Combating Fake News on Social Media: An Early Detection Approach Using Multimodal Adversarial Transfer Learning

References

• Allcott H, Gentzkow M (2017) Social media and fake news in the 2016 election. J. Econom. Perspective 31(2):211–236.Crossref, Google Scholar
• Baltrušaitis T, Ahuja C, Morency LP (2018) Multimodal machine learning: A survey and taxonomy. IEEE Trans. Pattern Anal. Machine Intelligence 41(2):423–443.Crossref, Google Scholar
• Bashath S, Perera N, Tripathi S, Manjang K, Dehmer M, Streib FE (2022) A data-centric review of deep transfer learning with applications to text data. Inform. Sci. 585:498–528.Crossref, Google Scholar
• Bastani H (2021) Predicting with proxies: Transfer learning in high dimension. Management Sci. 67(5):2964–2984.Link, Google Scholar
• Ben-David S, Blitzer J, Crammer K, Pereira F (2006) Analysis of representations for domain adaptation. Adv. Neural Inform. Processing Systems, vol. 19 (Curran Associates, Red Hook, NY), 129–136.Google Scholar
• Bian T, Xiao X, Xu T, Zhao P, Huang W, Rong Y, Huang J (2020) Rumor detection on social media with bi-directional graph convolutional networks. Proc. AAAI Conf. Artificial Intelligence, vol. 34, no. 8 (AAAI Press, Palo Alto, CA), 549–556.Google Scholar
• Boididou C, Papadopoulos S, Zampoglou M, Apostolidis L, Papadopoulou O, Kompatsiaris Y (2018) Detection and visualization of misleading content on Twitter. Internat. J. Multimedia. Inform. Retrieval 7(1):71–86.Crossref, Google Scholar
• Borgwardt KM, Gretton A, Rasch MJ, Kriegel HP, Schölkopf B, Smola AJ (2006) Integrating structured biological data by kernel maximum mean discrepancy. Bioinformatics 22(14):e49–e57.Crossref, Google Scholar
• Breiman L (2001) Random forests. Machine Learn. 45:5–32.Crossref, Google Scholar
• Cai W, Jiang J, Wang F, Tang J, Kim S, Huang J (2025) A survey on mixture of experts in large language models. IEEE Trans. Knowledge Data Engrg. 37(7):3896–3915.Google Scholar
• Chen T, Guestrin C (2016) Xgboost: A scalable tree boosting system. Proc. 22nd ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (ACM, New York), 785–794.Google Scholar
• Clarke J, Chen H, Du D, Hu YJ (2020) Fake news, investor attention, and market reaction. Inform. Systems Res. 32(1):35–52.Link, Google Scholar
• Cortes C, Vapnik V (1995) Support-vector networks. Machine Learn. 20:273–297.Crossref, Google Scholar
• Cox DR (1958) The regression analysis of binary sequences. J. Roy. Statist. Soc. Ser. B: Statist. Methodology 20(2):215–232.Crossref, Google Scholar
• Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, et al. (2020) An image is worth 16x16 words: Transformers for image recognition at scale. Preprint, submitted October 22, https://arxiv.org/abs/2010.11929v1.Google Scholar
• Ganin Y, Ustinova E, Ajakan H, Germain P, Larochelle H, Laviolette F, March M, et al. (2016) Domain-adversarial training of neural networks. J. Machine Learn. Res. 17(59):1–35.Google Scholar
• Grewal R, Gupta S, Hamilton R (2021) Marketing insights from multimedia data: Text, image, audio, and video. J. Marketing Res. 58(6):1025–1033.Crossref, Google Scholar
• Hu W, Chen W, Yuan W, Wang Y, Cai S, Zhang Y (2024) Dual-stream pre-training transformer to enhance multimodal learning for social media prediction. Proc. 32nd ACM Internat. Conf. Multimedia (ACM, New York), 11450–11456.Google Scholar
• Jia C, Yang Y, Xia Y, Chen YT, Parekh Z, Pham H, Le Q, et al. (2021) Scaling up visual and vision-language representation learning with noisy text supervision. Meila M, Zhang T, eds. Proc. 38th Internat. Conf. Machine Learn. Proceedings of Machine Learning Research, vol. 139 (PMLR, New York), 4904–4916.Google Scholar
• Jin Z, Cao J, Guo H, Zhang Y, Luo J (2017) Multimodal fusion with recurrent neural networks for rumor detection on microblogs. Proc. 25th ACM Internat. Conf. Multimedia (ACM, New York), 795–816.Google Scholar
• Kang Z, Cao Y, Shang Y, Liang T, Tang H, Tong L (2021) Fake news detection with heterogenous deep graph convolutional network. Pacific-Asia Conf. Knowledge Discovery Data Mining (ACM, New York), 408–420.Google Scholar
• Khattar D, Goud JS, Gupta M, Varma V (2019) MVAE: Multimodal variational autoencoder for fake news detection. Proc. World Wide Web Conf. (ACM, New York), 2915–2921.Google Scholar
• Liu Y, Wu YF (2018) Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. Proc. AAAI Conf. Artificial Intelligence, vol. 32, no. 8 (AAAI Press, Palo Alto, CA), 354–361.Google Scholar
• Liu Y, Ott M, Goyal N, Du J, Joshi M, Chen D, Levy O, et al. (2019) Roberta: A robustly optimized bert pretraining approach. Preprint, submitted July 26, https://arxiv.org/abs/1907.11692.Google Scholar
• Long M, Zhu H, Wang J, Jordan MI (2017) Deep transfer learning with joint adaptation networks. Proc. Internat. Conf. Machine Learn. (PMLR, New York), 2208–2217.Google Scholar
• Luca M, Zervas G (2016) Fake it till you make it: Reputation, competition, and yelp review fraud. Management Sci. 62(12):3412–3427.Link, Google Scholar
• Moravec PL, Kim A, Dennis AR, Minas RK (2022) Do you really know if it’s true? How asking users to rate stories affects belief in fake news on social media. Inform. Systems Res. 33(3):887–907.Link, Google Scholar
• Ng KC, Ke PF, So MKP, Tam KY (2023) Augmenting fake content detection in online platforms: A domain adaptive transfer learning via adversarial training approach. Production Oper. Management 32(7):2101–2122.Crossref, Google Scholar
• Pan SJ, Yang Q (2009) A survey on transfer learning. IEEE Trans. Knowledge Data Engrg. 22(10):1345–1359.Crossref, Google Scholar
• Qian S, Wang J, Hu J, Fang Q, Xu C (2021) Hierarchical multi-modal contextual attention network for fake news detection. Proc. 44th Internat. ACM SIGIR Conf. Res. Development Inform. Retrieval (ACM, New York), 153–162.Google Scholar
• Radford A, Kim JW, Hallacy C, Ramesh A, Goh G, Agarwal S, Sastry G, et al. (2021) Learning transferable visual models from natural language supervision. Meila M, Zhang T, eds. Proc. 38th Internat. Conf. Machine Learn., Proceedings of Machine Learning Research, vol. 139 (PMLR, New York), 8748–8763.Google Scholar
• Raghunathan AU, Bergman D, Hooker JN, Serra T, Kobori S (2024) Seamless multimodal transportation scheduling. INFORMS J. Comput. 36(2):336–358.Link, Google Scholar
• Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2017) Grad-cam: Visual explanations from deep networks via gradient-based localization. Proc. IEEE Internat. Conf. Computer Vision (IEEE, Piscataway, NJ), 618–626.Google Scholar
• Shu K, Wang S, Liu H (2019) Beyond news contents: The role of social context for fake news detection. Proc. 12th ACM Internat. Conf. Web Search Data Mining (ACM, New York), 312–320.Google Scholar
• Singhal S, Shah RR, Chakraborty T, Kumaraguru P, Satoh S (2019) Spotfake: A multi-modal framework for fake news detection. Proc. IEEE 5th Internat. Conf. Multimedia Big Data (IEEE, New York), 39–47.Google Scholar
• Sun B, Saenko K (2016) Deep coral: Correlation alignment for deep domain adaptation. Proc. Comput. Vision–ECCV Workshops (Springer, Cham, Switzerland), 443–450.Google Scholar
• Tzeng E, Hoffman J, Saenko K, Darrell T (2017) Adversarial discriminative domain adaptation. Proc. IEEE Conf. Comput. Vision Pattern Recognition (IEEE, Piscataway, NJ), 7167–7176.Google Scholar
• Vosoughi S, Roy D, Aral S (2018) The spread of true and false news online. Science 359(6380):1146–1151.Crossref, Google Scholar
• Wanda P, Diqi M (2024) Deepnews: Enhancing fake news detection using generative round network (GRN). Internat. J. Inform. Tech. 16(7):4289–4298.Google Scholar
• Wang C, Zhang C, Chen R (2025) Combating fake news on social media: An early detection approach using multimodal adversarial transfer learning. https://doi.org/10.1287/ijoc.2023.0514.cd, https://github.com/INFORMSJoC/2023.0514.Google Scholar
• Wang L, Zhang C, Xu H, Xu Y, Xu X, Wang S (2023) Cross-modal contrastive learning for multimodal fake news detection. Proc. 31st ACM Internat. Conf. Multimedia (ACM, New York), 5696–5704.Google Scholar
• Wang Y, Ma F, Jin Z, Yuan Y, Xun G, Jha K, Su L, et al. (2018) EANN: Event adversarial neural networks for multi-modal fake news detection. Proc. 24th ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (ACM, New York), 849–857.Google Scholar
• Wu J, Xu W, Liu Q, Wu S, Wang L (2023) Adversarial contrastive learning for evidence-aware fake news detection with graph neural networks. IEEE Trans. Knowledge Data Engrg. 36(11):5591–5604.Google Scholar
• Yuan H, Zheng J, Ye Q, Qian Y, Zhang Y (2021) Improving fake news detection with domain-adversarial and graph-attention neural network. Decision Support Systems 151:113633.Crossref, Google Scholar
• Zhao W, Nakashima Y, Chen H, Babaguchi N (2023) Enhancing fake news detection in social media via label propagation on cross-modal tweet graph. Proc. 31st ACM Internat. Conf. Multimedia (ACM, New York), 2400–2408.Google Scholar
• Zhou X, Zafarani R (2020) A survey of fake news: Fundamental theories, detection methods, and opportunities. ACM Comput. Survey 53(5):1–40.Crossref, Google Scholar
• Zhou Y, Yang Y, Ying Q, Qian Z, Zhang X (2023) Multimodal fake news detection via clip-guided learning. Proc. IEEE Internat. Conf. Multimedia (ACM, New York), 2825–2830.Google Scholar
• Zhu Y, Zhuang F, Wang J, Ke G, Chen J, Bian J, Xiong H, He Q (2020) Deep subdomain adaptation network for image classification. IEEE Trans. Neural Networks Learn. Systems 32(4):1713–1722.Crossref, Google Scholar