n    * splitting dataset and model discussion in Stages as well \n    * splitting dataset discussion in different paragraphs (i.e. input vs. output)\n    * providing a table on benchmarks and grouping them according to some attributes (e.g. tasks, modalities, source, etc.)\n\nIf the reviewer could provide  specific suggestions on how to improve the structure, we are happy to adjust the paper accordingly. \n\n**Details and discussion about the annotated data**\n\nDue to space limits we excluded the annotated data table and discussion from the paper but definitely agree with the review that is important information. \n\nBelow you can find a table highlighting the modalities of the annotated data, we will add it to the main paper with a discussion: \n\n| Claim Modality | Percentage |\n|----------------|------------|\n| Image          | 20.07%     |\n| Video          | 8.06%      |\n| Audio          | 0.55%      |\n| Total          | 28.68%     |\n\n**Quantitative comparison/experiments**\n\nWe thank the reviewer for bringing these points to our attention. Our method, which involves comparing various models through the outcomes documented in their papers and discussing their similarities/ contrasts, is in line with prior surveys of the field. To illustrate, both [1] and [2], which are commonly-cited surveys in the domain of automated fact-checking, employ a similar strategy. \n\nWe agree that comparative evaluations are useful, and indeed these occur in the context of shared tasks that conduct them. Many of them have proceedings such as FEVER and FACTIFY which have overview papers for this purpose. We will cite those that are related to multi-modality if accepted.\n\n**Conclusion and insights**\n\nIn the Challenges and Future Directions section (L568-L687), we discuss challenges related to claim extraction from multimodal content, generalizing detection of visual manipulations, multimodal evidence retrieval, challenges related joint multimodal and multilingual fact checking, justifications for multimodal fact-checking. Moreover, we highlight insights we gained which can lead to the following future works: \n\n    * effectively align and integrate all modalities present in and around the claim for claim understanding (L586 ff.)\n    * retrieving non-textual evidence (L614)\n    * requirements for multimodal fact-checking benchmarks to facilitate future work (L622 ff.)\n    * etc. \n\n**References**\n\n[1] Thorne and Vlachos(2018): Automated fact checking: Task formulations, methods and future directions.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "The paper presents an extensive review of multimodal automated fact-checking tasks.\nOne of the most outstanding aspects of the manuscript is the vast amount of revised papers, on both, variety and quality.", "reasons_to_reject": "Nevertheless, I think the paper lacks the structure to be clear, there are a lot of sections about specific aspects, for instance, those works related to multilinguality and multimodality, and others. This way turns into a complicated reading. Also, in line 64, the authors state \"To validate the importance of multimodal fact-checking, we manually annotated 9,255 claims from..\" in the rest of the paper there is no table comparing quantitative aspects of all the described and consulted works. Table 1 resume many works, but there is no comparison in terms of some experiment of quantitative comparison. \n\nThe manuscript would be more relevant if any quantitative analysis could be done, and maybe being more concise in the conclusions or define the principal insights as results of this revision. ", "questions_to_authors": "The paper presents an extensive review of multimodal automated fact-checking tasks.\nOne of the most outstanding aspects of the manuscript is the vast amount of revised papers, on both, variety and quality.\n\nNevertheless, I think the paper lacks the structure to be clear, there are a lot of sections about specific aspects, for instance, those works related to multilinguality and multimodality, and others. This way turns into a complicated reading. Also, in line 64, the authors state \"To validate the importance of multimodal fact-checking, we manually annotated 9,255 claims from..\" in the rest of the paper there is no table comparing quantitative aspects of all the described and consulted works. Table 1 resume many works, but there is no comparison in terms of some experiment of quantitative comparison. \n\nThe manuscript would be more relevant if any quantitative analysis could be done, and maybe being more concise in the conclusions or define the principal insights as results of this revision. ", "paper_topic_and_main_contributions": "The paper presents an extensive review of multimodal automated fact-checking tasks.\nOne of the most outstanding aspects of the manuscript is the vast amount of revised papers, on both, variety and quality.\n\nNevertheless, I think the paper lacks the structure to be clear, there are a lot of sections about specific aspects, for instance, those works related to multilinguality and multimodality, and others. This way turns into a complicated reading. Also, in line 64, the authors state \"To validate the importance of multimodal fact-checking, we manually annotated 9,255 claims from..\" in the rest of the paper there is no table comparing quantitative aspects of all the described and consulted works. Table 1 resume many works, but there is no comparison in terms of some experiment of quantitative comparison. \n\nThe manuscript would be more relevant if any quantitative analysis could be done, and maybe being more concise in the conclusions or define the principal insights as results of this revision. ", "excitement": 4, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Missing_References": "NA", "Typos_Grammar_Style_and_Presentation_Improvements": "NA"}, "extra_text": {}}]

Author rebuttal:

Meta-review: The paper presents a survey of recent work in multi-modal fact checking and a framework for categorizing the sub-tasks and existing datasets. All reviewers agreed that a survey of this kind would be useful for the community. The paper covers an extensive set of previous work and the reviewers mostly agree that the coverage is mostly comprehensive.

Several of the objections come from disagreements about the importance of evaluating the performance of existing models on the benchmark datasets. However, the authors argue in their rebuttal that this is in line with previous surveys that have been published in the field. 

A second objection raised by the reviewers is in the structure and organization of the paper, and more specifically, the links between specific benchmark datasets and the sub-tasks they are useful for. However, these objections are not well fleshed out in the reviewer comments. The authors have proposed a reorganization of the paper to improve its structure, as well as an extensive update to Table 1 to include more information about the rationale and process behind each dataset.
Decision: Accept-Findings