Ways to detect fake news in the media from the point of view of communication specialists and professionals

Shabani, Karim; Geranmayepour, Ali; Hashemi, Shahnaz

doi:10.22054/nms.2022.60020.1178

Document Type : Research Paper

Authors

¹ Ph.D. student, Department of Social Communication Sciences, Qeshm Branch, Islamic Azad University, Qeshm, Iran

² Assistant Professor of Communication Sciences, Central Tehran Branch, Islamic Azad University (Central Tehran Branch), Tehran, Iran

³ Faculty member of the Institute of Educational Studies, Educational Research and Planning Organization, Tehran, Iran

https://doi.org/10.22054/nms.2022.60020.1178

Abstract

The present study aimed to discover the factors that detect fake news in the media, especially social networks because the speed of spreading fake news in new media and its negative impact on public opinion is irreparable in some cases. The method of this qualitative study and its statistical population included experts in the field of communication sciences, media, and news, which were selected from the method of targeted sampling and snowball (chain reference) based on the inclusion criteria. The required information was obtained by using documentary studies and semi-structured interviews for 1720 minutes and 15 interviews with publication saturation conditions and the method of receiving participants' feedback was used to validate the thematic analysis. The results were analyzed using MAXQDA 2020 software and the thematic analysis method. The results of the analysis were formed in the form of 439 key concepts and 57 codes, 12 sub-themes, and 5 main themes of "content structure", "publishing agent", "news source", "rules", and "machine algorithms". Interpretation and analysis of data showed that the detection of fake news in the media and social networks in Iran, above all, needed to create a database to compare and model the methods of writing fake news and detect it, and more research is needed in the field. Content analysis is done in Persian to get a better understanding of fake news writing patterns in Persian.

Keywords

20.1001.1.25382209.1401.8.30.8.4

References

Ahmed, Hadeer, Traore, Issa, & Saad, Sherif. (2018). Detecting opinion spams and fake news using text classification. Security and Privacy, 1(1), 1-15. doi:https://doi.org/10.1002/spy2.9

Allcott, Hunt, & Gentzkow, Matthew. (2017). Social Media and Fake News in the 2016 Election. Journal of Economic Perspectives, 31(2), 211-236. doi:10.1257/jep.31.2.211

Antoun, W., Baly, F., Achour, R., Hussein, A., & Hajj, H. (2020, 2-5 Feb. 2020). State of the Art Models for Fake News Detection Tasks. Paper presented at the 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT).

Baek, Young Min, Kang, Hyunhee, & Kim, Sonho. (2019). Fake News Should Be Regulated Because It Influences Both “Others” and “Me”: How and Why the Influence of Presumed Influence Model Should Be Extended. Mass Communication and Society, 22(3), 301-323. doi:10.1080/15205436.2018.1562076

Balwant, M. K. (2019, 6-8 July 2019). Bidirectional LSTM Based on POS tags and CNN Architecture for Fake News Detection. Paper presented at the 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT).

Bozarth, Lia, & Budak, Ceren. (2020). Toward a Better Performance Evaluation Framework for Fake News Classification. Proceedings of the International AAAI Conference on Web and Social Media, 14(1), 60-71.

Cao, Juan, Qi, Peng, Sheng, Qiang, Yang, Tianyun, Guo, Junbo, & Li, Jintao. (2020). Exploring the role of visual content in fake news detection. Disinformation, Misinformation, Fake News in Social Media, 141-161. doi:https://doi.org/10.1007/978-3-030-42699-6_8

Chen, Zhouhan, & Freire, Juliana. (2020). Proactive Discovery of Fake News Domains from Real-Time Social Media Feeds. Paper presented at the Companion Proceedings of the Web Conference 2020, Taipei, Taiwan. https://doi.org/10.1145/3366424.3385772

Choraś, Michał, Pawlicki, Marek, Kozik, Rafał, Demestichas, Konstantinos, Kosmides, Pavlos, & Gupta, Manik. (2019). SocialTruth Project Approach to Online Disinformation (Fake News) Detection and Mitigation. Paper presented at the Proceedings of the 14th International Conference on Availability, Reliability and Security, Canterbury, CA, United Kingdom. https://doi.org/10.1145/3339252.3341497

Corbu, Nicoleta, Oprea, Denisa-Adriana, Negrea-Busuioc, Elena, & Radu, Loredana. (2020). ‘They can’t fool me, but they can fool the others!’ Third person effect and fake news detection. European Journal of Communication, 35(2), 165-180. doi:10.1177/0267323120903686

Ghanem, Bilal, Ponzetto, Simone Paolo, & Rosso, Paolo. (2020, 2020//). FacTweet: Profiling Fake News Twitter Accounts. Paper presented at the Statistical Language and Speech Processing, Cham.

Guo, Chuan, Cao, Juan, Zhang, Xueyao, Shu, Kai, & Yu, Miao. (2021). Exploiting Emotions for Fake News Detection on Social Media. Ljubljana, Slovenia. ACM,: New York, NY, USA.

Hamdi, Tarek, Slimi, Hamda, Bounhas, Ibrahim, & Slimani, Yahya. (2020, 2020//). A Hybrid Approach for Fake News Detection in Twitter Based on User Features and Graph Embedding. Paper presented at the Distributed Computing and Internet Technology, Cham.

Hammad, A. M., Hamed, R., Al-Qerem, W., Bandar, A., & Hall, F. S. (2021). Optimism Bias, Pessimism Bias, Magical Beliefs, and Conspiracy Theory Beliefs Related to COVID-19 among the Jordanian Population. Am J Trop Med Hyg, 1-19. doi:10.4269/ajtmh.20-1412

Hossain, Md Zobaer, Rahman, Md Ashraful, Islam, Md Saiful, & Kar, Sudipta. (2020). BanFakeNews: A dataset for detecting fake news in bangla. European Language Resources Association.

Karimi, Hamid, Roy, Proteek, Saba-Sadiya, Sari, & Tang, Jiliang. (2018). Multi-source multi-class fake news detection. Paper presented at the Proceedings of the 27th International Conference on Computational Linguistics.

Kaur, Sawinder, Kumar, Parteek, & Kumaraguru, Ponnurangam. (2020). Automating fake news detection system using multi-level voting model. Soft Computing, 24(12), 9049-9069. doi:10.1007/s00500-019-04436-y

Khattar, Dhruv, Goud, Jaipal Singh, Gupta, Manish, & Varma, Vasudeva. (2019). MVAE: Multimodal Variational Autoencoder for Fake News Detection. Paper presented at the The World Wide Web Conference, San Francisco, CA, USA. https://doi.org/10.1145/3308558.3313552

Meel, Priyanka, & Vishwakarma, Dinesh Kumar. (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, 153, 1-26. doi:https://doi.org/10.1016/j.eswa.2019.112986

Narwal, B. (2018, 12-13 Oct. 2018). Fake News in Digital Media. Paper presented at the 2018 International Conference on Advances in Computing, Communication Control and Networking (ICACCCN).

Roy, Arjun, Basak, Kingshuk, Ekbal, Asif, & Bhattacharyya, Pushpak. (2018). A deep ensemble framework for fake news detection and classification. arXiv preprint arXiv:.04670, 1-6.

Rubin, Victoria L. (2010). On deception and deception detection: Content analysis of computer-mediated stated beliefs. Proceedings of the American Society for Information Science and Technology, 47(1), 1-10. doi:https://doi.org/10.1002/meet.14504701124

Sahoo, Somya Ranjan, & Gupta, B. B. (2021). Multiple features based approach for automatic fake news detection on social networks using deep learning. Applied Soft Computing, 100. doi:https://doi.org/10.1016/j.asoc.2020.106983

Scheufele, Dietram A., & Krause, Nicole M. (2019). Science audiences, misinformation, and fake news. Proceedings of the National Academy of Sciences, 116(16), 7662–7669. doi:10.1073/pnas.1805871115

Sharma, Karishma, Qian, Feng, Jiang, He, Ruchansky, Natali, Zhang, Ming, & Liu, Yan. (2019). Combating Fake News: A Survey on Identification and Mitigation Techniques. 10(3%J ACM Trans. Intell. Syst. Technol.), 1-41. doi:10.1145/3305260

Shu, Kai, Mahudeswaran, Deepak, Wang, Suhang, Lee, Dongwon, & Liu, Huan. (2020). FakeNewsNet: A Data Repository with News Content, Social Context, and Spatiotemporal Information for Studying Fake News on Social Media. Big Data, 8(3), 171-188. doi:10.1089/big.2020.0062

Shu, Kai, Wang, Suhang, & Liu, Huan. (2019). Beyond News Contents: The Role of Social Context for Fake News Detection. Paper presented at the Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, Melbourne VIC, Australia. https://doi.org/10.1145/3289600.3290994

Silverman, Craig. (2016). This analysis shows how viral fake election news stories outperformed real news on Facebook. BuzzFeed news, 16.

Steinebach, Martin, Gotkowski, Karol, & Liu, Hujian. (2019). Fake News Detection by Image Montage Recognition. Paper presented at the Proceedings of the 14th International Conference on Availability, Reliability and Security, Canterbury, CA, United Kingdom. https://doi.org/10.1145/3339252.3341487

Tandoc, Edson C., Lim, Zheng Wei, & Ling, Richard. (2018). Defining “Fake News”. Digital Journalism, 6(2), 137-153. doi:10.1080/21670811.2017.1360143

Tandoc Jr, Edson C. (2019). The facts of fake news: A research review. Sociology Compass, 13(9), 9-1. doi:https://doi.org/10.1111/soc4.12724

Terry, Gareth, Hayfield, Nikki, Clarke, Victoria, & Braun, Virginia. (2017). Thematic analysis. The Sage handbook of qualitative research in psychology, 17-37.

Vogel, Inna, & Meghana, Meghana. (2020). Fake News Spreader Detection on Twitter using Character N -Grams Notebook for PAN at CLEF 2020.

Vosoughi, S., Roy, D., & Aral, S. (2018). The spread of true and false news online. Science, 359(6380), 1146-1151. doi:10.1126/science.aap9559

Wang, Yuhang, Wang, Li, Yang, Yanjie, & Lian, Tao. (2021). SemSeq4FD: Integrating global semantic relationship and local sequential order to enhance text representation for fake news detection. Expert Systems with Applications, 166, 1-12. doi:https://doi.org/10.1016/j.eswa.2020.114090

Yanagi, Y., Orihara, R., Sei, Y., Tahara, Y., & Ohsuga, A. (2020, 8-10 July 2020). Fake News Detection with Generated Comments for News Articles. Paper presented at the 2020 IEEE 24th International Conference on Intelligent Engineering Systems (INES).

Zhang, Chaowei, Gupta, Ashish, Kauten, Christian, Deokar, Amit V., & Qin, Xiao. (2019). Detecting fake news for reducing misinformation risks using analytics approaches. European Journal of Operational Research, 279(3), 1036-1052. doi:https://doi.org/10.1016/j.ejor.2019.06.022

Zhang, Dongsong, Zhou, Lina, Kehoe, Juan Luo, & Kilic, Isil Yakut. (2016). What Online Reviewer Behaviors Really Matter? Effects of Verbal and Nonverbal Behaviors on Detection of Fake Online Reviews. Journal of Management Information Systems, 33(2), 456-481. doi:10.1080/07421222.2016.1205907

Zhou, Xinyi, Jain, Atishay, Phoha, Vir V., & Zafarani, Reza. (2020). Fake News Early Detection: A Theory-driven Model. 1(2%J Digital Threats: Research and Practice), 1-25. doi:10.1145/3377478

Zhou, Xinyi, Wu, Jindi, & Zafarani, Reza. (2020). SAFE: Similarity-Aware Multi-modal Fake News Detection. Paper presented at the Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2020.

New Media Studies

Ways to detect fake news in the media from the point of view of communication specialists and professionals

References

References

Volume 8, Issue 30 - Serial Number 30
September 2022
Pages 233-207

Ways to detect fake news in the media from the point of view of communication specialists and professionals

References

References

Volume 8, Issue 30 - Serial Number 30September 2022Pages 233-207

Volume 8, Issue 30 - Serial Number 30
September 2022
Pages 233-207