Fighting disinformation with artificial intelligence: fundamentals, advances and challenges
DOI:
https://doi.org/10.3145/epi.2023.may.22Keywords:
Journalism, Disinformation, Computing, Artificial Intelligence, AI, Machine learning, Fact-checking, Datasets, Natural language processing, NLP, Social network analysis, Deep fakes, Large language modelsAbstract
Internet and social media have revolutionised the way news is distributed and consumed. However, the constant flow of massive amounts of content has made it difficult to discern between truth and falsehood, especially in online platforms plagued with malicious actors who create and spread harmful stories. Debunking disinformation is costly, which has put artificial intelligence (AI) and, more specifically, machine learning (ML) in the spotlight as a solution to this problem. This work revises recent literature on AI and ML techniques to combat disinformation, ranging from automatic classification to feature extraction, as well as their role in creating realistic synthetic content. We conclude that ML advances have been mainly focused on automatic classification and scarcely adopted outside research labs due to their dependence on limited-scope datasets. Therefore, research efforts should be redirected towards developing AI-based systems that are reliable and trustworthy in supporting humans in early disinformation detection instead of fully automated solutions.Â
Downloads
References
Afroz, Sadia; Brennan, Michael; Greenstadt, Rachel (2012). "Detecting hoaxes, frauds, and deception in writing style online". In: IEEE symposium on security and privacy, pp. 461-475. https://doi.org/10.1109/SP.2012.34
Aggarwal, Charu C. (2011). "An introduction to social network data analytics". In: Aggarwal, Charu C. (ed.). Social network data analytics. Springer. https://doi.org/10.1007/978-1-4419-8462-3
Amador, Julio; Molina-Solana, Miguel; Gómez-Romero, Juan (2019). "Towards easy-to-implement misinformation automatic detection for online social media". In: Proceedings of the conference for truth and trust online 2019. https://doi.org/10.36370/tto.2019.4
Arnold, Phoebe (2020). "The challenges of online fact checking". Full fact, 17 December. https://fullfact.org/blog/2020/dec/the-challenges-of-online-fact-checking-how-technology-can-and-cant-help
Azevedo, Lucas; D´Aquin, Mathieu; Davis, Brian; Zarrouk, Manel (2021). "LUX (linguistic aspects under examination): discourse analysis for automatic fake news classification". In: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pp. 41-56. https://doi.org/10.18653/v1/2021.findings-acl.4
Barabási, Albert-László (2016). Network science. Cambridge University Press. ISBN: 978 1 107 07626 6 http://networksciencebook.com
Bedi, Punam; Sharma, Chhavi (2016). "Community detection in social networks". Wiley interdisciplinary reviews: Data mining and knowledge discovery, v. 6, n. 3, pp. 115-135. https://doi.org/10.1002/widm.1178
Bishop, Christopher M. (2006). Pattern recognition and machine learning. Springer. ISBN: 978 0 387 31073 2 https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf
Blondel, Vincent D.; Guillaume, Jean-Loup; Lambiotte, Renaud; Lefebvre, Etienne (2008). "Fast unfolding of communities in large networks". Journal of statistical mechanics: theory and experiment, n. 10, pp. P10008. https://doi.org/10.1088/1742-5468/2008/10/P10008
Bondielli, Alessandro; Marcelloni, Francesco (2019). "A survey on fake news and rumour detection techniques". Information sciences, v. 497, pp. 38-55. https://doi.org/10.1016/j.ins.2019.05.035
Bonet-Jover, Alba; Piad-Morffis, Alejandro; Saquete, Estela; Martínez-Barco, Patricio; García-Cumbreras, Miguel-Ángel (2021). "Exploiting discourse structure of traditional digital media to enhance automatic fake news detection". Expert systems with applications, v. 169, 114340. https://doi.org/10.1016/j.eswa.2020.114340
Brown, Tom B.; Mann, Benjamin; Ryder, Nick; Subbiah, Melanie; Kaplan, Jared; Dhariwal, Prafulla; Neelakantan, Arvind; Shyam, Pranav; Sastry, Girish; Askell, Amanda; Agarwal, Sandhini; Herbert-Voss, Ariel; Krueger, Gretchen; Henighan, Tom; Child, Rewon; Ramesh, Aditya; Ziegler, Daniel M.; Wu, Jeffrey; Winter, Clemens; Hesse, Christopher; Chen, Mark; Sigler, Eric; Litwin, Mateusz; Gray, Scott; Chess, Benjamin; Clark, Jack; Berner, Christopher; McCandlish, Sam; Radford, Alec; Sutskever, Ilya; Amodei, Dario (2020). "Language models are few-shot learners". Advances in neural information processing systems, v. 33, pp. 1877-1901. https://papers.nips.cc/paper/2020/hash/1457c0d6bfcb4967418bfb8ac142f64a-Abstract.html
Buda, Jakab; Bolonyai, Flora (2020). "An ensemble model using n-grams and statistical features to identify fake news spreaders on Twitter". In: Working notes of CLEF 2020 - Conference and labs of the evaluation forum, v. 2696. https://ceur-ws.org/Vol-2696/paper_189.pdf
Camacho, David; Panizo-Lledot, Ángel; Bello-Orgaz, Gema; González-Pardo, Antonio; Cambria, Erik (2020). "The four dimensions of social network analysis: an overview of research methods, applications, and software tools". Information fusion, v. 63, pp. 88-120. https://doi.org/10.1016/j.inffus.2020.05.009
Cambria, Erik; Wang, Haixun; White, Bebo (2014). "Guest editorial: big social data analysis". Knowledge-based systems, v. 69. https://doi.org/10.1016/j.knosys.2014.07.002
Castelo, Sonia; Almeida, Thais; Elghafari, Anas; Santos, Aécio; Pham, Kien; Nakamura, Eduardo; Freire, Juliana (2019). "A topic-agnostic approach for identifying fake news pages". In: Companion proceedings of the 2019 World Wide Web conference, pp. 975-980. https://doi.org/10.1145/3308560.3316739
Dagar, Deepak; Vishwakarma, Dinesh K. (2022). "A literature review and perspectives in deepfakes: generation, detection, and applications". International journal of multimedia information retrieval, v. 11, n. 3, pp. 219-289. https://doi.org/10.1007/s13735-022-00241-w
Das, Anubrata; Liu, Houjiang; Kovatchev, Venelin; Lease, Matthew (2023). "The state of human-centered NLP technology for fact-checking". Information processing & management, v. 60, n. 2, 103219. https://doi.org/10.1016/j.ipm.2022.103219
Davis, Clayton-Allen; Varol, Onur; Ferrara, Emilio; Flammini, Alessandro; Menczer, Filippo (2016). "BotOrNot: a system to evaluate social bots". In: Proceedings of the 25th International conference companion on World Wide Web, pp. 273-274. https://doi.org/10.1145/2872518.2889302
Della-Vedova, Marco L.; Tacchini, Eugenio; Moret, Stefano; Ballarin, Gabriele; DiPierro, Massimo; De-Alfaro, Luca (2018). "Automatic online fake news detection combining content and social signals". In: 22nd Conference of open innovations association (Fruct), pp. 272-279. https://doi.org/10.23919/FRUCT.2018.8468301
De-Souza, Mariana C.; Nogueira, Bruno-Magalhí£es; Rossi, Rafael-Geraldeli; Marcacini, Ricardo-Marcondes; Dos-Santos, Brucce-Neves; Rezende, Solange-Oliveira (2022). "A network-based positive and unlabeled learning approach for fake news detection". Machine learning, v. 111, n. 10, pp. 3549-3592. https://doi.org/10.1007/s10994-021-06111-6
Del-Vicario, Michela; Vivaldo, Gianna; Bessi, Alessandro; Zollo, Fabiana; Scala, Antonio; Caldarelli, Guido; Quattrociocchi, Walter (2016). "Echo chambers: emotional contagion and group polarization on facebook". Scientific reports, v. 6, 37825. https://doi.org/10.1038/srep37825
Des-Mesnards, Nicolas-Guenon; Hunter, David-Scott; El-Hjouji, Zakaria; Zaman, Tauhid (2022). "Detecting bots and assessing their impact in social networks". Operations research, v. 70, n. 1. https://doi.org/10.1287/opre.2021.2118
Dong, Xishuang; Victor, Uboho; Qian, Lijun (2020). "Two-path deep semisupervised learning for timely fake news detection". IEEE transactions on computational social systems, v. 7, n. 6, pp. 1386-1398. https://doi.org/10.1109/TCSS.2020.3027639
Ferrara, Emilio; Varol, Onur; Davis, Clayton; Menczer, Filippo; Flammini, Alessandro (2016). "The rise of social bots". Communications of the ACM, v. 59, n. 7, pp. 96-104. https://doi.org/10.1145/2818717
Fortunato, Santo (2010). "Community detection in graphs". Physics reports, v. 486, n. 3-5, pp. 75-174. https://doi.org/10.1016/j.physrep.2009.11.002
Ghanem, Bilal; Ponzetto, Simone P.; Rosso, Paolo; Rangel, Francisco (2021). "FakeFlow: fake news detection by modeling the flow of affective information". In: Proceedings of the 16th Conference of the European chapter of the Association for Computational Linguistics, pp. 679-689. https://doi.org/10.18653/v1/2021.eacl-main.56
Giachanou, Anastasia; Ghanem, Bilal; Ríssola, Esteban A.; Rosso, Paolo; Crestani, Fabio; Oberski, Daniel (2022). "The impact of psycholinguistic patterns in discriminating between fake news spreaders and fact checkers". Data & knowledge engineering, v. 138, 101960. https://doi.org/10.1016/j.datak.2021.101960
Giachanou, Anastasia; Rosso, Paolo; Crestani, Fabio (2019). "Leveraging emotional signals for credibility detection". In: Proceedings of the 42nd international ACM SIGIR conference on research and development in information retrieval, pp. 877-880. https://doi.org/10.1145/3331184.3331285
Giachanou, Anastasia; Rosso, Paolo; Crestani, Fabio (2021). "The impact of emotional signals on credibility assessment". Journal of the Association for Information Science and Technology, v. 72, n. 9, pp. 1117-1132. https://doi.org/10.1002/asi.24480
Giachanou, Anastasia; Zhang, Guobiao; Rosso, Paolo (2020). "Multimodal multi-image fake news detection". In: IEEE 7th International conference on data science and advanced analytics (DSAA), pp. 647-654. https://doi.org/10.1109/DSAA49011.2020.00091
Goodfellow, Ian; Bengio, Yoshua; Courville, Aaron (2016). Deep learning. MIT Press. ISBN: 978 0 262 035613
Goodfellow, Ian J.; Pouget-Abadie, Jean; Mirza, Mehdi; Xu, Bing; Warde-Farley, David; Ozair, Sherjil; Courville, Aaron; Bengio, Yoshua (2014). "Generative adversarial nets". Advances in neural information processing systems, v. 27. https://papers.nips.cc/paper/5423-generative-adversarial-nets
Graves, Lucas (2018). Understanding the promise and limits of automated fact-checking. Reuters Institute, University of Oxford. https://reutersinstitute.politics.ox.ac.uk/sites/default/files/2018-02/graves_factsheet_180226%20FINAL.pdf
Graves, Lucas; Nyhan, Brendan; Reifler, Jason (2016). "Understanding innovations in journalistic practice: a field experiment examining motivations for fact-checking". Journal of communication, v. 66, n. 1, pp. 102-138. https://doi.org/10.1111/jcom.12198
Greengard, Samuel (2019). "Will deepfakes do deep damage?". Communications of the ACM, v. 63, n. 1, pp. 17-19. https://doi.org/10.1145/3371409
Grinberg, Nir; Joseph, Kenneth; Friedland, Lisa; Swire-Thompson, Briony; Lazer, David (2019). "Fake news on Twitter during the 2016 U.S. presidential election". Science, v. 363, n. 6425, pp. 374-378. https://doi.org/10.1126/science.aau2706
Guo, Bin; Ding, Yasan; Yao, Lina; Liang, Yunji; Yu, Zhiwen (2020). "The future of false information detection on social media: new perspectives and trends". ACM computing surveys, v. 53, n. 4. https://doi.org/10.1145/3393880
Guo, Zhijiang; Schlichtkrull, Michael; Vlachos, Andreas (2022). "A survey on automated fact-checking". Transactions of the Association for Computational Linguistics, v. 10, pp. 178-206. https://doi.org/10.1162/tacl_a_00454
Hangloo, Sakshini; Arora, Bhavna (2022). "Combating multimodal fake news on social media: methods, datasets, and future perspective". Multimedia systems, v. 28, n. 6, pp. 2391-2422. https://doi.org/10.1007/s00530-022-00966-y
Harrigan, Paul; Daly, Timothy M.; Coussement, Kristof; Lee, Julie A.; Soutar, Geoffrey N.; Evers, Uwana (2021). "Identifying influencers on social media". International journal of information management, v. 56, 102246. https://doi.org/10.1016/j.ijinfomgt.2020.102246
Jing, Jing; Wu, Hongchen; Sun, Jie; Fang, Xiaochang; Zhang, Huaxiang (2023). "Multimodal fake news detection via progressive fusion networks". Information processing & management, v. 60, n. 1, 103120. https://doi.org/10.1016/j.ipm.2022.103120
John, Oliver P.; Srivastava, Sanjay (1999). "The big five trait taxonomy: history, measurement, and theoretical perspectives". In: Pervin, Lawrence A.; John, Oliver P. (eds.). Handbook of personality: Theory and research, pp. 102-138. https://pages.uoregon.edu/sanjay/pubs/bigfive.pdf
Kang, SeongKu; Hwang, Junyoung; Yu, Hwanjo (2020). "Multi-modal component embedding for fake news detection". In: 14th international conference on ubiquitous information management and communication (Imcom). https://doi.org/10.1109/IMCOM48794.2020.9001800
Karras, Tero; Aila, Timo; Laine, Samuli; Lehtinen, Jaakko (2018). "Progressive growing of GANs for improved quality, stability, and variation". In: 6th International conference on learning representations. https://research.nvidia.com/sites/default/files/pubs/2017-10_Progressive-Growing-of/karras2018iclr-paper.pdf
Karras, Tero; Laine, Samuli; Aila, Timo (2019). "A style-based generator architecture for generative adversarial networks". In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition, pp. 4396-4405. https://doi.org/10.1109/CVPR.2019.00453
Kartal, Yavuz-Selim; Kutlu, Mucahid (2023). "Re-think before you share: a comprehensive study on prioritizing check-worthy claims". IEEE transactions on computational social systems, v. 10, n. 1, pp. 362-375. https://doi.org/10.1109/TCSS.2021.3138642
Khattar, Dhruv; Goud, Jaipal-Singh; Gupta, Manish; Varma, Vasudeva (2019). "MVAE: multimodal variational autoencoder for fake news detection". In: The World Wide Web conference, pp. 2915-2921. https://doi.org/10.1145/3308558.3313552
Konstantinovskiy, Lev; Price, Oliver; Babakar, Mevan; Zubiaga, Arkaitz (2021). "Toward automated factchecking: developing an annotation schema and benchmark for consistent automated claim detection". Digital threats: research and practice, v. 2, n. 2. https://doi.org/10.1145/3412869
Kudugunta, Sneha; Ferrara, Emilio (2018). "Deep neural networks for bot detection". Information sciences, v. 467, pp. 312-322. https://doi.org/10.1016/j.ins.2018.08.019
La-Barbera, David; Roitero, Kevin; Mizzaro, Stefano (2022). "A hybrid human-in-the-loop framework for fact checking". In: Proceedings of the 6th Workshop on natural language for artificial intelligence (NL4AI 2022), v. 3287. https://ceur-ws.org/Vol-3287/paper4.pdf
LeCun, Yann; Bengio, Yoshua; Hinton, Geoffrey (2015). "Deep learning". Nature, v. 521, n. 7553, pp. 436-444. https://doi.org/10.1038/nature14539
Li, Dun; Guo, Haimei; Wang, Zhenfei; Zheng, Zhiyun (2021). "Unsupervised fake news detection based on autoencoder". IEEE access, v. 9, pp. 29356-29365. https://doi.org/10.1109/ACCESS.2021.3058809
Li, Shuo; Yao, Tao; Li, Saifei; Yan, Lianshan (2022). "Semantic"enhanced multimodal fusion network for fake news detection". International journal of intelligent systems, v. 37, n. 12, pp. 12235-12251. https://doi.org/10.1002/int.23084
Li, Xin; Lu, Peixin; Hu, Lianting; Wang, Xiao-Guang; Lu, Long (2022). "A novel self-learning semi-supervised deep learning network to detect fake news on social media". Multimedia tools and applications, v. 81, n. 14, pp. 19341-19349. https://doi.org/10.1007/s11042-021-11065-x
Liu, Yang; Wu, Yi-Fang (2018). "Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks". Proceedings of the AAAI conference on artificial intelligence, v. 32, n. 1, pp. 354-361. https://doi.org/10.1609/aaai.v32i1.11268
Liu, Yang; Xu, Songhua (2016). "Detecting rumors through modeling information propagation networks in a social media environment". IEEE transactions on computational social systems, v. 3, n. 2, pp. 46-62. https://doi.org/10.1109/TCSS.2016.2612980
Manning, Christopher D.; Schí¼tze, Hinrich (1999). Foundations of statistical natural language processing. MIT Press. ISBN: 978 0 262 133609
Marcus, Gary (2022). "AI platforms like chatGPT are easy to use but also potentially dangerous". Scientific American, 19 December. https://www.scientificamerican.com/article/ai-platforms-like-chatgpt-are-easy-to-use-but-also-potentially-dangerous
Martín, Alejandro; Huertas-Tato, Javier; Huertas-García, Álvaro; Villar-Rodríguez, Guillermo; Camacho, David (2022). "FacTeR-check: semi-automated fact-checking through semantic similarity and natural language inference". Knowledge-based systems, v. 251, 109265. https://doi.org/10.1016/j.knosys.2022.109265
Masood, Momina; Nawaz, Mariam; Malik, Khalid M.; Javed, Ali; Irtaza, Aun; Malik, Hafiz (2022). "Deepfakes generation and detection: state-of-the-art, open challenges, countermeasures, and way forward". Applied intelligence, v. 54, pp. 3974-4026. https://doi.org/10.1007/s10489-022-03766-z
Meel, Priyanka; Vishwakarma, Dinesh K. (2020). "Fake news, rumor, information pollution in social media and web: a contemporary survey of state-of-the-arts, challenges and opportunities". Expert systems with applications, v. 153, 112986. https://doi.org/10.1016/j.eswa.2019.112986
Meel, Priyanka; Vishwakarma, Dinesh K. (2021). "A temporal ensembling based semi-supervised convnet for the detection of fake news articles". Expert systems with applications, v. 177, 115002. https://doi.org/10.1016/j.eswa.2021.115002
Megahed, Fadel M.; Chen, Ying-Ju; Ferris, Joshua A.; Knoth, Sven; Jones-Farmer, L. Allison (2023). "How generative AI models such as chatGPT can be (mis)used in SPC practice, education, and research? An exploratory study". ArXiv. https://doi.org/10.48550/arXiv.2302.10916
Mikolov, Tomas; Chen, Kai; Corrado, Greg; Dean, Jeffrey (2013). "Efficient estimation of word representations in vector space". In: 1st International conference on learning representations (ICLR). https://arxiv.org/abs/1301.3781
Mirsky, Yisroel; Lee, Wenke (2022). "The creation and detection of deepfakes". ACM computing surveys, v. 54, n. 1. https://doi.org/10.1145/3425780
Mitchell, Eric; Lee, Yoonho; Khazatsky, Alexander; Manning, Christopher D.; Finn, Chelsea (2023). "DetectGPT: zero-shot machine-generated text detection using probability curvature". ArXiv. https://doi.org/10.48550/arXiv.2301.11305
Molina-Solana, Miguel; Amador, Julio; Gómez-Romero, Juan (2018). "Deep learning for fake news classification". In: I Workshop on deep learning, pp. 1197-1201. https://sci2s.ugr.es/caepia18/proceedings/docs/CAEPIA2018_paper_207.pdf
Nakamura, Kai; Levy, Sharon; Wang, William Y. (2020). "Fakeddit: a new multimodal benchmark dataset for fine-grained fake news detection". In: Proceedings of the 12th International conference on language resources and evaluation, pp. 6149-6157. https://aclanthology.org/2020.lrec-1.755.pdf
Nakov, Preslav; Corney, David; Hasanain, Maram; Alam, Firoj; Elsayed, Tamer; Barrón-Cedeño, Alberto; Papotti, Paolo; Shaar, Shaden; Da-San-Martino, Giovanni (2021). "Automated fact-checking for assisting human fact-checkers". In: Proceedings of the Thirtieth international joint conference on artificial intelligence (IJCAI), pp. 4551-4558. https://doi.org/10.24963/ijcai.2021/619
Nakov, Preslav; Da-San-Martino, Giovanni; Elsayed, Tamer; Barrón-Cedeño, Alberto; Míguez, Rubén; Shaar, Shaden; Alam, Firoj; Haouari, Fatima; Hasanain, Maram; Mansour, Watheq; Hamdan, Bayan; Ali, Zien-Sheikh; Babulkov, Nikolay; Nikolov, Alex; Shahi, Gautam-Kishore; StruíŸ, Julia-Maria; Mandl, Thomas; Kutlu, Mucahid; Kartal, Yavuz-Selim (2021). "Overview of the clef-2021 checkthat! Lab on detecting check-worthy claims, previously fact-checked claims, and fake news". In: International conference of the cross-language evaluation forum for European languages. Experimental IR meets multilinguality, multimodality, and interaction, pp. 264-291. https://doi.org/10.1007/978-3-030-85251-1_19
Newman, Mark E. J. (2004). "Fast algorithm for detecting community structure in networks". Physical review E, v. 69, n. 6, 066133. https://doi.org/10.1103/PhysRevE.69.066133
Oehmichen, Axel; Hua, Kevin; Amador, Julio; Molina-Solana, Miguel; Gómez-Romero, Juan; Guo, Yi-ke (2019). "Not all lies are equal. A study into the engineering of political misinformation in the 2016 US presidential election". IEEE access, v. 7, pp. 126305-126314. https://doi.org/10.1109/ACCESS.2019.2938389
Paka, William-Scott; Bansal, Rachit; Kaushik, Abhay; Sengupta, Shubhashis; Chakraborty, Tanmoy (2021). "Cross-sean: a cross-stitch semi-supervised neural attention model for Covid-19 fake news detection". Applied soft computing, v. 107. https://doi.org/10.1016/j.asoc.2021.107393
Pasi, Gabriella; De-Grandis, Marco; Viviani, Marco (2020). "Decision making over multiple criteria to assess news credibility in microblogging sites". In: IEEE International conference on fuzzy systems (FUZZ-IEEE). https://doi.org/10.1109/FUZZ48607.2020.9177751
Pennebaker, James W.; Boyd, Ryan L.; Jordan, Kayla; Blackburn, Kate (2015). The development and psychometric properties of LIWC2015. Austin, TX: University of Texas at Austin. https://repositories.lib.utexas.edu/handle/2152/31333
Pennington, Jeffrey; Socher, Richard; Manning, Christopher (2014). "GloVe: global vectors for word representation". In: Proceedings of the 2014 Conference on empirical methods in natural language processing (Emnlp), pp. 1532-1543. https://doi.org/10.3115/v1/D14-1162
Qi, Peng; Cao, Juan; Yang, Tianyun; Guo, Junbo; Li, Jintao (2019). "Exploiting multi-domain visual information for fake news detection". In: IEEE International conference on data mining (ICDM), pp. 518-527. https://doi.org/10.1109/ICDM.2019.00062
Rana, Md-Shohel; Nobi, Mohammad-Nur; Murali, Beddhu; Sung, Andrew H. (2022). "Deepfake detection: a systematic literature review". IEEE access, v. 10, pp. 25494-25513. https://doi.org/10.1109/ACCESS.2022.3154404
Rashkin, Hannah; Choi, Eunsol; Jang, Jin Y.; Volkova, Svitlana; Choi, Yejin (2017). "Truth of varying shades: analyzing language in fake news and political fact-checking". In: Proceedings of the 2017 Conference on empirical methods in natural language processing, pp. 2931-2937. https://doi.org/10.18653/v1/D17-1317
Rath, Bhavtosh; Salecha, Aadesh; Srivastava, Jaideep (2022). "Fake news spreader detection using trust-based strategies in social networks with bot filtration". Social network analysis and mining, v. 12, n. 66. https://doi.org/10.1007/s13278-022-00890-z
Ruffo, Giancarlo; Semeraro, Alfonso; Giachanou, Anastasia; Rosso, Paolo (2023). "Studying fake news spreading, polarisation dynamics, and manipulation by bots: a tale of networks and language". Computer science review, v. 47, 100531. https://doi.org/10.1016/j.cosrev.2022.100531
Russell, Stuart; Norvig, Peter (2020). Artificial intelligence: a modern approach. Pearson Series. ISBN: 978 0 134 610993
Saif, Shahela; Tehseen, Samabia (2022). "Deepfake videos: synthesis and detection techniques - a survey". Journal of intelligent and fuzzy systems, v. 42, n. 4, pp. 2989-3009. https://doi.org/10.3233/JIFS-210625
Schuster, Tal; Schuster, Roei; Shah, Darsh J.; Barzilay, Regina (2020). "The limitations of stylometry for detecting machine-generated fake news". Computational linguistics, v. 46, n. 2, pp. 499-510. https://doi.org/10.1162/coli_a_00380
Serengil, Sefik I.; Ozpinar, Alper (2021). "HyperExtended lightface: a facial attribute analysis framework". In: International conference on engineering and emerging technologies (Iceet). https://doi.org/10.1109/ICEET53442.2021.9659697
Serrano-Guerrero, Jesús; Olivas, José A.; Romero, Francisco P.; Herrera-Viedma, Enrique (2015). "Sentiment analysis: a review and comparative analysis of web services". Information sciences, v. 311, pp. 18-38. https://doi.org/10.1016/j.ins.2015.03.040
Shabani, Shaban; Charlesworth, Zarina; Sokhn, Maria; Schuldt, Heiko (2021). "SAMS: human-in-the-loop approach to combat the sharing of digital misinformation". CEUR workshop proceedings, v. 2846. https://ceur-ws.org/Vol-2846/paper27.pdf
Shao, Chengcheng; Ciampaglia, Giovanni-Luca; Varol, Onur; Yang, Kai-Cheng; Flammini, Alessandro; Menczer, Filippo (2018). "The spread of low-credibility content by social bots". Nature communications, v. 9, n. 1, pp. 4787. https://doi.org/10.1038/s41467-018-06930-7
Shao, Chengcheng; Hui, Pik-Mai; Wang, Lei; Jiang, Xinwen; Flammini, Alessandro; Menczer, Filippo; Ciampaglia, Giovanni-Luca (2018). "Anatomy of an online misinformation network". Plos one, v. 13, n. 4, e0196087. https://doi.org/10.1371/journal.pone.0196087
Shrestha, Anu; Spezzano, Francesca (2022). "Characterizing and predicting fake news spreaders in social networks". International journal of data science and analytics, v. 13, n. 4, pp. 385-398. https://doi.org/10.1007/s41060-021-00291-z
Shu, Kai; Sliva, Amy; Wang, Suhang; Tang, Jiliang; Liu, Huan (2017). "Fake news detection on social media: a data mining perspective". ACM SIGKDD explorations newsletter, v. 19, n. 1, pp. 22-36. https://doi.org/10.1145/3137597.3137600
Shu, Kai; Wang, Suhang; Liu, Huan (2019). "Beyond news contents: the role of social context for fake news detection". In: Proceedings of the 12th ACM International conference on web search and data mining, pp. 312-320. https://doi.org/10.1145/3289600.3290994
Shu, Kai; Zhou, Xinyi; Wang, Suhang; Zafarani, Reza; Liu, Huan (2019). "The role of user profiles for fake news detection". In: Proceedings of the 2019 IEEE/ACM International conference on advances in social networks analysis and mining, pp. 436-439. https://doi.org/10.1145/3341161.3342927
Simko, Jakub; Racsko, Patrik; Tomlein, Matus; Hanakova, Martina; Moro, Robert; Bielikova, Maria (2021). "A study of fake news reading and annotating in social media context". New review of hypermedia and multimedia, v. 27, n. 1-2, pp. 97-127. https://doi.org/10.1080/13614568.2021.1889691
Singh, Prabhav; Srivastava, Ridam; Rana, K. P. S.; Kumar, Vineet (2023). "SEMI-fnd: stacked ensemble based multimodal inferencing framework for faster fake news detection". Expert systems with applications, v. 215, 119302. https://doi.org/10.1016/j.eswa.2022.119302
Solaiman, Irene; Brundage, Miles; Clark, Jack; Askell, Amanda; Herbert-Voss, Ariel; Wu, Jeff; Radford, Alec; Krueger, Gretchen; Kim, Jong-Wook; Kreps, Sarah; McCain, Miles; Newhouse, Alex; Blazakis, Jason; McGuffie, Kris; Wang, Jasmine (2019). "Release strategies and the social impacts of language models". ArXiv. https://doi.org/10.48550/arXiv.1908.09203
Song, Chenguang; Teng, Yiyang; Zhu, Yangfu; Wei, Siqi; Wu, Bin (2022). "Dynamic graph neural network for fake news detection". Neurocomputing, v. 505, pp. 362-374. https://doi.org/10.1016/j.neucom.2022.07.057
Srinivas, P. Y. K. L.; Das, Amitava; Pulabaigari, Viswanath (2022). "Fake spreader is narcissist; real spreader is Machiavellian prediction of fake news diffusion using psycho-sociological facets". Expert systems with applications, v. 207, 117952. https://doi.org/10.1016/j.eswa.2022.117952
Stella, Massimo; Ferrara, Emilio; De-Domenico, Manlio (2018). "Bots increase exposure to negative and inflammatory content in online social systems". Proceedings of the National Scademy of Sciences, v. 115, n. 49, pp. 12435-12440. https://doi.org/10.1073/pnas.1803470115
Tacchini, Eugenio; Ballarin, Gabriele; Della-Vedova, Marco L.; Moret, Stefano; De-Alfaro, Luca (2017). "Some like it hoax: automated fake news detection in social networks". In: CEUR Workshop proceedings, v. 1960. https://arxiv.org/abs/1704.07506
Thorne, James; Vlachos, Andreas (2018). "Automated fact checking: task formulations, methods and future directions". In: Proceedings of the 27th International conference on computational linguistics, pp. 3346-3359. https://aclanthology.org/C18-1283
Tolosana, Rubén; Vera-Rodríguez, Rubén; Fierrez, Julián; Morales, Aythami; Ortega-García, Javier (2020). "Deepfakes and beyond: a survey of face manipulation and fake detection". Information fusion, v. 64, pp. 131-148. https://doi.org/10.1016/j.inffus.2020.06.014
Vaswani, Ashish; Shazeer, Noam; Parmar, Niki; Uszkoreit, Jakob; Jones, Llion; Gomez, Aidan N.; Kaiser, Åukasz; Polosukhin, Illia (2017). "Attention is all you need". In: 31st Conference on neural information processing systems. https://papers.neurips.cc/paper/7181-attention-is-all-you-need.pdf
Vogel, Inna; Meghana, Meghana (2020). "Fake news spreader detection on Twitter using character n-grams". In: CEUR Workshop proceedings, v. 2696. https://ceur-ws.org/Vol-2696/paper_59.pdf
Vosoughi, Soroush; Roy, Deb; Aral, Sinan (2018). "The spread of true and false news online". Science, v. 359, n. 6380, pp. 1146-1151. https://doi.org/10.1126/science.aap9559
Wang, Tingting; Liu, Hongyan; He, Jun; Du, Xiaoyong (2013). "Mining user interests from information sharing behaviors in social media". In: Pacific-Asia conference on knowledge discovery and data mining, pp. 85-98. https://doi.org/10.1007/978-3-642-37456-2_8
Wang, William Y. (2017). ""˜Liar, liar pants on fire´: a new benchmark dataset for fake news detection". In: 55th Annual meeting of the Association for Computational Linguistics, v. 2, pp. 422-426. https://doi.org/10.18653/v1/P17-2067
Wang, Yaqing; Ma, Fenglong; Jin, Zhiwei; Yuan, Ye; Xun, Guangxu; Jha, Kishlay; Su, Lu; Gao, Jing (2018). "EANN: event adversarial neural networks for multi-modal fake news detection". In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, pp. 849-857. https://doi.org/10.1145/3219819.3219903
Wardle, Claire; Derakhshan, Hossein (2017). Information disorder: toward an interdisciplinary framework for research and policy making. Council of Europe report. https://rm.coe.int/information-disorder-toward-an-interdisciplinary-framework-for-researc/168076277c
Xiong, Shufeng; Zhang, Guipei; Batra, Vishwash; Xi, Lei; Shi, Lei; Liu, Liangliang (2023). "Trimoon: two-round inconsistency-based multi-modal fusion network for fake news detection". Information fusion, v. 93, pp. 150-158. https://doi.org/10.1016/j.inffus.2022.12.016
Xu, Fan; Sheng, Victor S.; Wang, Mingwen (2023). "A unified perspective for disinformation detection and truth discovery in social sensing: a survey". ACM computing surveys, v. 55, n. 1. https://doi.org/10.1145/3477138
Yang, Jing; Vega-Oliveros, Didier; Seibt, Tais; Rocha, Anderson (2021). "Scalable fact-checking with human-in-the-loop". In: IEEE International workshop on information forensics and security (WIFS). https://doi.org/10.1109/WIFS53200.2021.9648388
Yang, Shuo; Shu, Kai; Wang, Suhang; Gu, Renjie; Wu, Fan; Liu, Huan (2019). "Unsupervised fake news detection on social media: a generative approach". Proceedings of the AAAI Conference on artificial intelligence, v. 33, n. 1, pp. 5644-5651. https://doi.org/10.1609/aaai.v33i01.33015644
Yin, Zhijun; Cao, Liangliang; Gu, Quanquan; Han, Jiawei (2012). "Latent community topic analysis". ACM transactions on intelligent systems and technology, v. 3, n. 4. https://doi.org/10.1145/2337542.2337548
Zhang, Guobiao; Giachanou, Anastasia; Rosso, Paolo (2022). "SceneFND: multimodal fake news detection by modelling scene context information". Journal of information science, Online first. https://doi.org/10.1177/01655515221087683
Zhang, Xichen; Ghorbani, Ali A. (2020). "An overview of online fake news: characterization, detection, and discussion". Information processing and management, v. 57, n. 2. https://doi.org/10.1016/j.ipm.2019.03.004
Zhou, Xinyi; Jain, Atishay; Phoha, Vir V.; Zafarani, Reza (2020). "Fake news early detection". Digital threats: research and practice, v. 1, n. 2. https://doi.org/10.1145/3377478
Zhou, Xinyi; Zafarani, Reza (2020). "A survey of fake news: fundamental theories, detection methods, and opportunities". ACM computing surveys, v. 53, n. 5. https://doi.org/10.1145/3395046
Zhu, Q.; Luo, J. (2022). "Generative pre-trained transformer for design concept generation: an exploration". Proceedings of the design society, v. 2, pp. 1825-1834. https://doi.org/10.1017/pds.2022.185
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Profesional de la información
This work is licensed under a Creative Commons Attribution 4.0 International License.
Dissemination conditions of the articles once they are published
Authors can freely disseminate their articles on websites, social networks and repositories
However, the following conditions must be respected:
- Only the editorial version should be made public. Please do not publish preprints, postprints or proofs.
- Along with this copy, a specific mention of the publication in which the text has appeared must be included, also adding a clickable link to the URL: http://www.profesionaldelainformacion.com
- Only the final editorial version should be made public. Please do not publish preprints, postprints or proofs.
- Along with that copy, a specific mention of the publication in which the text has appeared must be included, also adding a clickable link to the URL: http://revista.profesionaldelainformacion.com
Profesional de la información journal offers the articles in open access with a Creative Commons BY license.
