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Dezenformasyonun Otomatik Tespiti: Sistematik Bir Haritalama Çalışması

Yıl 2024, ERKEN GÖRÜNÜM, 1 - 1
https://doi.org/10.2339/politeknik.1307037

Öz

Son yıllarda çevrimiçi sosyal medya platformlarında bilgi kirliliği türlerinden olan dezenformasyonun yayılımı hızlanmış olup birey ve toplumlar üzerinde yarattığı olumsuz etkiyi kaldırabilmek amacıyla dezenformasyonun erken tespiti önem kazanmıştır. Bu doğrultuda son yıllarda dezenformasyonun otomatik tespitine odaklanan çalışmaların sayısında ve geliştirilen yaklaşımların çeşitliliğinde artış gözlemlenmiş, gerçekleştirilen çalışmalardaki eğilimlerin detaylı bir şekilde incelenmesi ihtiyacı ortaya çıkmıştır. Bu çalışma, dezenformasyonun otomatik olarak tespitine yönelik araştırma alanının bir haritasını ortaya koymayı amaçlamaktadır. Bu doğrultuda araştırma kapsamına alınan Scopus ve Web of Science elektronik veri tabanlarında 2018-2022 yılları arasında yayınlanmış 61 birincil kaynak incelenmiş ve belirlenen kriterler çerçevesinde analiz edilmiştir. Yürütülen sistematik haritalama çalışması yayın yılı, dergi, dergi sınıfı ve yayıncı adı, yazarların menşe ülkesi, en üretken yazarlar ve kurumlar, kullanılan anahtar kelimeler, desteklenen yaklaşımlar, elde edilen doğruluk oranları ve kullanılan veri kümeleri dahil olmak üzere dezenformasyonun otomatik tespiti hakkında yararlı bilgiler sağlamayı amaçlamaktadır. Bu araştırmanın, dezenformasyonun tespiti için geliştirilen yaklaşımlar konusunda araştırmacılara yol göstermesi/yönlendirmesi ve bundan sonraki çalışmalara katkı sağlaması beklenmektedir.

Kaynakça

  • [1] Daouadi, K. E., Rebaï, R. Z. & Amous, I., “Real-Time Bot Detection from Twitter Using the Twitterbot+ Framework”, Journal of Universal Computer Science, 26(4):496-507, (2020).
  • [2] Krause, H.‑V., Baum, K., Baumann, A. & Krasnova, H., “Unifying the detrimental and beneficial effects of social network site use on self-esteem: A systematic literature review”, Media Psychology, 24(1): 10–4, (2021).
  • [3] Whiting, A. & Williams, D., “Why people use social media: a uses and gratifications approach”, Qualitative Market Research, 16(4): 362–369, (2013).
  • [4] Allcott, H. & Gentzkow, M., “Social media and fake news in the 2016 election”, Journal of Economic Perspectives, 31(2): 211–236, (2017).
  • [5] Michael, R. B. & Breaux, B. O., “The relationship between political affiliation and beliefs about sources of ‘fake news’”, Cognitive Research: Principles and Implications, 6(6): 1-15, (2021).
  • [6] De Maeyer, D., “Internet's information highway potential”, Internet Research, (1997).
  • [7] Shu, K., Wang, S. & Liu, H., “Understanding user profiles on social media for fake news”, Det. 2018 IEEE Conference on Multimedia Information Processing and Retrieval, (2018).
  • [8] Karakaş, O., & Doğru, Y. B., “Analysis Of Produced New Media Contents For Covid-19 Vaccines In The Context Of The Post-Truth Concept”, Asya Studies-Academic Social Studies, 5(16): 163-182, (2021).
  • [9] Tandoc, E.C., Lim, Z.W. & Ling, R., “Defining “fake news” a typology of scholarly definitions”, Digital Journalism, (2017).
  • [10] Wardle, C. & Derakhshan, H., “Information Disorder: Toward an interdisciplinary framework for research and policymaking”, Strasbourg: Council of Europe, (2017).
  • [11] TDK., “Manipülasyon”, Türk Dil Kurumu Büyük Türkçe Sözlük. http://www.tdk.org.tr/index.php?option=com_bts&arama=kelime&guid=TDK.GTS.5c2f51b8ede4c1.63593221. (2018).
  • [12] Karlova, N. A. & Fisher, K. E., “Plz RT: A Social Diffusion Model of 75 Misinformation and Disinformation for Understanding Human Information Behaviour”, Information Research, 18, 1-17, (2013).
  • [13] İnceoğlu, Y. & Akıner, N., “Continuity In Disinformation: Some Examples From The War On Iraq”, 2nd International Symposium of Communication in The New Millennium, Istanbul, (2004).
  • [14] Swire-Thompson B. & Lazer D., “Public health and online misinformation: challenges and recommendations”, Annu Rev Public Health, 41: 433–51, (2020).
  • [15] Taylor, A., “Before ‘fake news’ there was soviet ‘disinformation’”, The Washington Post, https://www.washingtonpost.com/news/worldviews/wp/2016/11/26/before-fake-news-there-was-soviet-disinformation/ (2016).
  • [16] Manning, M. J., Manning, M. & Romerstein, H., “Historical dictionary of American propaganda”, West Port, CT: Greenwood Publishing Group, (2004).
  • [17] Zimdars, M. & McLeod, K. (Eds.), “Fake News: Understanding Media and Misinformation in the Digital Age”, MIT Press, (2020).
  • [18] Sari, R. F., Ilmananda, A. S. & Romano, D. M., “Social Trust-based Blockchain-enabled Social Media News Verification System”, Journal of Universal Computer Science, 27(9): 979-998, (2021).
  • [19] Shu, K. & Liu, H., “Detecting fake news on social media”, Synthesis Lectures on Data Mining and Knowledge Discovery, 11(3): 1–129, (2019).
  • [20] Ünver, H. A., “Türkıye’de doğruluk kontrolü ve doğrulama kuruluşları”, Siber Politikalar Dijital Demokrasi. https://edam.org.tr/wpcontent/uploads/2020/06/TürkiyedeDoğruluk-Kontrolü-ve-Doğrulama-Kuruluşları-Akın Ünver.pdf (2020).
  • [21] Ciampaglia G. L., Shiralkar P., Rocha L. M., Bollen J., Menczer F. & Flammini A., “Computational fact checking from knowledge networks”, PloS One, 10(6), (2015).
  • [22] Lahby, M., Aqil, S., Yafooz, W.M.S. & Abakarim, Y., “Online Fake News Detection Using Machine Learning Techniques: A Systematic Mapping Study”. In: Lahby, M., Pathan, AS.K., Maleh, Y., Yafooz, W.M.S. (eds) Combating Fake News with Computational Intelligence Techniques. Studies in Computational Intelligence, 1001. Springer, Cham., (2022).
  • [23] Caio V., Meneses Silva, Raphael Silva Fontes & Methanias Colaço Júnior, “Intelligent Fake News Detection: A Systematic Mapping”, Journal of Applied Security Research, 16(2): 168-189, (2021).
  • [24] Choraś, M., Demestichas, K., Giełczyk, A., Herrero, Á., Ksieniewicz, P., Remoundou, K., Urda, D. & Woźniak, M., “Advanced Machine Learning techniques for fake news (online disinformation) detection: A systematic mapping study”, Applied Soft Computing, 101: 1568-4946, (2021).
  • [25] Souza, J., Gomes J.J., Marques, F., Julio, A. & Souza, J., “A systematic mapping on automatic classification of fake news in social media”, Social Network Analysis and Mining, 10, (2020).
  • [26] Morgan, D. & Rasinski, T., “The power and potential of primary sources”, The Reading Teacher, 65(8): 584-594, (2012).
  • [27] Fernandez-Sotos P., Torio I., Fernandez-Caballero A., Navarro E., Gonzalez P., Dompablo M. & Rodriguez-Jimenez R., “Social cognition remediation interventions: a systematic mapping review”, PloS One, 14(6): (2019).
  • [28] Cooper ID, “What is a ‘mapping study?’”. J Med Libr Assoc, 104(1), (2016).
  • [29] Haddaway N. R., Bernes C., Jonsson B.-G. & Hedlund K., “The benefits of systematic mapping to evidence based environmental management”, Ambio, 45(5): 613-620, (2016).
  • [30] Petersen, K., Feldt, R., Mujtaba, S. & Mattsson, M., “Systematic mapping studies in software engineering”, 12th International Conference on Evaluation and Assessment in Software Engineering, 17, (2008).
  • [31] Neiva F. W., David J. M. N., Braga R. & Campos F., “Towards pragmatic interoperability to support collaboration: a systematic review and mapping of the literature”, Inf. Softw. Technol, 72: 137–150, (2016).
  • [32] Petticrew, M. & Roberts, H., “Systematic Reviews in the Social Sciences: A Practical Guide”, (2006).
  • [33] Costa C. & Murta L., “Version control in distributed software development: a systematic mapping study”, In: 8th international conference on global software engineering (ICGSE), IEEE, 90–99, (2013).
  • [34] Ramos-Rodríguez, A. R. & Ruíz-Navarro, J., “Changes in the Intellectual Structure of Strategic Management Research: A Bibliometric Study of the Strategic Management Journal”, Strategic Management Journal, 25(10): 981–1004, (2004).
  • [35] Collins, “Collins 2017 word of the year shortlist”,(2017). https://www.collinsdictionary.com/ word-lovers-blog/new/collins-2017-word-of-the-year-shortlist,396,HCB.html
  • [36] Twitter, “Twitter muda regras para combater fake news e manipulac¸~ao polıtica”, (2018). https://help.twitter.com/pt/rules-and-policies/twitter-report-violation
  • [37] Rochlin, N., “Fake news: Belief in post-truth”, Library Hi Tech, 35(3): 386–392, (2017).
  • [38] Zubiaga, A., Aker, A., Bontcheva, K., Liakata, M., Procter, R., “Detection and resolution of rumours in social media: A survey”, ACM Computing Surveys (CSUR), 51: 1–36, (2018).
  • [39] Tandoc, E. C., Jr, Lim, Z. W., & Ling, R., “Defining ‘fake news’ a typology of scholarly definitions”, Digital Journalism, 6(2): 137–153, (2018).
  • [40] Silva, A., Han, Y., Luo, L., Karunasekera, S., Leckie, C., “Propagation2Vec: embedding partial propagation networks for explainable fake news early detection”, Inf Process Manag., (2021).
  • [41] Zubiaga, A., Ji, H., “Tweet, but verify: epistemic study of information verifcation on twitter”, Soc. Netw. Anal. Min., 4(1), (2014).
  • [42] Korkmaz Ş., Alkan M., “Derin öğrenme algoritmalarını kullanarak deepfake video tespiti”, Journal of Polytechnic, 26(2): 855-862, (2023).
  • [43] Yavanoğlu U., Sağıroğlu Ş., Çolak, İ., “Sosyal Ağlarda Bilgi Güvenliği Tehditleri ve Alınması Gereken Önlemler”, Journal of Polytechnic, 15(1) : 15-27, (2012).
  • [44] Darıcı M. B., “Performance analysis of combination of cnn-based models with adaboost algorithm to diagnose covid-19 disease”, Journal of Polytechnic, 26(1): 179-190, (2023).
  • [45] World Health Organizaton (WHO), “Coronavirus disease (COVID-19)”, https://www.who.int/emergencies/diseases/novel-coronavirus-2019/question-and-answers-hub/q-a-detail/coronavirus-disease-covid- 19 (2021, May, 13).
  • [46] Dixon, S., “Social media use during COVID-19 worldwide - statistics & facts.” Statista. https://www.statista.com/topics/7863/social-media-use-during coronavirus-covid-19-worldwide/#topicHeader__wrapper. (2022, February 8).
  • [47] Melchior, C. & Oliveira, M., “Health-related fake news on social media platforms: A systematic literature review”, New Media and Society, (2021).
  • [48] Sicilia, R., Lo Giudice, S., Pei, Y., Pechenizkiy, M. & Soda, P., “Twitter rumour detection in the health domain”, Expert Systems with Applications, 110: 33-40, (2018).
  • [49] Xiaoning, G., De Zhern, T., King, S. W., Fei, T. Y. & Shuan, L. H., “News reliability evaluation using latent semantic analysis”, Telkomnika (Telecommunication Computing Electronics and Control), 16(4): 1704-1711, (2018).
  • [50] Moin, R., Zahoor-ur-Rehman, Mahmood, K., Alzahrani, M. E. & Saleem, M. Q., “Framework for rumors detection in social media”, International Journal of Advanced Computer Science and Applications, 9(5): 439-444, (2018).
  • [51] Ozbay, F. A. & Alatas, B., “A novel approach for detection of fake news on social media using metaheuristic optimization algorithms”, Elektronika Ir Elektrotechnika, 25(4): 62-67, (2019).
  • [52] Gravanis, G., Vakali, A., Diamantaras, K., & Karadais, P., “Behind the cues: A benchmarking study for fake news detection”, Expert Systems with Applications, 128: 201-213, (2019).
  • [53] Lee, D., Kim, Y., Kim, H., Park, S. & Yang, Y., “Fake news detection using deep learning”, Journal of Information Processing Systems, 15(5): 1119-1130, (2019).
  • [54] Jadhav, S. S., & Thepade, S. D., “Fake news identification and classification using DSSM and improved recurrent neural network classifier”, Applied Artificial Intelligence, 33(12): 1058-1068, (2019).
  • [55] Shu, K., Mahudeswaran, D. & Liu, H., “FakeNewsTracker: A tool for fake news collection, detection, and visualization”, Computational and Mathematical Organization Theory, 25(1): 60-71, (2019).
  • [56] Wang, Z., Guo, Y., Wang, J., Li, Z. & Tang, M., “Rumor events detection from chinese microblogs via sentiments enhancement”, IEEE Access, 7: 103000- 103018, (2019).
  • [57] Son, L. H., Kumar, A., Sangwan, S. R., Arora, A., Nayyar, A. & Abdel-Basset, M., “Sarcasm detection using soft attention-based bidirectional long short-term memory model with convolution network”, IEEE Access, 7: 23319-23328, (2019).
  • [58] Fang, Y., Gao, J., Huang, C., Peng, H., & Wu, R., “Self multi-head attention-based convolutional neural networks for fake news detection”, PLoS ONE, 14(9), (2019).
  • [59] Umer, M., Imtiaz, Z., Ullah, S., Mehmood, A., Choi, G. S. & On, B., “Fake news stance detection using deep learning architecture (CNN-LSTM)”, IEEE Access, 8: 156695-156706, (2020).
  • [60] Huang, Y. & Chen, P., “Fake news detection using an ensemble learning model based on self-adaptive harmony search algorithms”, Expert Systems with Applications, 159, (2020).
  • [61] Kumar, G. V. D., Jadhav, M. V., Tadisetti, A. & Kiran, K., “A deep model on hoax detection using feed forward neural network and LSTM”, Webology, 17(2): 652-662, (2020).
  • [62] Chen, X., Ke, L., Lu, Z., Su, H., & Wang, H., “A novel hybrid model for cantonese rumor detection on twitter”, Applied Sciences (Switzerland), 10(20): 1-12, (2020).
  • [63] Guo, M., Xu, Z., Liu, L., Guo, M., Zhang, Y., & Kotsiantis, S. B., “An adaptive deep transfer learning model for rumor detection without sufficient identified rumors”, Mathematical Problems in Engineering, (2020).
  • [64] Albahr, A., & Albahar, M., “An empirical comparison of fake news detection using different machine learning algorithms”, International Journal of Advanced Computer Science and Applications, 11(9): 146-152, (2020).
  • [65] Mertoğlu, U. & Genç, B., “Automated fake news detection in the age of digital libraries”, Information Technology and Libraries, 39(4), (2020).
  • [66] Kaur, S., Kumar, P., & Kumaraguru, P., “Automating fake news detection system using multi-level voting model”, Soft Computing, 24(12): 9049-9069(2020).
  • [67] Saeed, F., Al-Sarem, M., Hezzam, E. A. & Yafooz, W. M. S., “Detecting health-related rumors on twitter using machine learning methods”, International Journal of Advanced Computer Science and Applications, 11(8): 324-332, (2020).
  • [68] Alsaeedi, A., & Al-Sarem, M., “Detecting rumors on social media based on a CNN deep learning technique”, Arabian Journal for Science and Engineering, 45(12): 10813-10844, (2020).
  • [69] Agarwal, A., Mittal, M., Pathak, A., & Goyal, L. M., “Fake news detection using a blend of neural networks: An application of deep learning”, SN Computer Science, 1(3), (2020).
  • [70] Kaliyar, R. K., Goswami, A., Narang, P., & Sinha, S., “FNDNet–A deep convolutional neural network for fake news detection”, Cognitive Systems Research, 61: 32-44, (2020).
  • [71] Abonizio, H. Q., de Morais, J. I., Tavares, G. M., & Junior, S. B., “Language-independent fake news detection: English, Portuguese, and Spanish mutual features”, Future Internet, 12(5), (2020).
  • [72] Albahar, M., “A hybrid model for fake news detection: Leveraging news content and user comments in fake news”, IET Information Security, 15(2): 169-177, (2021).
  • [73] Shim, J., Lee, Y. & Ahn, H., “A link2vec-based fake news detection model using web search results”, Expert Systems with Applications, 184, (2021).
  • [74] Song, C., Ning, N., Zhang, Y. & Wu, B., “A multimodal fake news detection model based on crossmodal attention residual and multichannel convolutional neural networks”, Information Processing and Management, 58(1), (2021).
  • [75] Chen, X., Wang, C., Li, D., & Sun, X., “A new early rumor detection model based on BiGRU neural network”, Discrete Dynamics in Nature and Society, (2021).
  • [76] Sandrilla, R. & Devi, M. S., “A Robust Technique Of Fake News Identification Using Ensemble Feature Selection”, Indian Journal of Computer Science and Engineering,12(6): 1886-1898, (2021).
  • [77] Qasem, S. N., Al-Sarem, M. & Saeed, F., “An ensemble learning based approach for detecting and tracking COVID19 rumors”, Computers, Materials and Continua, 70(1): 1721-1747, (2021).
  • [78] Abdelminaam, D. S., Ismail, F. H., Taha, M., Taha, A., Houssein, E. H., & Nabil, A., “CoAID-DEEP: An optimized intelligent framework for automated detecting COVID-19 misleading information on twitter”, IEEE Access, 9: 27840-27867, (2021).
  • [79] Goldani, M. H., Safabakhsh, R., & Momtazi, S., “Convolutional neural network with margin loss for fake news detection”, Information Processing and Management, 58(1), (2021).
  • [80] Kaliyar, R. K., Goswami, A., & Narang, P., “DeepFakE: Improving fake news detection using tensor decomposition-based deep neural network”, Journal of Supercomputing, 77(2): 1015-1037. (2021).
  • [81] Divya, T. V., & Banik, B. G., “Detecting fake news over job posts via bi-directional long short-term memory (BIDLSTM)”, International Journal of Web-Based Learning and Teaching Technologies, 16(6): 1-18. (2021).
  • [82] Sastrawan, I. K., Bayupati, I. P. A. & Arsa, D. M. S., “Detection of fake news using deep learning CNN–RNN based methods”, ICT Express, (2021).
  • [83] Asghar, M. Z., Habib, A., Habib, A., Khan, A., Ali, R., & Khattak, A., “Exploring deep neural networks for rumor detection”, Journal of Ambient Intelligence and Humanized Computing, 12(4): 4315-4333, (2021).
  • [84] Aslam, N., Ullah Khan, I., Alotaibi, F. S., Aldaej, L. A., & Aldubaikil, A. K., “Fake detect: A deep learning ensemble model for fake news detection”, Complexity, (2021).
  • [85] Zeng, J., Zhang, Y. & Ma, X., “Fake news detection for epidemic emergencies via deep correlations between text and images”, Sustainable Cities and Society, (66), (2021).
  • [86] Ying, L., Yu, H., Wang, J., Ji, Y. & Qian, S., “Fake news detection via multi-modal topic memory network”, IEEE Access, 9:132818-132829, (2021).
  • [87] Kaliyar, R. K., Goswami, A., & Narang, P., “FakeBERT: Fake news detection in social media with a BERT-based deep learning approach”. Multimedia Tools and Applications, 80(8): 11765-11788, (2021).
  • [88] Meel, P. & Vishwakarma, D. K., “HAN, image captioning, and forensics ensemble multimodal fake news detection”, Information Sciences, 567: 23-41, (2021).
  • [89] Khanday, A. M. U. D., Khan, Q. R., & Rabani, S. T., “Identifying propaganda from online social networks during COVID-19 using machine learning techniques”, International Journal of Information Technology (Singapore), 13(1): 115-122, (2021).
  • [90] Choudhary, A., & Arora, A., “Linguistic feature based learning model for fake news detection and classification”, Expert Systems with Applications, 169, (2021).
  • [91] Chauhan, T., & Palivela, H., “Optimization and improvement of fake news detection using deep learning approaches for societal benefit”, International Journal of Information Management Data Insights, 1(2), (2021).
  • [92] Tu, K., Chen, C., Hou, C., Yuan, J., Li, J. & Yuan, X., “Rumor2vec: A rumor detection framework with joint text and propagation structure representation learning”, Information Sciences, 560: 137-151, (2021).
  • [93] Bhattacharya, P., Patel, S. B., Gupta, R., Tanwar, S., & Rodrigues, J. J. P. C., “SaTYa: Trusted bi-LSTM-based fake news classification scheme for smart community”, IEEE Transactions on Computational Social Systems, (2021).
  • [94] Islam, N., Shaikh, A., Qaiser, A., Asiri, Y., Almakdi, S., Sulaiman, A., Moazzam, V. & Babar, S. A., “Ternion: An autonomous model for fake news detection”, Applied Sciences(Switzerland),11(19), (2021).
  • [95] Senhadji, S. & Ahmed, R. A. S., “Fake news detection using naïve bayes and long short term memory algorithms”, IAES International Journal of Artificial Intelligence, 11(2): 746-752, (2022).
  • [96] Gonwirat, S., Choompol, A., & Wichapa, N., “A combined deep learning model based on the ideal distance weighting method for fake news detection”, International Journal of Data and Network Science, 6(2): 347-354, (2022).
  • [97] Palani, B., Elango, S. & Vignesh Viswanathan, K., “CB-fake: A multimodal deep learning framework for automatic fake news detection using capsule neural network and BERT”, Multimedia Tools and Applications, 81(4): 5587-5620, (2022).
  • [98] Alsaidi, H., & Etaiwi, W., “Empirical evaluation of machine learning classification algorithms for detecting COVID-19 fake news”, International Journal of Advances in Soft Computing and its Applications, 14(1): 49-59, (2022).
  • [99] Dixit, D. K., Bhagat, A., & Dangi, D., “Fake news classification using a fuzzy convolutional recurrent neural network”, Computers, Materials and Continua, 71(2): 5733-5750, (2022).
  • [100] Wang, J., Mao, H. & Li, H., “FMFN: Fine-grained multimodal fusion networks for fake news detection”, Applied Sciences (Switzerland), 12(3): (2022).
  • [101] Sandrilla, R. & Devi, M. S., “FNU-BiCNN: Fake news and fake URL detection using bi-CNN”, International Journal of Advanced Computer Science and Applications, 13(2): 477-488, (2022).
  • [102] Almars, A. M., Almaliki, M., Noor, T. H., Alwateer, M. M., & Atlam, E., “HANN: Hybrid attention neural network for detecting covid-19 related rumors”, IEEE Access, 10:12334-12344, (2022).
  • [103] Yu, D., Zhou, Y., Zhang, S. & Liu, C., “Heterogeneous graph convolutional network-based dynamic rumor detection on social media”, Complexity, (2022).
  • [104] Tembhurne, J. V., Moin Almin, M. & Diwan, T., “Mc-DNN: Fake news detection using MultiChannel deep neural networks”, International Journal on Semantic Web and Information Systems, 18(1), (2022).
  • [105] Zhang, H., Qian, S., Fang, Q. & Xu, C., “Multi-modal meta multi-task learning for social media rumor detection”, IEEE Transactions on Multimedia, 24: 1449-1459, (2022).
  • [106] He, X., Tuerhong, G., Wushouer, M., & Xin, D., “Rumors detection based on lifelong machine learning”, IEEE Access, 10: 25605-25620, (2022).
  • [107] Hirlekar, V. V. & Kumar, A., “Tweet credibility detection for COVID-19 tweets using text and user content features”, International Journal of Advanced Computer Science and Applications,13(4): 430-439, (2022).
  • [108] Alotaibi, F. L., & Alhammad, M. M., “Using a rule-based model to detect arabic fake news propagation during covid-19”, International Journal of Advanced Computer Science and Applications, 13(1): 112-119, (2022).

Automatic Detection of Disinformation: A Systematic Mapping Study

Yıl 2024, ERKEN GÖRÜNÜM, 1 - 1
https://doi.org/10.2339/politeknik.1307037

Öz

In recent years, the spread of disinformation, which is one of the kind of information pollution, has accelerated on online social media platforms, and detecting disinformation early has become significant to be able to remove the negative impact it has on individuals and societies. In this direction, increased number of studies focusing on the automatic detection of disinformation and the variety of approaches developed have been observed in recent years, and the need to study the trends in the studies carried out in detail has emerged. This research seeks to present a map of the research area for the automatic detection of disinformation. In this context, 61 primary sources published in the electronic databases named Web of Science and Scopus between 2018-2022 included in the research scope have been examined and analyzed within the framework of the determined criteria. The conducted systematic mapping study aims to provide useful insights about automatic detection of disinformation including publication year, journal, journal class and publisher name, country of origin of the authors, most prolific authors and institutions, keywords used, supported approaches, obtained accuracy rates and datasets used. It is expected that this research will guide/direct researchers about the approaches developed for the detection of disinformation and contribute to future studies.

Kaynakça

  • [1] Daouadi, K. E., Rebaï, R. Z. & Amous, I., “Real-Time Bot Detection from Twitter Using the Twitterbot+ Framework”, Journal of Universal Computer Science, 26(4):496-507, (2020).
  • [2] Krause, H.‑V., Baum, K., Baumann, A. & Krasnova, H., “Unifying the detrimental and beneficial effects of social network site use on self-esteem: A systematic literature review”, Media Psychology, 24(1): 10–4, (2021).
  • [3] Whiting, A. & Williams, D., “Why people use social media: a uses and gratifications approach”, Qualitative Market Research, 16(4): 362–369, (2013).
  • [4] Allcott, H. & Gentzkow, M., “Social media and fake news in the 2016 election”, Journal of Economic Perspectives, 31(2): 211–236, (2017).
  • [5] Michael, R. B. & Breaux, B. O., “The relationship between political affiliation and beliefs about sources of ‘fake news’”, Cognitive Research: Principles and Implications, 6(6): 1-15, (2021).
  • [6] De Maeyer, D., “Internet's information highway potential”, Internet Research, (1997).
  • [7] Shu, K., Wang, S. & Liu, H., “Understanding user profiles on social media for fake news”, Det. 2018 IEEE Conference on Multimedia Information Processing and Retrieval, (2018).
  • [8] Karakaş, O., & Doğru, Y. B., “Analysis Of Produced New Media Contents For Covid-19 Vaccines In The Context Of The Post-Truth Concept”, Asya Studies-Academic Social Studies, 5(16): 163-182, (2021).
  • [9] Tandoc, E.C., Lim, Z.W. & Ling, R., “Defining “fake news” a typology of scholarly definitions”, Digital Journalism, (2017).
  • [10] Wardle, C. & Derakhshan, H., “Information Disorder: Toward an interdisciplinary framework for research and policymaking”, Strasbourg: Council of Europe, (2017).
  • [11] TDK., “Manipülasyon”, Türk Dil Kurumu Büyük Türkçe Sözlük. http://www.tdk.org.tr/index.php?option=com_bts&arama=kelime&guid=TDK.GTS.5c2f51b8ede4c1.63593221. (2018).
  • [12] Karlova, N. A. & Fisher, K. E., “Plz RT: A Social Diffusion Model of 75 Misinformation and Disinformation for Understanding Human Information Behaviour”, Information Research, 18, 1-17, (2013).
  • [13] İnceoğlu, Y. & Akıner, N., “Continuity In Disinformation: Some Examples From The War On Iraq”, 2nd International Symposium of Communication in The New Millennium, Istanbul, (2004).
  • [14] Swire-Thompson B. & Lazer D., “Public health and online misinformation: challenges and recommendations”, Annu Rev Public Health, 41: 433–51, (2020).
  • [15] Taylor, A., “Before ‘fake news’ there was soviet ‘disinformation’”, The Washington Post, https://www.washingtonpost.com/news/worldviews/wp/2016/11/26/before-fake-news-there-was-soviet-disinformation/ (2016).
  • [16] Manning, M. J., Manning, M. & Romerstein, H., “Historical dictionary of American propaganda”, West Port, CT: Greenwood Publishing Group, (2004).
  • [17] Zimdars, M. & McLeod, K. (Eds.), “Fake News: Understanding Media and Misinformation in the Digital Age”, MIT Press, (2020).
  • [18] Sari, R. F., Ilmananda, A. S. & Romano, D. M., “Social Trust-based Blockchain-enabled Social Media News Verification System”, Journal of Universal Computer Science, 27(9): 979-998, (2021).
  • [19] Shu, K. & Liu, H., “Detecting fake news on social media”, Synthesis Lectures on Data Mining and Knowledge Discovery, 11(3): 1–129, (2019).
  • [20] Ünver, H. A., “Türkıye’de doğruluk kontrolü ve doğrulama kuruluşları”, Siber Politikalar Dijital Demokrasi. https://edam.org.tr/wpcontent/uploads/2020/06/TürkiyedeDoğruluk-Kontrolü-ve-Doğrulama-Kuruluşları-Akın Ünver.pdf (2020).
  • [21] Ciampaglia G. L., Shiralkar P., Rocha L. M., Bollen J., Menczer F. & Flammini A., “Computational fact checking from knowledge networks”, PloS One, 10(6), (2015).
  • [22] Lahby, M., Aqil, S., Yafooz, W.M.S. & Abakarim, Y., “Online Fake News Detection Using Machine Learning Techniques: A Systematic Mapping Study”. In: Lahby, M., Pathan, AS.K., Maleh, Y., Yafooz, W.M.S. (eds) Combating Fake News with Computational Intelligence Techniques. Studies in Computational Intelligence, 1001. Springer, Cham., (2022).
  • [23] Caio V., Meneses Silva, Raphael Silva Fontes & Methanias Colaço Júnior, “Intelligent Fake News Detection: A Systematic Mapping”, Journal of Applied Security Research, 16(2): 168-189, (2021).
  • [24] Choraś, M., Demestichas, K., Giełczyk, A., Herrero, Á., Ksieniewicz, P., Remoundou, K., Urda, D. & Woźniak, M., “Advanced Machine Learning techniques for fake news (online disinformation) detection: A systematic mapping study”, Applied Soft Computing, 101: 1568-4946, (2021).
  • [25] Souza, J., Gomes J.J., Marques, F., Julio, A. & Souza, J., “A systematic mapping on automatic classification of fake news in social media”, Social Network Analysis and Mining, 10, (2020).
  • [26] Morgan, D. & Rasinski, T., “The power and potential of primary sources”, The Reading Teacher, 65(8): 584-594, (2012).
  • [27] Fernandez-Sotos P., Torio I., Fernandez-Caballero A., Navarro E., Gonzalez P., Dompablo M. & Rodriguez-Jimenez R., “Social cognition remediation interventions: a systematic mapping review”, PloS One, 14(6): (2019).
  • [28] Cooper ID, “What is a ‘mapping study?’”. J Med Libr Assoc, 104(1), (2016).
  • [29] Haddaway N. R., Bernes C., Jonsson B.-G. & Hedlund K., “The benefits of systematic mapping to evidence based environmental management”, Ambio, 45(5): 613-620, (2016).
  • [30] Petersen, K., Feldt, R., Mujtaba, S. & Mattsson, M., “Systematic mapping studies in software engineering”, 12th International Conference on Evaluation and Assessment in Software Engineering, 17, (2008).
  • [31] Neiva F. W., David J. M. N., Braga R. & Campos F., “Towards pragmatic interoperability to support collaboration: a systematic review and mapping of the literature”, Inf. Softw. Technol, 72: 137–150, (2016).
  • [32] Petticrew, M. & Roberts, H., “Systematic Reviews in the Social Sciences: A Practical Guide”, (2006).
  • [33] Costa C. & Murta L., “Version control in distributed software development: a systematic mapping study”, In: 8th international conference on global software engineering (ICGSE), IEEE, 90–99, (2013).
  • [34] Ramos-Rodríguez, A. R. & Ruíz-Navarro, J., “Changes in the Intellectual Structure of Strategic Management Research: A Bibliometric Study of the Strategic Management Journal”, Strategic Management Journal, 25(10): 981–1004, (2004).
  • [35] Collins, “Collins 2017 word of the year shortlist”,(2017). https://www.collinsdictionary.com/ word-lovers-blog/new/collins-2017-word-of-the-year-shortlist,396,HCB.html
  • [36] Twitter, “Twitter muda regras para combater fake news e manipulac¸~ao polıtica”, (2018). https://help.twitter.com/pt/rules-and-policies/twitter-report-violation
  • [37] Rochlin, N., “Fake news: Belief in post-truth”, Library Hi Tech, 35(3): 386–392, (2017).
  • [38] Zubiaga, A., Aker, A., Bontcheva, K., Liakata, M., Procter, R., “Detection and resolution of rumours in social media: A survey”, ACM Computing Surveys (CSUR), 51: 1–36, (2018).
  • [39] Tandoc, E. C., Jr, Lim, Z. W., & Ling, R., “Defining ‘fake news’ a typology of scholarly definitions”, Digital Journalism, 6(2): 137–153, (2018).
  • [40] Silva, A., Han, Y., Luo, L., Karunasekera, S., Leckie, C., “Propagation2Vec: embedding partial propagation networks for explainable fake news early detection”, Inf Process Manag., (2021).
  • [41] Zubiaga, A., Ji, H., “Tweet, but verify: epistemic study of information verifcation on twitter”, Soc. Netw. Anal. Min., 4(1), (2014).
  • [42] Korkmaz Ş., Alkan M., “Derin öğrenme algoritmalarını kullanarak deepfake video tespiti”, Journal of Polytechnic, 26(2): 855-862, (2023).
  • [43] Yavanoğlu U., Sağıroğlu Ş., Çolak, İ., “Sosyal Ağlarda Bilgi Güvenliği Tehditleri ve Alınması Gereken Önlemler”, Journal of Polytechnic, 15(1) : 15-27, (2012).
  • [44] Darıcı M. B., “Performance analysis of combination of cnn-based models with adaboost algorithm to diagnose covid-19 disease”, Journal of Polytechnic, 26(1): 179-190, (2023).
  • [45] World Health Organizaton (WHO), “Coronavirus disease (COVID-19)”, https://www.who.int/emergencies/diseases/novel-coronavirus-2019/question-and-answers-hub/q-a-detail/coronavirus-disease-covid- 19 (2021, May, 13).
  • [46] Dixon, S., “Social media use during COVID-19 worldwide - statistics & facts.” Statista. https://www.statista.com/topics/7863/social-media-use-during coronavirus-covid-19-worldwide/#topicHeader__wrapper. (2022, February 8).
  • [47] Melchior, C. & Oliveira, M., “Health-related fake news on social media platforms: A systematic literature review”, New Media and Society, (2021).
  • [48] Sicilia, R., Lo Giudice, S., Pei, Y., Pechenizkiy, M. & Soda, P., “Twitter rumour detection in the health domain”, Expert Systems with Applications, 110: 33-40, (2018).
  • [49] Xiaoning, G., De Zhern, T., King, S. W., Fei, T. Y. & Shuan, L. H., “News reliability evaluation using latent semantic analysis”, Telkomnika (Telecommunication Computing Electronics and Control), 16(4): 1704-1711, (2018).
  • [50] Moin, R., Zahoor-ur-Rehman, Mahmood, K., Alzahrani, M. E. & Saleem, M. Q., “Framework for rumors detection in social media”, International Journal of Advanced Computer Science and Applications, 9(5): 439-444, (2018).
  • [51] Ozbay, F. A. & Alatas, B., “A novel approach for detection of fake news on social media using metaheuristic optimization algorithms”, Elektronika Ir Elektrotechnika, 25(4): 62-67, (2019).
  • [52] Gravanis, G., Vakali, A., Diamantaras, K., & Karadais, P., “Behind the cues: A benchmarking study for fake news detection”, Expert Systems with Applications, 128: 201-213, (2019).
  • [53] Lee, D., Kim, Y., Kim, H., Park, S. & Yang, Y., “Fake news detection using deep learning”, Journal of Information Processing Systems, 15(5): 1119-1130, (2019).
  • [54] Jadhav, S. S., & Thepade, S. D., “Fake news identification and classification using DSSM and improved recurrent neural network classifier”, Applied Artificial Intelligence, 33(12): 1058-1068, (2019).
  • [55] Shu, K., Mahudeswaran, D. & Liu, H., “FakeNewsTracker: A tool for fake news collection, detection, and visualization”, Computational and Mathematical Organization Theory, 25(1): 60-71, (2019).
  • [56] Wang, Z., Guo, Y., Wang, J., Li, Z. & Tang, M., “Rumor events detection from chinese microblogs via sentiments enhancement”, IEEE Access, 7: 103000- 103018, (2019).
  • [57] Son, L. H., Kumar, A., Sangwan, S. R., Arora, A., Nayyar, A. & Abdel-Basset, M., “Sarcasm detection using soft attention-based bidirectional long short-term memory model with convolution network”, IEEE Access, 7: 23319-23328, (2019).
  • [58] Fang, Y., Gao, J., Huang, C., Peng, H., & Wu, R., “Self multi-head attention-based convolutional neural networks for fake news detection”, PLoS ONE, 14(9), (2019).
  • [59] Umer, M., Imtiaz, Z., Ullah, S., Mehmood, A., Choi, G. S. & On, B., “Fake news stance detection using deep learning architecture (CNN-LSTM)”, IEEE Access, 8: 156695-156706, (2020).
  • [60] Huang, Y. & Chen, P., “Fake news detection using an ensemble learning model based on self-adaptive harmony search algorithms”, Expert Systems with Applications, 159, (2020).
  • [61] Kumar, G. V. D., Jadhav, M. V., Tadisetti, A. & Kiran, K., “A deep model on hoax detection using feed forward neural network and LSTM”, Webology, 17(2): 652-662, (2020).
  • [62] Chen, X., Ke, L., Lu, Z., Su, H., & Wang, H., “A novel hybrid model for cantonese rumor detection on twitter”, Applied Sciences (Switzerland), 10(20): 1-12, (2020).
  • [63] Guo, M., Xu, Z., Liu, L., Guo, M., Zhang, Y., & Kotsiantis, S. B., “An adaptive deep transfer learning model for rumor detection without sufficient identified rumors”, Mathematical Problems in Engineering, (2020).
  • [64] Albahr, A., & Albahar, M., “An empirical comparison of fake news detection using different machine learning algorithms”, International Journal of Advanced Computer Science and Applications, 11(9): 146-152, (2020).
  • [65] Mertoğlu, U. & Genç, B., “Automated fake news detection in the age of digital libraries”, Information Technology and Libraries, 39(4), (2020).
  • [66] Kaur, S., Kumar, P., & Kumaraguru, P., “Automating fake news detection system using multi-level voting model”, Soft Computing, 24(12): 9049-9069(2020).
  • [67] Saeed, F., Al-Sarem, M., Hezzam, E. A. & Yafooz, W. M. S., “Detecting health-related rumors on twitter using machine learning methods”, International Journal of Advanced Computer Science and Applications, 11(8): 324-332, (2020).
  • [68] Alsaeedi, A., & Al-Sarem, M., “Detecting rumors on social media based on a CNN deep learning technique”, Arabian Journal for Science and Engineering, 45(12): 10813-10844, (2020).
  • [69] Agarwal, A., Mittal, M., Pathak, A., & Goyal, L. M., “Fake news detection using a blend of neural networks: An application of deep learning”, SN Computer Science, 1(3), (2020).
  • [70] Kaliyar, R. K., Goswami, A., Narang, P., & Sinha, S., “FNDNet–A deep convolutional neural network for fake news detection”, Cognitive Systems Research, 61: 32-44, (2020).
  • [71] Abonizio, H. Q., de Morais, J. I., Tavares, G. M., & Junior, S. B., “Language-independent fake news detection: English, Portuguese, and Spanish mutual features”, Future Internet, 12(5), (2020).
  • [72] Albahar, M., “A hybrid model for fake news detection: Leveraging news content and user comments in fake news”, IET Information Security, 15(2): 169-177, (2021).
  • [73] Shim, J., Lee, Y. & Ahn, H., “A link2vec-based fake news detection model using web search results”, Expert Systems with Applications, 184, (2021).
  • [74] Song, C., Ning, N., Zhang, Y. & Wu, B., “A multimodal fake news detection model based on crossmodal attention residual and multichannel convolutional neural networks”, Information Processing and Management, 58(1), (2021).
  • [75] Chen, X., Wang, C., Li, D., & Sun, X., “A new early rumor detection model based on BiGRU neural network”, Discrete Dynamics in Nature and Society, (2021).
  • [76] Sandrilla, R. & Devi, M. S., “A Robust Technique Of Fake News Identification Using Ensemble Feature Selection”, Indian Journal of Computer Science and Engineering,12(6): 1886-1898, (2021).
  • [77] Qasem, S. N., Al-Sarem, M. & Saeed, F., “An ensemble learning based approach for detecting and tracking COVID19 rumors”, Computers, Materials and Continua, 70(1): 1721-1747, (2021).
  • [78] Abdelminaam, D. S., Ismail, F. H., Taha, M., Taha, A., Houssein, E. H., & Nabil, A., “CoAID-DEEP: An optimized intelligent framework for automated detecting COVID-19 misleading information on twitter”, IEEE Access, 9: 27840-27867, (2021).
  • [79] Goldani, M. H., Safabakhsh, R., & Momtazi, S., “Convolutional neural network with margin loss for fake news detection”, Information Processing and Management, 58(1), (2021).
  • [80] Kaliyar, R. K., Goswami, A., & Narang, P., “DeepFakE: Improving fake news detection using tensor decomposition-based deep neural network”, Journal of Supercomputing, 77(2): 1015-1037. (2021).
  • [81] Divya, T. V., & Banik, B. G., “Detecting fake news over job posts via bi-directional long short-term memory (BIDLSTM)”, International Journal of Web-Based Learning and Teaching Technologies, 16(6): 1-18. (2021).
  • [82] Sastrawan, I. K., Bayupati, I. P. A. & Arsa, D. M. S., “Detection of fake news using deep learning CNN–RNN based methods”, ICT Express, (2021).
  • [83] Asghar, M. Z., Habib, A., Habib, A., Khan, A., Ali, R., & Khattak, A., “Exploring deep neural networks for rumor detection”, Journal of Ambient Intelligence and Humanized Computing, 12(4): 4315-4333, (2021).
  • [84] Aslam, N., Ullah Khan, I., Alotaibi, F. S., Aldaej, L. A., & Aldubaikil, A. K., “Fake detect: A deep learning ensemble model for fake news detection”, Complexity, (2021).
  • [85] Zeng, J., Zhang, Y. & Ma, X., “Fake news detection for epidemic emergencies via deep correlations between text and images”, Sustainable Cities and Society, (66), (2021).
  • [86] Ying, L., Yu, H., Wang, J., Ji, Y. & Qian, S., “Fake news detection via multi-modal topic memory network”, IEEE Access, 9:132818-132829, (2021).
  • [87] Kaliyar, R. K., Goswami, A., & Narang, P., “FakeBERT: Fake news detection in social media with a BERT-based deep learning approach”. Multimedia Tools and Applications, 80(8): 11765-11788, (2021).
  • [88] Meel, P. & Vishwakarma, D. K., “HAN, image captioning, and forensics ensemble multimodal fake news detection”, Information Sciences, 567: 23-41, (2021).
  • [89] Khanday, A. M. U. D., Khan, Q. R., & Rabani, S. T., “Identifying propaganda from online social networks during COVID-19 using machine learning techniques”, International Journal of Information Technology (Singapore), 13(1): 115-122, (2021).
  • [90] Choudhary, A., & Arora, A., “Linguistic feature based learning model for fake news detection and classification”, Expert Systems with Applications, 169, (2021).
  • [91] Chauhan, T., & Palivela, H., “Optimization and improvement of fake news detection using deep learning approaches for societal benefit”, International Journal of Information Management Data Insights, 1(2), (2021).
  • [92] Tu, K., Chen, C., Hou, C., Yuan, J., Li, J. & Yuan, X., “Rumor2vec: A rumor detection framework with joint text and propagation structure representation learning”, Information Sciences, 560: 137-151, (2021).
  • [93] Bhattacharya, P., Patel, S. B., Gupta, R., Tanwar, S., & Rodrigues, J. J. P. C., “SaTYa: Trusted bi-LSTM-based fake news classification scheme for smart community”, IEEE Transactions on Computational Social Systems, (2021).
  • [94] Islam, N., Shaikh, A., Qaiser, A., Asiri, Y., Almakdi, S., Sulaiman, A., Moazzam, V. & Babar, S. A., “Ternion: An autonomous model for fake news detection”, Applied Sciences(Switzerland),11(19), (2021).
  • [95] Senhadji, S. & Ahmed, R. A. S., “Fake news detection using naïve bayes and long short term memory algorithms”, IAES International Journal of Artificial Intelligence, 11(2): 746-752, (2022).
  • [96] Gonwirat, S., Choompol, A., & Wichapa, N., “A combined deep learning model based on the ideal distance weighting method for fake news detection”, International Journal of Data and Network Science, 6(2): 347-354, (2022).
  • [97] Palani, B., Elango, S. & Vignesh Viswanathan, K., “CB-fake: A multimodal deep learning framework for automatic fake news detection using capsule neural network and BERT”, Multimedia Tools and Applications, 81(4): 5587-5620, (2022).
  • [98] Alsaidi, H., & Etaiwi, W., “Empirical evaluation of machine learning classification algorithms for detecting COVID-19 fake news”, International Journal of Advances in Soft Computing and its Applications, 14(1): 49-59, (2022).
  • [99] Dixit, D. K., Bhagat, A., & Dangi, D., “Fake news classification using a fuzzy convolutional recurrent neural network”, Computers, Materials and Continua, 71(2): 5733-5750, (2022).
  • [100] Wang, J., Mao, H. & Li, H., “FMFN: Fine-grained multimodal fusion networks for fake news detection”, Applied Sciences (Switzerland), 12(3): (2022).
  • [101] Sandrilla, R. & Devi, M. S., “FNU-BiCNN: Fake news and fake URL detection using bi-CNN”, International Journal of Advanced Computer Science and Applications, 13(2): 477-488, (2022).
  • [102] Almars, A. M., Almaliki, M., Noor, T. H., Alwateer, M. M., & Atlam, E., “HANN: Hybrid attention neural network for detecting covid-19 related rumors”, IEEE Access, 10:12334-12344, (2022).
  • [103] Yu, D., Zhou, Y., Zhang, S. & Liu, C., “Heterogeneous graph convolutional network-based dynamic rumor detection on social media”, Complexity, (2022).
  • [104] Tembhurne, J. V., Moin Almin, M. & Diwan, T., “Mc-DNN: Fake news detection using MultiChannel deep neural networks”, International Journal on Semantic Web and Information Systems, 18(1), (2022).
  • [105] Zhang, H., Qian, S., Fang, Q. & Xu, C., “Multi-modal meta multi-task learning for social media rumor detection”, IEEE Transactions on Multimedia, 24: 1449-1459, (2022).
  • [106] He, X., Tuerhong, G., Wushouer, M., & Xin, D., “Rumors detection based on lifelong machine learning”, IEEE Access, 10: 25605-25620, (2022).
  • [107] Hirlekar, V. V. & Kumar, A., “Tweet credibility detection for COVID-19 tweets using text and user content features”, International Journal of Advanced Computer Science and Applications,13(4): 430-439, (2022).
  • [108] Alotaibi, F. L., & Alhammad, M. M., “Using a rule-based model to detect arabic fake news propagation during covid-19”, International Journal of Advanced Computer Science and Applications, 13(1): 112-119, (2022).
Toplam 108 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Derleme Makalesi
Yazarlar

Merve Ertürk 0000-0001-6440-8724

Tuana İrkey 0000-0002-0169-5460

Başak Gök 0000-0002-8687-5961

Hadi Gökçen 0000-0002-5163-0008

Erken Görünüm Tarihi 16 Şubat 2024
Yayımlanma Tarihi
Gönderilme Tarihi 30 Mayıs 2023
Yayımlandığı Sayı Yıl 2024 ERKEN GÖRÜNÜM

Kaynak Göster

APA Ertürk, M., İrkey, T., Gök, B., Gökçen, H. (2024). Automatic Detection of Disinformation: A Systematic Mapping Study. Politeknik Dergisi1-1. https://doi.org/10.2339/politeknik.1307037
AMA Ertürk M, İrkey T, Gök B, Gökçen H. Automatic Detection of Disinformation: A Systematic Mapping Study. Politeknik Dergisi. Published online 01 Şubat 2024:1-1. doi:10.2339/politeknik.1307037
Chicago Ertürk, Merve, Tuana İrkey, Başak Gök, ve Hadi Gökçen. “Automatic Detection of Disinformation: A Systematic Mapping Study”. Politeknik Dergisi, Şubat (Şubat 2024), 1-1. https://doi.org/10.2339/politeknik.1307037.
EndNote Ertürk M, İrkey T, Gök B, Gökçen H (01 Şubat 2024) Automatic Detection of Disinformation: A Systematic Mapping Study. Politeknik Dergisi 1–1.
IEEE M. Ertürk, T. İrkey, B. Gök, ve H. Gökçen, “Automatic Detection of Disinformation: A Systematic Mapping Study”, Politeknik Dergisi, ss. 1–1, Şubat 2024, doi: 10.2339/politeknik.1307037.
ISNAD Ertürk, Merve vd. “Automatic Detection of Disinformation: A Systematic Mapping Study”. Politeknik Dergisi. Şubat 2024. 1-1. https://doi.org/10.2339/politeknik.1307037.
JAMA Ertürk M, İrkey T, Gök B, Gökçen H. Automatic Detection of Disinformation: A Systematic Mapping Study. Politeknik Dergisi. 2024;:1–1.
MLA Ertürk, Merve vd. “Automatic Detection of Disinformation: A Systematic Mapping Study”. Politeknik Dergisi, 2024, ss. 1-1, doi:10.2339/politeknik.1307037.
Vancouver Ertürk M, İrkey T, Gök B, Gökçen H. Automatic Detection of Disinformation: A Systematic Mapping Study. Politeknik Dergisi. 2024:1-.
 
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