شناخت پژوهی مطالعات سیاسی

شناخت پژوهی مطالعات سیاسی

فنون مقابله و نبرد هوشمندانه با اخبار جعلی در عصر هوش‌مصنوعی

نوع مقاله : مقاله پژوهشی

نویسنده
دانشکده مدیریت راهبردی دانشگاه عالی دفاع ملی، تهران، ایران
چکیده
تشخیص حقیقت از فریب نیازمند ابزارهای نوآورانه در عصری است که در محاصره اطلاعات نادرست است. اطلاعات نادرست مانند اخبار جعلی و شایعات، تهدیدی جدی برای زیست‌بوم‌های اطلاعاتی، زودباوری و اعتماد عمومی است و ظهور هوش‌مصنوعی پتانسیل زیادی برای تغییر شکل چشم‌انداز مبارزه با اطلاعات نادرست دارد. هدف اصلی این مقاله بررسی فنون مقابله و نبرد هوشمندانه با اخبار جعلی در عصر هوش‌مصنوعی است. پژوهش حاضر، از نظر هدف، کاربردی توسعه‌ای؛ از لحاظ رویکرد، کیفی و بر اساس روش فراترکیب انجام شده است. بر این اساس، جامعه آماری پژوهش، شامل مجموعه مقالاتی است که در بازه زمانی سال‌های 2020 تا 2024 میلادی و با موضوع هوش‌مصنوعی، اخبار جعلی، فنون مقابله و نبرد هوشمندانه به چاپ رسیده‌اند که با روش فراترکیب نهایتاً 33 مقاله از میان 63 مقاله انتخاب گردید. در این پژوهش بر اساس نتایج تحلیل مضمون، متون مرتبط مورد بررسی قرارگرفت و نتایج حاصل از روش تحقیق و تحلیل انجام شده در این پژوهش در قالب مدل مفهومی ترکیبی فنون مقابله و نبرد هوشمندانه با اخبار جعلی در عصر هوش‌مصنوعی ارائه گردید.
کلیدواژه‌ها

عنوان مقاله English

Techniques to confront and fight smartly with fake news in the age of artificial intelligence

نویسنده English

Ahmadreza Tarassoli
Higher National Defense University
چکیده English

Distinguishing truth from deception requires innovative tools in an era of misinformation. Misinformation, such as fake news and rumors, poses a serious threat to information ecosystems, credulity, and public trust, and the emergence of artificial intelligence has great potential to reshape the landscape of combating misinformation. The main purpose of this article is to examine techniques for countering and intelligently combating fake news in the age of artificial intelligence. The present study is developmental in purpose and applied in approach; qualitative in approach, and based on the meta-synthesis method. Accordingly, the statistical population of the study includes a collection of articles published between 2020 and 2024 on the topics of artificial intelligence, fake news, countering techniques, and intelligently combating, and 33 articles were ultimately selected from 63 articles using the meta-synthesis method. In this study, based on the results of the content analysis, related texts were examined, and the results of the research and analysis method conducted in this study were presented in the form of a combined conceptual model of techniques for intelligently combating and fighting fake news in the age of artificial intelligence.

کلیدواژه‌ها English

Artificial intelligence
machine learning
fake news
disinformation
confront and fight and intelligent combat

Abishethvarman, V., Banujan, K., Kumara, B.T.G.S., Ravikumar, N. (2024). Unmasking Fake News with Emotional Cues: A Comparative Study of Sentiment Reasoning Models. In 2024 4th International Conference on Advanced Research in Computing (ICARC). IEEE.
Akhgari, M.R., Momtazi, S. (1402). Application of Artificial Intelligence in News Verification: Detecting Fake News Using News Text and Information from News Publishers. Media and Communication Research, 1(1), 243-268. [In Persian]
Akhtar, M.M., Masood, R., Ikram, M., Kanhere, S.S. (2024). SoK: False Information, Bots and Malicious Campaigns: Demystifying Elements of Social Media Manipulations. In Proceedings of the 19th ACM Asia Conference on Computer and Communications Security. 
Alaa Safaa M., Shati, N.M. (2024). A Survey on Fake News Detection in Social Media Using Graph Neural Networks. Journal of Al-Qadisiyah for Computer Science and Mathematics, 16(2), 23-41.
Alghamdi, J., Lin, Y., Luo, S. (2024). Enhancing hierarchical attention networks with CNN and stylistic features for fake news detection. Expert Systems with Applications, 125024.
Ali, A.M., Ghaleb, F.A., Mohammed, M.S., Alsolami, F.J., Khan, A.I. (2023). Web-informed-augmented fake news detection model using stacked layers of convolutional neural network and deep autoencoder. Mathematics11(9), 1992.
Ayetiran, E.F., Özgöbek, Ö. (2024). A review of deep learning techniques for multimodal fake news and harmful languages detection. IEEE Access.
Baguian, H., Ashley, H.N. (2024). JOKER Track CLEF 2024: the Jokesters’ approaches for retrieving, classifying, and translating wordplay. In Working Notes of the Conference and Labs of the Evaluation Forum (CLEF 2024). CEUR Workshop Proceedings, pp. 1811-1817).
Chan, J. (2024). Online astroturfing: A problem beyond disinformation. Philosophy & Social Criticism, 50(3), 507-528.
Collins, B., Hoang, D.T., Nguyen, N.T., Hwang, D. (2021). Trends in combating fake news on social media–a survey. Journal of Information and Telecommunication5(2).
Dong, X., Sarker, S., Qian, L. (2022, September). Integrating human-in-the-loop into swarm learning for decentralized fake news detection. In 2022 International Conference on Intelligent Data Science Technologies and Applications (IDSTA). IEEE.
Emami Rudsari, Hossein. (2023). Methods and tricks of dealing with fake news. Media and Communication Research, 1(1). [In Persian]
Godel, W., Sanderson, Z., Aslett, K., Nagler, J., Bonneau, R., Persily, N., & Tucker, J. A. (2021). Moderating with the mob: Evaluating the efficacy of real-time crowdsourced fact-checking. Journal of Online Trust and Safety, 1(1).
Ghosh, S., Mitra, P., Nakov, P. (2024, May). Clock against Chaos: Dynamic Assessment and Temporal Intervention in Reducing Misinformation Propagation. In Proceedings of the International AAAI Conference on Web and Social Media. 1(18) 462-473.
Jain, M. K., Gopalani, D., Meena, Y. K. (2024). ConFake: fake news identification using content-based features. Multimedia Tools and Applications, 83(3).
Kitsa, M. (2023). Classification of Fake News in Ukraine and Abroad. State and Regions. Series: Social Communications, 1 (53).
Lakzaei, B., Haghir Chehreghani, M., Bagheri, A. (2024). Disinformation detection using graph neural networks: a survey. Artificial Intelligence Review, 57(3), 52.
La Barbera, D., Maddalena, E., Soprano, M., Roitero, K., Demartini, G., Ceolin, D., ... & Mizzaro, S. (2024). Crowdsourced Fact-checking: Does It Actually Work? Information Processing & Management, 61(5), 103792.
Liu, Z., Zhang, T., Yang, K., Thompson, P., Yu, Z., Ananiadou, S. (2024). Emotion detection for misinformation: A review. Information Fusion, 102300.
 Lybarger, K., Dobbins, N.J., Long, R., Singh, A., Wedgeworth, P., Uzuner, Ö., Yetisgen, M. (2023). Leveraging natural language processing to augment structured social determinants of health data in the electronic health record. Journal of the American Medical Informatics Association, 30(8).
Madani, M., Motameni, H., Roshani, R. (2024). Fake news detection using feature extraction, natural language processing, curriculum learning, and deep learning. International Journal of Information Technology & Decision Making23(03), 1063-1098.
Mahdi, A. S., Shati, N. M. (2024). A Survey on Fake News Detection in social media Using Graph Neural Networks. Journal of Al-Qadisiyah for Computer Science and Mathematics, 16(2), 23-41.
Mittal, T., Chowdhury, S., Guhan, P., Chelluri, S.,  Manocha, D. (2024). Towards determining perceived audience intent for multimodal social media posts using the theory of reasoned action. Scientific Reports14(1), 10606.
Mousavi Loghman, S.A, Mousavi, S.S., Davoodi Zadeh, L., Pishvaei, M. (2023). Presenting a conceptual model of "family productivity" with emphasis on the economic aspect: a content analysis of social welfare, Quarterly Scientific Research Journal of Social Welfare, 23 (91): 323-364. [In Persian]
Nasery, M. (2024). Fake News on social media: From Fake News Lifecycle to Fake News Combat Cycle (Doctoral dissertation).
Pourkarimi, J., Azizi, M. (2024). Leaders' Competencies in Turbulent Environments (A Meta-Synthesis Study). Public Administration, 16(3), [In Persian]
Rastogi, S., Bansal, D. (2023). A review on fake news detection 3T’s: typology, time of detection, taxonomies. International Journal of Information Security22(1).
Sahoo, S.R., Gupta, B.B. (2021). Multiple features-based approach for automatic fake news detection on social networks using deep learning. Applied Soft Computing100, 106983.
Shahid, W., Li, Y., Staples, D., Amin, G., Hakak, S.,  Ghorbani, A. (2022). Are you a cyborg, bot or human? a survey on detecting fake news spreaders. IEEE Access10, 27069-27083.
Shrestha, A., Flood, A., Sohrawardi, S., Wright, M., Al-Ameen, M. N. (2024, May). A First Look into Targeted Clickbait and its Countermeasures: The Power of Storytelling. In Proceedings of the CHI Conference on Human Factors in Computing Systems (pp. 1-23).
Tarassoli, A.R. (2024). Application of Artificial Intelligence in Detecting Fake News. Quarterly Journal of Defense Preparedness and Technology, 7(2), 82-112. [In Persian]
Tikkanen, R. (2024). Intercepted Phone Calls at the Russo-Ukrainian War: Cyberoperation or Propaganda Campaign? Master's Degree Programme in Information Technology, Cyber Security.
Ventre, D. (2020). Artificial Intelligence, Cybersecurity and Cyber Defense. y ISTE Ltd and John Wiley & Sons, Inc.
Yildirim, G. (2023). A novel hybrid multi-thread metaheuristic approach for fake news detection in social media. Applied Intelligence, 53(9), 11182-11202.
دوره 1، شماره 2 - شماره پیاپی 2
فصل تابستان
پاییز 1403
صفحه 70-92

  • تاریخ دریافت 10 آبان 1403
  • تاریخ بازنگری 02 آذر 1403
  • تاریخ پذیرش 03 آذر 1403