Abstract
The escalation of false information related to the massive use of social media has become a challenging problem, and significant is the effort of the research community in providing effective solutions to detecting it. Fake news are spreading for decades, but with the rise of social media, the nature of misinformation has evolved from text-based modality to visual modalities, such as images, audio, and video. Therefore, the identification of media-rich fake news requires an approach that exploits and effectively combines the information acquired from different multimodal categories. Multimodality is a key approach to improving fake news detection, but effective solutions supporting it are still poorly explored. More specifically, many different works exist that investigate if a text, an image, or a video is fake or not, but effective research on a real multimodal setting, ‘fusing’ the different modalities with their different structure and dimension is still an open problem. The paper is a focused survey concerning a very specific topic which is the use of deep learning (DL) methods for multimodal fake news detection on social media. The survey provides, for each work surveyed, a description of some relevant features such as the DL method used, the type of analysed data, and the fusion strategy adopted. The paper also highlights the main limitations of the current state of the art and draws some future directions to address open questions and challenges, including explainability and effective cross-domain fake news detection strategies.
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References
Abdali S (2022) Multi-modal misinformation detection: approaches, challenges and opportunities
Alam F, Cresci S, Chakraborty T, Silvestri F, Dimitrov D, Martino GDS, Shaar S, Firooz H, Nakov P (2022) A survey on multimodal disinformation detection. In: Proceedings of the 29th international conference on computational linguistics. International Committee on Computational Linguistics, Gyeongju, Republic of Korea, pp 6625–6643
Alonso-Bartolome S, Segura-Bedmar I (2021) Multimodal fake news detection. arXiv. https://doi.org/10.48550/ARXIV.2112.04831
Amerini I, Ballan L, Caldelli R, Del Bimbo A, Serra G (2011) A sift-based forensic method for copy-move attack detection and transformation recovery. In: IEEE Transactions on information forensics and security, vol 6, pp 1099–1110. https://doi.org/10.1109/TIFS.2011.2129512
Amri S, Sallami D, Aïmeur E (2021) Exmulf: an explainable multimodal content-based fake news detection system. Springer, Berlin, pp 177–187. https://doi.org/10.1007/978-3-031-08147-7_12
Benamira A, Devillers B, Lesot E, Ray AK, Saadi M, Malliaros FD (2020) Semi-supervised learning and graph neural networks for fake news detection. In: Proceedings of the 2019 IEEE/ACM international conference on advances in social networks analysis and mining. ASONAM’19, pp 568–569
Boididou C, Papadopoulos S, Zampoglou M, Apostolidis L, Papadopoulou O, Kompatsiaris Y (2018) Detection and visualization of misleading content on twitter. Int J Multimed Info Retr 7:71–86. https://doi.org/10.1007/s13735-017-0143-x
Bovet A, Makse HA (2019) Influence of fake news in twitter during the 2016 US presidential election. Nat Commun. https://doi.org/10.1038/s41467-018-07761-2
Cao J, Sheng Q, Qi P, Zhong L, Wang Y, Zhang X (2019) False news detection on social media. arXiv. https://doi.org/10.48550/ARXIV.1908.10818
Cui L, Lee D (2020) CoAID: COVID-19 healthcare misinformation dataset
Cui L, Wang S, Lee D (2019) SAME: Sentiment-aware multi-modal embedding for detecting fake news. In: 2019 IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM), pp 41–48. https://doi.org/10.1145/3341161.3342894
da Silva FCD, Vieira R, Garcia ACB (2019) Can machines learn to detect fake news? A survey focused on social media. In: HICSS
Dolhansky B, Howes R, Pflaum B, Baram N, Ferrer CC (2019) The DeepFake detection challenge (DFDC) preview dataset
Dong M, Yao L, Wang X, Benatallah B, Sheng QZ, Huang H (2018) Dual: A deep unified attention model with latent relation representations for fake news detection. In: Hacid H, Cellary W, Wang H, Paik H-Y, Zhou R (eds) WISE, pp 199–209
Dou Y, Shu K, Xia C, Yu PS, Sun L (2021) User preference-aware fake news detection. arXiv
Eyben F, Weninger F, Groß F, Schuller B (2013) Recent developments in openSMILE, the munich open-source multimedia feature extractor. In: Proceedings of the 21st ACM international conference on multimedia
Ferreira W, Vlachos A (2016) Emergent: a novel data-set for stance classification. https://doi.org/10.18653/v1/N16-1138
Giachanou A, Zhang G, Rosso P (2020) Multimodal multi-image fake news detection. In: 2020 IEEE 7th international conference on data science and advanced analytics (DSAA), pp 647–654. https://doi.org/10.1109/DSAA49011.2020.00091
Hameleers M, Powell TE, Meer TGLAVD, Bos L (2020) A picture paints a thousand lies? The effects and mechanisms of multimodal disinformation and rebuttals disseminated via social media. Polit Commun 37(2):281–301. https://doi.org/10.1080/10584609.2019.1674979
Hangloo S, Arora B (2022) Combating multimodal fake news on social media: methods, datasets, and future perspective. Multimedia Syst 28:2391–2422
Hua J, Cui X, Li X, Tang K, Zhu P (2023) Multimodal fake news detection through data augmentation-based contrastive learning. Appl Soft Comput 136:110125. https://doi.org/10.1016/j.asoc.2023.110125
Ji S, Xu W, Yang M, Yu K (2013) 3D convolutional neural networks for human action recognition. IEEE Trans Pattern Anal Mach Intell 35(1):221–231. https://doi.org/10.1109/TPAMI.2012.59
Jiang S, Chen X, Zhang L, Chen S, Liu H (2019) User-characteristic enhanced model for fake news detection in social media. In: Tang J, Kan M, Zhao D, Li S, Zan H (eds) Natural language processing and Chinese computing—8th CCF international conference, NLPCC 2019, Dunhuang, China, October 9–14, 2019, Proceedings, Part I. Lecture notes in computer science, vol 11838. Springer, Berlin, pp 634–646
Jin Z, Cao J, Guo H, Zhang Y, Luo J (2017) Multimodal fusion with recurrent neural networks for rumor detection on microblogs. In: Proceedings of the 25th ACM international conference on multimedia. MM’17. Association for Computing Machinery, New York, pp 795–816
Jing Q, Yao D, Fan X, Wang B, Tan H, Bu X, Bi J (2021) TRANSFAKE: Multi-task transformer for multimodal enhanced fake news detection. In: IJCNN, pp 1–8
Kaliyar RK, Kumar P, Kumar M, Narkhede M, Namboodiri S, Mishra S (2020) Deepnet: an efficient neural network for fake news detection using news-user engagements. In: 2020 5th International conference on computing, communication and security (ICCCS), pp 1–6. https://doi.org/10.1109/ICCCS49678.2020.9277353
Karimi H, Tang J, Li Y (2018) Toward end-to-end deception detection in videos. In: 2018 IEEE international conference on big data (Big Data), pp 1278–1283. https://doi.org/10.1109/BigData.2018.8621909
Khattar D, Goud JS, Gupta M, Varma V (2019) Mvae: Multimodal variational autoencoder for fake news detection. In: The world wide web conference. WWW’19, pp 2915–2921
Kirchknopf A, Slijepčević D, Zeppelzauer M (2021) Multimodal detection of information disorder from social media. In: CBMI Conf., pp 1–4. https://doi.org/10.1109/CBMI50038.2021.9461898
Korshunov P, Marcel S (2018) DeepFakes: a new threat to face recognition? Assessment and detection
Krishnamurthy G, Majumder N, Poria S, Cambria E (2018) A deep learning approach for multimodal deception detection. arXiv. https://doi.org/10.48550/ARXIV.1803.00344
Kumari R, Ekbal A (2021) AMFB: Attention based multimodal factorized bilinear pooling for multimodal fake news detection. Expert Syst Appl 184:115412
Kumar S, Shah N (2018) False information on web and social media: a survey. arXiv. https://doi.org/10.48550/ARXIV.1804.08559
Li Y, Xie Y (2020) Is a picture worth a thousand words? an empirical study of image content and social media engagement. J Mark Res 57(1):1–19. https://doi.org/10.1177/0022243719881113
Lu Y-J, Li C-T (2020) GCAN: Graph-aware co-attention networks for explainable fake news detection on social media. In: Proceedings of the 58th annual meeting of the association for computational linguistics, pp 505–514
Ma J, Gao W, Mitra P, Kwon S, Jansen BJ, Wong K-F, Cha M (2016) Detecting rumors from microblogs with recurrent neural networks. In: IJCAI’16, pp 3818–3824
McCrae S, Wang K, Zakhor A (2022) Multi-modal semantic inconsistency detection in social media news posts. In: Multimedia modeling. Springer, Berlin, pp 331–343
Mendels G, Levitan SI, Lee K-Z, Hirschberg J (2017) Hybrid acoustic-lexical deep learning approach for deception detection. In: INTERSPEECH
Mittal T, Bhattacharya U, Chandra R, Bera A, Manocha D (2020) Emotions Don’t Lie: an audio-visual deepfake detection method using affective cues
Mosallanezhad A, Karami M, Shu K, Mancenido MV, Liu H (2022) Domain adaptive fake news detection via reinforcement learning. In: Proceedings of the ACM web conference 2022. https://doi.org/10.1145/3485447.3512258
Mu M, Bhattacharjee SD, Yuan J (2023) Self-supervised distilled learning for multi-modal misinformation identification. In: IEEE/CVF Winter conference on applications of computer vision, WACV 2023, Waikoloa, HI, USA, January 2–7, 2023. IEEE, pp 2818–2827
Murayama T (2021) Dataset of fake news detection and fact verification: a survey
Nakamura K, Levy S, Wang WY (2019) r/Fakeddit: A new multimodal benchmark dataset for fine-grained fake news detection. arXiv. https://doi.org/10.48550/ARXIV.1911.03854
Pérez-Rosas V, Abouelenien M, Mihalcea R, Burzo M (2015) Deception detection using real-life trial data. In: 2015 ACM on international conference on multimodal interaction, pp 59–66
Qian F, Gong C, Sharma K, Liu Y (2018) Neural user response generator: fake news detection with collective user intelligence. In: Proceedings of the twenty-seventh international joint conference on artificial intelligence, IJCAI-18, pp 3834–3840
Qi P, Cao J, Li X, Liu H, Sheng Q, Mi X, He Q, Lv Y, Guo C, Yu Y (2021) Improving fake news detection by using an entity-enhanced framework to fuse diverse multimodal clues, pp 1212–1220
Raj C, Meel P (2021) Convnet frameworks for multi-modal fake news detection. Appl Intell. https://doi.org/10.1007/s10489-021-02345-y
Rezayi S, Soleymani S, Arabnia HR, Li S (2021) Socially aware multimodal deep neural networks for fake news classification. In: 2021 IEEE 4th international conference on multimedia information processing and retrieval (MIPR), pp 253–259. https://doi.org/10.1109/MIPR51284.2021.00048
Ruchansky N, Seo S, Liu Y (2017) CSI: A hybrid deep model for fake news detection. In: Proceedings of the 2017 ACM on conference on information and knowledge management. ACM. https://doi.org/10.1145/3132847.3132877
Sachan T, Pinnaparaju N, Gupta M, Varma V (2021) SCATE: Shared cross attention transformer encoders for multimodal fake news detection. In: Proceedings of the 2021 IEEE/ACM international conference on advances in social networks analysis and mining. ASONAM’21, pp 399–406
Shang L, Kou Z, Zhang Y, Wang D (2021) A multimodal misinformation detector for COVID-19 short videos on Tiktok. In: 2021 IEEE international conference on big data (big data), pp 899–908. https://doi.org/10.1109/BigData52589.2021.9671928
Shang L, Kou Z, Zhang Y, Wang D (2022) A duo-generative approach to explainable multimodal COVID-19 misinformation detection. In: Proceedings of the ACM web conference 2022. WWW’22, pp 3623–3631
Shu K, Sliva A, Wang S, Tang J, Liu H (2017) Fake news detection on social media: a data mining perspective. SIGKDD Explor Newsl 19(1):22–36
Shu K, Cui L, Wang S, Lee D, Liu H (2019a) dEFEND: Explainable fake news detection. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery and data mining. KDD’19, pp 395–405
Shu K, Mahudeswaran D, Wang S, Lee D, Liu H (2019b) FakeNewsNet: a data repository with news content, social context and spatial temporal information for studying fake news on social media
Shu K, Mahudeswaran D, Wang S, Liu H (2020) Hierarchical propagation networks for fake news detection: Investigation and exploitation. In: Proceedings of the international AAAI conference on web and social media, vol 14, issue 1, pp 626–637
Shu K, Wang S, Liu H (2019) Beyond news contents: The role of social context for fake news detection. In: Proceedings of the twelfth ACM international conference on web search and data mining. WSDM’19, pp 312–320
Silva A, Luo L, Karunasekera S, Leckie C (2021) Embracing domain differences in fake news: cross-domain fake news detection using multi-modal data. In: The thirty-fifth AAAI conference on artificial intelligence (AAAI-21). https://doi.org/10.48550/ARXIV.2102.06314
Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition
Singhal S, Dhawan M, Shah RR, Kumaraguru P (2021) Inter-modality discordance for multimodal fake news detection. In: MMAsia
Song C, Ning N, Zhang Y, Wu B (2021) A multimodal fake news detection model based on crossmodal attention residual and multichannel convolutional neural networks. Inf Process Manag 58(1):102437
Volkova S, Shaffer K, Jang JY, Hodas N (2017) Separating facts from fiction: linguistic models to classify suspicious and trusted news posts on Twitter. In: Proceedings of the 55th annual meeting of the association for computational linguistics (volume 2: short papers), pp 647–653
Vosoughi S, Roy D, Aral S (2018) The spread of true and false news online. Science 359:1146–1151. https://doi.org/10.1126/science.aap9559
Wang WY (2017) “Liar, Liar Pants on Fire”: a new benchmark dataset for fake news detection. arXiv. https://doi.org/10.48550/ARXIV.1705.00648
Wang Y, Ma F, Jin Z, Yuan Y, Xun G, Jha K, Su L, Gao J (2018) Eann: Event adversarial neural networks for multi-modal fake news detection. In: KDD, pp 849–857
Wang Y, Ma F, Wang H, Jha K, Gao J (2021) Multimodal emergent fake news detection via meta neural process networks. In: Proceedings of the 27th ACM SIGKDD conference on knowledge discovery and data mining. KDD’21, pp 3708–3716
Wang K, Chan D, Zhao SZ, Canny J, Zakhor A (2022) Misinformation detection in social media video posts
Wu L, Rao Y (2020) Adaptive interaction fusion networks for fake news detection. In: 24th European conference on artificial intelligence—ECAI 2020
Xie J, Liu S, Liu R, Zhang Y, Zhu Y (2021) SERN: Stance extraction and reasoning network for fake news detection. In: ICASSP, pp 2520–2524. https://doi.org/10.1109/ICASSP39728.2021.9414787
Xue J, Wang Y, Tian Y, Li Y, Shi L, Wei L (2021) Detecting fake news by exploring the consistency of multimodal data. Inf Process Manag 58(5):102610
Yang Y, Zheng L, Zhang J, Cui Q, Li Z, Yu PS (2018) TI-CNN: convolutional neural networks for fake news detection. arXiv
Yang S, Shu K, Wang S, Gu R, Wu F, Liu H (2019) Unsupervised fake news detection on social media: a generative approach. In: Proceedings of the AAAI conference on artificial intelligence, vol 33, issue 01, pp 5644–5651
Zannettou S, Caulfield T, Blackburn J, De Cristofaro E, Sirivianos M, Stringhini G, Suarez-Tangil G (2018) On the origins of memes by means of fringe web communities. arXiv. https://doi.org/10.48550/ARXIV.1805.12512
Zhang DY, Shang L, Geng B, Lai S, Li K, Zhu H, Amin MT, Wang D (2018) FauxBuster: A content-free fauxtography detector using social media comments. In: 2018 IEEE Big Data Conf., pp 891–900. https://doi.org/10.1109/BigData.2018.8622344
Zhang H, Fang Q, Qian S, Xu C (2019) Multi-modal knowledge-aware event memory network for social media rumor detection. In: Proceedings of the 27th ACM international conference on multimedia. MM’19, pp 1942–1951
Zhang T, Wang D, Chen H, Zeng Z, Guo W, Miao C, Cui L (2020) BDANN: Bert-based domain adaptation neural network for multi-modal fake news detection. In: 2020 International joint conference on neural networks (IJCNN), pp 1–8. https://doi.org/10.1109/IJCNN48605.2020.9206973
Zhou X, Mulay A, Ferrara E, Zafarani R (2020a) Recovery: a multimodal repository for COVID-19 news credibility research. In: CIKM’20. Association for Computing Machinery, New York, pp 3205–3212. https://doi.org/10.1145/3340531.3412880
Zhou X, Wu J, Zafarani R (2020b) SAFE: similarity-aware multi-modal fake news detection. arXiv. https://doi.org/10.48550/ARXIV.2003.04981
Zubiaga A, Liakata M, Procter R, Hoi GWS, Tolmie P (2016) Analysing how people orient to and spread rumours in social media by looking at conversational threads. PLOS ONE 11(3):0150989. https://doi.org/10.1371/journal.pone.0150989
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This work was partially supported by project SERICS (PE00000014) under the MUR National Recovery and Resilience Plan funded by the European Union - NextGenerationEU.
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Comito, C., Caroprese, L. & Zumpano, E. Multimodal fake news detection on social media: a survey of deep learning techniques. Soc. Netw. Anal. Min. 13, 101 (2023). https://doi.org/10.1007/s13278-023-01104-w
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DOI: https://doi.org/10.1007/s13278-023-01104-w