Examining the Models Used for Fake News Detection in the Scope of Social Context

Yıl 2023, Cilt: 11 Sayı: 1, 39 - 54, 25.03.2023

Gülsüm Kayabaşı Koru , Çelebi Uluyol

https://doi.org/10.29109/gujsc.1145516

Öz

While in traditional news media, the content of the news is based on fake news detection, social context information in social media can be used to help detect fake news. Considering the social context, the distribution of data on social media and the interaction of online users with each other, it also explains the dissemination of news in the social environment and provides the necessary information to determine whether the news is true or not. Social media supports models based on news content. Developing these models provides some additional resources for researchers. Social context information represents three main topics: user detail, post and network analysis. In this study, a compilation study was conducted on the social context-based features and models of fake news from a data science perspective. Studies using these features and models in the literature have been examined with both machine learning and deep learning approaches. Analysis of 9 known data sets created for feature extraction and fake news detection was performed.

Anahtar Kelimeler

misinformation, fabricated news, fake news detection, social context

Sahte Haber Tespiti için Kullanılan Modellerin Sosyal Bağlam Kapsamında İncelenmesi

Öz

Geleneksel haber medyasında, sahte haber tespiti için haberin içeriği esas alınırken, sosyal medyada sosyal bağlam bilgileri sahte haberleri tespit etmeye yardımcı olmak için kullanılabilmektedir. Sosyal bağlam, verilerin sosyal medyada dağıtımı ve çevrimiçi kullanıcıların birbirleri ile etkileşimi de göz önünde bulundurularak haberlerin sosyal çevrede yayılımını da açıklayarak haberlerin doğru olup olmadığını tespit etmek maksadıyla gerekli bilgileri sağlamaktadır. Sosyal medya, haber içeriğine dayalı modelleri desteklemektedir. Bu modelleri geliştirmek araştırmacılar için ek bazı kaynaklar sunmaktadır. Sosyal bağlam bilgisi kullanıcı detayı, gönderi ve ağ analizi olmak üzere üç ana başlığı temsil etmektedir. Bu çalışmada veri bilimi perspektifinden sahte haberlerin sosyal bağlama dayalı özellikleri ve modelleri konusunda derleme çalışması yapılmıştır. Literatürde bu özellik ve modelleri kullanan çalışmalar hem makine öğrenmesi hem de derin öğrenme yaklaşımıyla incelenmiştir. Öznitelik çıkarımı ve sahte haber tespitine yönelik oluşturulan 9 adet bilinen veri setinin analizi yapılmıştır.

Anahtar Kelimeler

yanlış bilgilendirme, uydurma haber, sahte haber tespiti, sosyal bağlam

Kaynakça

• [1] Shu, K., Wang, S., Lee, D., & Liu, H. (2020). Mining disinformation and fake news: Concepts, methods, and recent advancements. In Disinformation, Misinformation, and Fake News in Social Media (pp. 1-19). Springer, Cham.
• [2] Edson C Tandoc Jr, Zheng Wei Lim, and Richard Ling. Defining fake news a typology of scholarly definitions. Digital journalism, 6(2):137{153, 2018.Edson C Tandoc Jr, Zheng Wei Lim, and Richard Ling. Defining fake news a typology of scholarly definitions. Digital journalism, 6(2):137{153, 2018.
• [3] Kai Shu and Huan Liu. Detecting fake news on social media. Synthesis Lectures on Data Mining and Knowledge Discovery, 2019.
• [4] Christopher Thomas Hidey and Kathleen McKeown. Persuasive inuence detection: The role of argument sequencing. In Thirty-Second AAAI Conference on Arti_cial Intelligence, 2018.
• [5] Xinyi Zhou, Reza Zafarani, Kai Shu, and Huan Liu. Fake news: Fundamental theories, detection strategies and challenges. In WSDM, 2019.
• [6] Kai Shu, H. Russell Bernard, and Huan Liu. Studying fake news via network analysis: Detection and mitigation. CoRR, abs/1804.10233, 2018.
• [7] Kai Shu, Deepak Mahudeswaran, SuhangWang, and Huan Liu. Hierarchical propagation networks for fake news detection: Investigation and exploitation. In ICWSM, 2020.
• [8] Kai Shu, Suhang Wang, and Huan Liu. 2018. Understanding User Profiles on Social Media for Fake News Detection. In 2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR). IEEE.
• [9] Juan Cao, Junbo Guo, Xirong Li, Zhiwei Jin, Han Guo, and Jintao Li. Automatic rumor detection on microblogs: A survey. arXiv preprint arXiv:1807.03505, 2018.
• [10] Antoine Bosselut, Hannah Rashkin, Maarten Sap, Chaitanya Malaviya, Asli Celikyilmaz, and Yejin Choi. Comet: Commonsense transformers for automatic knowledge graph construction. arXiv preprint arXiv:1906.05317, 2019.
• [11] Shu, K., Sliva, A., Wang, S., Tang, J., & Liu, H. (2017). Fake news detection on social media: A data mining perspective. ACM SIGKDD explorations newsletter, 19(1), 22-36.
• [12] Wang, W. Y. 2017. ” liar, liar pants on fire”: A new benchmark dataset for fake news detection. arXiv preprint arXiv:1705.00648.
• [13] Castillo, C.; Mendoza, M.; and Poblete, B. 2011. Information credibility on twitter. In Proceedings of the 20th international conference on World wide web, 675–684. ACM.
• [14] Wu, L., and Liu, H. 2018. Tracing fake-news footprints: Characterizing social media messages by how they propagate. In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining (WSDM), 637–645. ACM.
• [15] Ma, J.; Gao,W.;Wei, Z.; Lu, Y.; andWong, K.-F. 2015. Detect rumors using time series of social context information on microblogging websites. In Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, 1751– 1754. ACM.
• [16] Kim, J.; Tabibian, B.; Oh, A.; Sch¨olkopf, B.; and Gomez- Rodriguez, M. 2018. Leveraging the crowd to detect and reduce the spread of fake news and misinformation. In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining (WSDM), 324–332. ACM.
• [17] Kai Shu, Deepak Mahudeswaran, Suhang Wang, Dongwon Lee, and Huan Liu. 2018. FakeNewsNet: A Data Repository with News Content, Social Context and Dynamic Information for Studying Fake News on Social Media. arXiv preprint arXiv:1809.01286 (2018).
• [18 Ozbay, Feyza Altunbey, and Bilal Alatas. (2020). Fake news detection within online social media using supervised artificial intelligence algorithms. Physica A: Statistical Mechanics and its Applications, 540, 123174.
• [19] Ozbay, Feyza Altunbey, and Bilal Alatas. "A novel approach for detection of fake news on social media using metaheuristic optimization algorithms." Elektronika ir Elektrotechnika 25.4 (2019): 62-67.
• [20] Ozbay, Feyza Altunbey, and Bilal Alatas. (2021). Adaptive Salp swarm optimization algorithms with inertia weights for novel fake news detection model in online social media. Multimedia Tools and Applications, 80(26), 34333-34357.
• [21] Chauhan, T., & Palivela, H. (2021). Optimization and improvement of fake news detection using deep learning approaches for societal benefit. International Journal of Information Management Data Insights, 1(2), 100051.
• [22] Ansar, W., & Goswami, S. (2021). Combating the menace: A survey on characterization and detection of fake news from a data science perspective. International Journal of Information Management Data Insights, 1(2), 100052.
• [23] Kabudi, T., Pappas, I., & Olsen, D. H. (2021). Ai-enabled adaptive learning systems: A systematic mapping of the literature. Computers and Education: Artificial Intelligence, 2, 100017.
• [24] Yang,S., Shu,K., Wang,S., Gu,R., Wu,F., and Liu,H. Unsupervised Fake News Detection on Social Media: A Generative Approach. In AAAI’19.
• [25] Shu,K., Mahudeswaran,D., and Liu,H. 2018. FakeNewsTracker: a tool for fake news collection, detection, and visualization. Computational and Mathematical Organization Theory (2018), 1–12.
• [26] Karimi, H., Roy,P., Saba-Sadiya,S., and Tang,J. Multi-Source Multi-Class Fake News Detection. In COLING’18.
• [27] Kwon,S., Cha,M., Jung,K., Chen,W., and Wang,Y. 2013. Prominent features of rumor propagation in online social media. In Data Mining (ICDM), 2013 IEEE 13th International Conference on. IEEE, 1103–1108.
• [28] Wang, Y., Ma,F., Jin,Z., Yuan,Y., Xun,G., Jha,K., Su,L., and Gao,J. EANN: Event Adversarial Neural Networks for Multi-Modal Fake News Detection. In KDD’18.
• [29] Shu, K., Zhou, X., Wang, S., Zafarani, R., & Liu, H. (2019, August). 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).
• [30] Yang, F., Liu, Y., Yu, X., & Yang, M. (2012). Automatic detection of rumor on Sina Weibo. In Proceedings of the ACM SIGKDD workshop on mining data semantics (pp. 1–7).
• [31] Castillo, C., Mendoza, M., & Poblete, B. (2013). Predicting information credibility in time-sensitive social media. Internet Research.
• [32] Jin, Z., Cao, J., Zhang, Y., & Luo, J. (2016, March). News verification by exploiting conflicting social viewpoints in microblogs. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 30, No. 1).
• [33] Rubin, V. L., Conroy, N., Chen, Y., & Cornwell, S. (2016). Fake news or truth? Using satir- ical cues to detect potentially misleading news. In Proceedings of the second workshop on computational approaches to deception detection (pp. 7–17).
• [34] Verma, P. K., Agrawal, P., Amorim, I., & Prodan, R. (2021). Welfake: Word embedding over linguistic features for fake news detection. IEEE Transactions on Computational Social Systems.
• [35] Tacchini, E., Ballarin, G., Della Vedova, M. L., Moret, S., & de Alfaro, L. (2017). Some like it hoax: Automated fake news detection in social networks. arXiv preprint arXiv:1704.07506
• [36] 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 (pp. 797–806).
• [37] Mohammad, S. M., Sobhani, P., & Kiritchenko, S. (2017). Stance and sentiment in tweets. ACM Transactions on Internet Technology (TOIT), 17(3), 1–23.
• [38] Shu, K., Wang, S., & Liu, H. (2019, January). 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 (pp. 312-320).
• [39] Schwartz, H. A., Eichstaedt, J. C., Kern, M. L., Dziurzynski, L., Ramones, S. M., Agrawal, M., ... & Ungar, L. H. (2013). Personality, gender, and age in the language of social media: The open-vocabulary approach. PloS one, 8(9), e73791.
• [40] Jin, Z., Cao,J., Jiang, Y. and Zhang,Y. “News credibility evaluation on microblog with a hierarchical propagation model,” In Data Mining (ICDM), 2014 IEEE International Conference, 2014, 230–239.
• [41] Shrivastava, G., Kumar, P., Ojha, R. P., Srivastava, P. K., Mohan, S., & Srivas- tava, G. (2020). Defensive modeling of fake news through online social networks. IEEE Transactions on Computational Social Systems, 7(5), 1159–1167.
• [42] Mukherjee, A., Venkataraman, V., Liu, B., & Glance, N. (2013, June). What yelp fake review filter might be doing?. In Proceedings of the international AAAI conference on web and social media (Vol. 7, No. 1).
• [43] Granik, M., & Mesyura, V. (2017, May). Fake news detection using naive Bayes classifier. In 2017 IEEE first Ukraine conference on electrical and computer engineering (UKRCON) (pp. 900-903). IEEE.
• [44] Gravanis, G., Vakali, A., Diamantaras, K., & Karadais, P. (2019). Behind the cues: A benchmarking study for fake news detection. Expert Systems with Applications, 128, 201-213.
• [45] TAŞKIN, S. G., Küçüksille, E. U., & Topal, K. Twitter üzerinde Türkçe sahte haber tespiti. Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 23(1), 151-172.
• [46] Long, Y., Lu, Q., Xiang, R., Li, M., & Huang, C. R. (2017, November). Fake news detection through multi-perspective speaker profiles. In Proceedings of the eighth international joint conference on natural language processing (volume 2: Short papers) (pp. 252-256).
• [47] Roy, A., Basak, K., Ekbal, A., & Bhattacharyya, P. (2018). A deep ensemble framework for fake news detection and classification. arXiv preprint arXiv:1811.04670.
• [48] Jwa, H., Oh, D., Park, K., Kang, J. M., & Lim, H. (2019). exbake: Automatic fake news detection model based on bidirectional encoder representations from transformers (bert). Applied Sciences, 9(19), 4062.
• [49] Polignano, M., de Pinto, M. G., Lops, P., & Semeraro, G. (2019, September). Identification Of Bot Accounts In Twitter Using 2D CNNs On User-generated Contents. In Clef (working notes).
• [50] Baruah, A., Das, K. A., Barbhuiya, F. A., & Dey, K. (2020, September). Automatic Detection of Fake News Spreaders Using BERT. In CLEF (Working Notes).
• [51] 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. Information Processing & Management, 58(1), 102437.
• [52] Paka, W. S., Bansal, R., Kaushik, A., Sengupta, S., & Chakraborty, T. (2021). Cross-SEAN: A cross-stitch semi-supervised neural attention model for COVID-19 fake news detection. Applied Soft Computing, 107, 107393.
• [53] Nasir, J. A., Khan, O. S., & Varlamis, I. (2021). Fake news detection: A hybrid CNN-RNN based deep learning approach. International Journal of Information Management Data Insights, 1(1), 100007.
• [54] Taskin, S. G., Kucuksille, E. U., & Topal, K. (2022). Detection of Turkish Fake News in Twitter with Machine Learning Algorithms. Arabian Journal for Science and Engineering, 47(2), 2359-2379.
• [55] Ahmed, H., Traore, I., & Saad, S. (2017, October). Detection of online fake news using n-gram analysis and machine learning techniques. In International conference on intelligent, secure, and dependable systems in distributed and cloud environments(pp. 127-138). Springer, Cham.
• [56] Ahmed, H., Traore, I., & Saad, S. (2018). Detecting opinion spams and fake news using text classification. Security and Privacy, 1(1), e9.
• [57] Santia, G., & Williams, J. (2018). BuzzFace: A news veracity dataset with facebook user commentary and egos. In Proceedings of the international AAAI conference on web and social media: vol. 12.