روش‌های شناسایی اخبار جعلی: مطالعه مروری سیستماتیک
محورهای موضوعی : ارتباطاتکریم شعبانی 1 , علی گرانمایه پور 2 , شهناز هاشمی 3
1 - دانشجوی دکتری علوم ارتباطات اجتماعی، واحد بین‌الملل قشم، دانشگاه آزاد اسلامی، قشم، ایران
2 - استادیار، گروه علوم ارتباطات، واحد تهران مرکزی، دانشگاه آزاد اسلامی، تهران، ایران
3 - عضو هیأت علمی پژوهشگاه مطالعات آموزش و پرورش، سازمان پژوهش و برنامه ریزی آموزشی، تهران، ایران
کلید واژه: تشخیص اخبار جعلی, اخبار جعلی, تحلیل اخبار جعلی,
چکیده مقاله :
مقدمه: اخبار جعلی با توجه به دسترسی افراد به شبکه‌های اجتماعی و پلتفرم‌هایی که از قابلیت نظارت مستقیم برخوردار نیستند، روز به روز در حال گسترش است. در نتیجه دانستن شیوه‌های تشخیص یک خبر جعلی می‌تواند گامی مؤثر را در کاهش این گونه تأثیرگذاری‌ها داشته باشد. روش پژوهش: دراین مطالعه مرور سیستماتیک، مقاله حاصل از پژوهش‌های انجام شده با موضوع شناسایی اخبار جعلی در جهان در دوره‌ی زمانی 2015 تا 2020 در مجلات معتبر علمی و پژوهشی داخلی و خارجی منتشر شده و در بانک‌های اطلاعاتی داخلی و بین‌المللی جمع‌آوری شد. در نهایت با اعمال معیار‌های ورود و خروج 43 مقاله مورد بررسی قرار گرفت. یافته‌ها: با استفاده از مطالعات انجام شده مقالات در دو دسته کلی طبقه‌بندی شد: 1- روش شناسایی انسانی، 2- روش شناسایی خودکار یا ماشینی. شاخص‌های ارائه شده در مورد موضوع و پیشرفت‌های حاصل شده در این زمینه، باز نیز حاکی از ضعف بسیاری در شناخت اخبار جعلی دارد. از یک سو مدل‌های ماشینی که از پایگاه داده استفاده می‌کنند، نمی‌توان مدلی را ارائه داد که در فضای واقعی به درستی عمل نماید و از سوی دیگر استفاده از روش‌های انسانی بسیار زمان‌بر است. نتیجه‌گیری: به نظر می‌رسد هنوز ضعف در تشخیص وجود دارد و در بسیاری از موارد مواجه با فضای واقعی خوب عمل نشده است، با این حال در سال‌های اخیر شبکه اجتماعی توئیتر بسیار بهتر از دیگر شبکه‌ها عمل کرده است.
Introduction: Fake news is spreading day by day due to individuals’ accessibility to social networks and those platforms with a lack of direct monitoring capability. Thus, understanding how to distinguish fake news can be a significant step in reducing such influences. Method: In this systematic review, original articles were obtained from those research conducted on the subject of identifying fake news in the world in the period 2015 to 2020, which was published in prestigious domestic and international scientific journals and collected in local and international databases. Finally, after considering the inclusion and exclusion criteria, 43 articles were reviewed. Results: By careful review of included studies, those articles were classified into two general categories: 1. Human identification method. 2. Automatic or machine identification method. The indicators presented on the subject and the progress made in this area also indicate many weaknesses in recognizing fake news. On the one hand, machine models that use databases, it is not possible to provide a model that works properly in the real world, and on the other hand, using human methods is very time-consuming. Conclusion: It seems that there is still a weakness in diagnosis, and in many cases, the real world has not been treated well; however, in recent years, the social network Twitter has performed much better than other systems.
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Allcott, Hunt. & Gentzkow, Matthew. (2017). “Social Media and Fake News in the 2016 Election”, Journal of Economic Perspectives, Vol. 31(2), PP.211-236. doi:10. 1257/jep. 31. 2. 211.
Bakhteev, Oleg., Ogaltsov, Aleksandr. & Ostroukhov, Petr. (2020). “Fake News Spreader Detection using Neural Tweet Aggregation”, Paper presented at the CLEF.
Baruah, Arup., Das, K., Barbhuiya, F. & Dey, Kuntal. (2020). “Automatic Detection of Fake News Spreaders Using BERT”, Paper presented at the CLEF.
Benson, Rodney. (2004). “Bringing the sociology of media back in”, Political, Communication, Vol. 21(3), PP. 275-292.
Bourgonje, Peter., Schneider, Julian Moreno. & Rehm, Georg. (2017). “From clickbait to fake news detection: an approach based on detecting the stance of headlines to articles”, Paper presented at the Proceedings of the 2017 EMNLP Workshop: Natural Language Processing meets Journalism.
Cardaioli, Matteo., Cecconello, Stefano., Conti, Mauro., Pajola, Luca. & Turrin, Federico. (2020). “Fake News Spreaders Profiling Through Behavioural Analysis”, Paper presented at the CLEF.
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, Vol. 35(2), PP. 165-180.
De Grandis, Marco., Pasi, Gabriella. & Viviani, Marco. (2019). “Fake news detection in microblogging through quantifier-guided aggregation”, Paper presented at the International Conference on Modeling Decisions for Artificial Intelligence.
Esmaieli, A., Rahimi, S. & Moradi, M. (2019). “The Relationship between Information Literacy and the Ability of Library Users to Spot Fake News Based on the Components of IFLA Infographic”, Librarianship and Information Organization Studies (Journal of National Studies on Librarianship and Information Organization), Vol. 30. 1(117), PP. 8-28.
Faustini, Pedro Henrique Arruda. & Covões, Thiago Ferreira. (2020). “Fake news detection in multiple platforms and languages”, Expert Systems with Applications, Vol. 158, 113503.
Fersini, Elisabetta., Armanini, Justin. & D’Intorni, Michael. (2020). “Profiling Fake News Spreaders: Stylometry, Personality, Emotions and Embeddings”, Paper presented at the CLEF.
Gardiner, Harris. & Melissa, Eddy. (2016). “Obama, with Angela Merkel in Berlin”, Assails Spread of Fake News.
Gautam, Akansha. & Jerripothula, Koteswar Rao. (2020). “SGG: Spinbot, Grammarly and GloVe based Fake News Detection”, arXiv preprint arXiv: 06854.
Giachanou, Anastasia., Zhang, Guobiao. & Rosso, Paolo. (2020). “Multimodal Fake News Detection with Textual, Visual and Semantic Information”, Paper presented at the International Conference on Text, Speech, and Dialogue.
Giglou, Hamed Babaei., Razmara, Jafar., Rahgouy, Mostafa. & Sanaei, Mahsa. (2020). “LSACoNet: A Combination of Lexical and Conceptual Features for Analysis of Fake News Spreaders on Twitter”, Paper presented at the CLEF.
Goldman, Russell. (2016). “Reading Fake News”, Pakistani Minister Directs Nuclear Threat at Israel.
Gravanis, Georgios., Vakali, Athena., Diamantaras, Konstantinos. & Karadais, Panagiotis. (2019). “Behind the cues: A benchmarking study for fake news detection”, Expert Systems with Applications, Vol. 128, PP. 201-213. doi:https://doi. org/10. 1016/j. eswa. 2019. 03. 036.
Herman, Edward S. & Chomsky, Noam. (2010). “Manufacturing consent: The political economy of the mass media”, Random House.
Horne, Benjamin D., Nørregaard, Jeppe., Adali, Sibel. (2019). “Robust fake news detection over time and attack”, ACM Transactions on Intelligent Systems, & Technology, Vol. 11(1), PP. 1-23.
Kaliyar, Rohit Kumar., Goswami, Anurag., Narang, Pratik. & Sinha, Soumendu. (2020). “FNDNet – A deep convolutional neural network for fake news detection”, Cognitive Systems Research, Vol. 61, PP. 32-44. doi:https://doi. org/10. 1016/j. cogsys. 2019. 12. 005.
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, PP. 1546-1557.
Karimi, Hamid. & Tang, Jiliang. (2019). “Learning hierarchical discourse-level structure for fake news detection”, arXiv preprint arXiv: 07389.
Kaur, Sawinder., Kumar, Parteek. & Kumaraguru, Ponnurangam. (2020). “Automating fake news detection system using multi-level voting model”, Soft Computing, Vol. 24(12), PP. 9049-9069.
Kershner, James W. (2011). “Elements of News Writing”, Pearson Higher Ed.
Kovach, Bill. & Rosenstiel, Tom. (2014). “The elements of journalism: What newspeople should know and the public should expect”, Three Rivers Press (CA).
Kula, Sebastian., Choraś, Michał., Kozik, Rafał., Ksieniewicz, Paweł. & Woźniak, Michał. (2020). “Sentiment analysis for fake news detection by means of neural networks”, Paper presented at the International Conference on Computational Science.
Kumar, Srijan. & Shah, Neil. (2018). “False information on web and social media: A survey”, arXiv preprint arXiv: 08559.
Le, Thai., Wang, Suhang. & Lee, Dongwon. (2020). “MALCOM: Generating Malicious Comments to Attack Neural Fake News Detection Models”, arXiv preprint arXiv: 01048.
Monti, Federico., Frasca, Fabrizio., Eynard, Davide., Mannion, Damon. & Bronstein, Michael M. (2019). “Fake news detection on social media using geometric deep learning”, arXiv preprint arXiv: 06673.
Nguyen, Duc Minh., Do, Tien Huu., Calderbank, Robert. & Deligiannis, Nikos. (2019). “Fake news detection using deep markov random fields”, Paper presented at the Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Vol. 1 (Long and Short Papers).
Nguyen, Van-Hoang., Sugiyama, Kazunari., Nakov, Preslav. & Kan, Min-Yen. (2020). “FANG: Leveraging Social Context for Fake News Detection Using Graph Representation”, arXiv preprint arXiv: 07939.
Ni, Bo., Guo, Zhichun., Li, Jianing. & Jiang, Meng. (2020). “Improving Generalizability of Fake News Detection Methods using Propensity Score Matching”, arXiv preprint arXiv: 00838.
Ozbay, Feyza Altunbey. & Alatas, Bilal. (2020). “Fake news detection within online social media using supervised artificial intelligence algorithms”, Physica A: Statistical Mechanics and its Applications, 540, 123174. doi:https://doi. org/10. 1016/j. physa. 2019. 123174.
Richardson, Brian. (2007). “The Process of Writing News”, From Information to Story: Allyn & Bacon.
Rini, Regina. (2017). “Fake news and partisan epistemology”, Kennedy Institute of Ethics Journal, Vol. 27(2), E-43-E-64.
Rubin, Victoria L. (2010). “On deception and deception detection: Content analysis of computer‐mediated stated beliefs”, Proceedings of the American Society for Information Science, & Technology, Vol. 47(1), PP. 1-10.
Shoemaker, Pamela J. & Reese, Stephen D. (2013). “Mediating the message in the 21st century”, A media sociology perspective: Routledge.
Shu, Kai., Mahudeswaran, Deepak., Liu, Huan. (2019). “FakeNewsTracker: a tool for fake news collection, detection, and visualization”, Computational, & Theory, Mathematical Organization, Vol. 25(1), PP. 60-71.
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.
Shu, Kai., Zhou, Xinyi., Wang, Suhang., Zafarani, Reza. & Liu, Huan. (2019). “The role of user profiles for fake news detection”, Paper presented at the Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining.
Singhal, Shivangi., Shah, Rajiv Ratn., Chakraborty, Tanmoy., Kumaraguru, Ponnurangam. & Satoh, Shin'ichi. (2019). “SpotFake: A Multi-modal Framework for Fake News Detection”, Paper presented at the 2019 IEEE Fifth International Conference on Multimedia Big Data (BigMM).
Sitaula, Niraj., Mohan, Chilukuri K., Grygiel, Jennifer., Zhou, Xinyi. & Zafarani, Reza. (2020). “Credibility-based fake news detection. In Disinformation”, Misinformation, and Fake News in social media (pp. 163-182): Springer.
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.
Suri, Bhawna., Taneja, Shweta., Aggarwal, Soumya. & Sharma, Vibhakar Raj. (2020). “Fake news detection tool (FNDT): Shield against sentimental deception”, Journal of Information and Optimization Sciences, 1-12. doi:10. 1080/02522667. 2020. 1802125.
Torabi Asr, Fatemeh., Taboada, Maite. (2019). “Big Data and quality data for fake news and misinformation detection”, Big Data, & Society, Vol. 6(1), 2053951719843310.
Tschiatschek, Sebastian., Singla, Adish., Gomez Rodriguez, Manuel., Merchant, Arpit. & Krause, Andreas. (2018). “Fake news detection in social networks via crowd signals”, Paper presented at the Companion Proceedings of the The Web Conference.
Umer, Muhammad., Imtiaz, Zainab., Ullah, Saleem., Mehmood, Arif., Choi, Gyu Sang. & On, Byung-Won. (2020). “Fake News Stance Detection Using Deep Learning Architecture (CNN-LSTM)”, IEEE Access, Vol. 8.
Vogel, Inna. & Meghana, Meghana. (2020). “Fake News Spreader Detection on Twitter using Character N-Grams”, Paper presented at the CLEF.
Vosoughi, Soroush., Roy, Deb. & Aral, Sinan. (2018). “The spread of true and false news online”, Science, 359(6380), 1146. doi:10. 1126/science. aap9559.
Wang, Yaqing., Yang, Weifeng., Ma, Fenglong., Xu, Jin., Zhong, Bin., Deng, Qiang. & Gao, Jing. (2020). “Weak supervision for fake news detection via reinforcement learning”, Paper presented at the Proceedings of the AAAI Conference on Artificial Intelligence.
Wardle, Claire. (2017). “Fake news. It’s complicated”, First Draft.
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