Machine Learning-Based Detection of Cyber Defamation in Social Networks
Keywords:Cyber defamation detection, social networks, machine learning, Naive Bayes
Cyber defamation, or the act of making false and harmful statements about individuals or organizations online, has become a prevalent issue in social networks. To safeguard the reputation and well-being of people and entities, cyber defamation must be identified and addressed. It suggests using machine learning to detect online slander in social networks. It involves collecting a dataset of social media posts and comments that have been reported or flagged as potentially defamatory. It preprocesses the textual data by removing noise, performing tokenization, and applying techniques such as stemming or lemmatization. Next, we extract relevant features from the text including linguistic patterns, sentiment and contextual information. It develops a machine learning model, such as a Support Vector Machine (SVM) or a deep learning model, such as a Recurrent Neural Network (RNN), using the preprocessed data and extracted features. The programme is trained to distinguish between defamatory and non-defamatory social media posts and comments. The results of the studies show how well our method works for spotting cyberbullying in social media. It delivers a high level of accuracy in recognising defamatory content by utilising machine learning techniques, enabling quick intervention and mitigation of the harm caused by such content. The findings have significant implications for online reputation management, social media platforms, and individuals or organizations targeted by cyber defamation. Detecting and addressing defamatory content in a timely manner can protect individuals' reputations, maintain a positive online environment, and contribute to the well-being of users in social networks. Moving forward, further research can focus on enhancing the model's performance by incorporating additional contextual features, exploring ensemble methods, or considering multilingual and cross-platform settings. By continuously improving cyber defamation detection systems, it can foster safer and more respectful online communities.
Shane Murnion, William J Buchanan, Adrian Smales, and Gordon Russell. Machine learning and semantic analysis of in-game chat for cyberbullying. Computers & Security, 76:197–213, 2018.
Sani Muhamad Isa, Livia Ashianti, et al. Cyberbullying classification using text mining. In Informatics and Computational Sciences (ICICoS),2017 1st International Conference on, pages 241–246. IEEE, 2017.
Karthik Dinakar, Birago Jones, Catherine Havasi, Henry Lieberman, and Rosalind Picard. Common sense reasoning for detection, prevention, and mitigation of cyberbullying. ACM Transactions on Interactive Intelligent Systems (TiiS), 2(3):18, 2012.
Michele Di Capua, Emanuel Di Nardo, and Alfredo Petrosino. Unsupervised cyberbullying detection in social networks. In Pattern Recognition (ICPR), 2016 23rd International Conference on, pages 432–437. IEEE, 2016.
Batoul Haidar, Maroun Chamoun, and Ahmed Serhrouchni. A multilingual system for cyberbullying detection: Arabic content detection using machine learning. Advances in Science, Technology and Engineering Systems Journal, 2(6):275–284, 2017.
B Sri Nandhini and JI Sheeba. Online social network bullying detection using intelligence techniques. Procedia Computer Science, 45:485–492,2015.
Walisa Romsaiyud, Kodchakornna Nakornphanom, Pimpaka Prasertsilp, Piyaporn Nurarak, and Pirom Konglerd. Automated cyberbullying detection using clustering appearance patterns. In Knowledge and Smart Technology (KST), 2017 9th International Conference on, pages 242–247. IEEE, 2017.
Dippelreiter, B., Grün, Chr., Pöttler, M., Seidel, I., Berger, H., Dittenbach, M. & Pesenhofer, A.(2007). Online Tourism Communities on the Path to Web 2.0 - An Evaluation, Virtual Communities in Travel and Tourism. Information Technology & Tourism, 10(4): 329-353.
Chavan, Vikas & S S, Shylaja. (2015). Machine learning approach for detection of cyber aggressive comments by peers on social media networks. 2354-2358. 10.1109/ICACCI.2015.7275970.
Nazir, A., Raza,S., Chuah, C.-N., Schipper, B.: Ghostbusting Facebook: sleuthing and characterizing phantom profiles in on-line social diversion applications. In: Proceedings of the third Conference on on-line Social Networks, WOSN 2010. USENIX Association, Berkeley, CA, USA, p. 1 (2010)
Adikari, S., Dutta, K.: distinguishing pretend profiles in Linkedin. given at the Pacific Asia Conference on info Systems PACIS 2014 Proceedings (2014)
Stringhini, G., Kruegel, C., Vigna, G.: sleuthing spammers on social networks. In: Proceedings of the twenty sixth Annual pc Security Applications Conference, ACSAC 2010, pp. 1–9 (2010)318–337. Springer, Heidelberg (2011) « Back
Neha Sharma, P. William, Kushagra Kulshreshtha, Gunjan Sharma, Bhadrappa Haralayya, Yogesh Chauhan, Anurag Shrivastava, “Human Resource Management Model with ICT Architecture: Solution of Management & Understanding of Psychology of Human Resources and Corporate Social Responsibility”, JRTDD, vol. 6, no. 9s(2), pp. 219–230, Aug. 2023.
William, P., Shrivastava, A., Chauhan, P.S., Raja, M., Ojha, S.B., Kumar, K. (2023). Natural Language Processing Implementation for Sentiment Analysis on Tweets. In: Marriwala, N., Tripathi, C., Jain, S., Kumar, D. (eds) Mobile Radio Communications and 5G Networks. Lecture Notes in Networks and Systems, vol 588. Springer, Singapore. https://doi.org/10.1007/978-981-19-7982-8_26
K. Maheswari, P. William, Gunjan Sharma, Firas Tayseer Mohammad Ayasrah, Ahmad Y. A. Bani Ahmad, Gowtham Ramkumar, Anurag Shrivastava, “Enterprise Human Resource Management Model by Artificial Intelligence to Get Befitted in Psychology of Consumers Towards Digital Technology”, JRTDD, vol. 6, no. 10s(2), pp. 209–220, Sep. 2023.
Kumar, A., More, C., Shinde, N. K., Muralidhar, N. V., Shrivastava, A., Reddy, C. V. K., & William, P. (2023). Distributed Electromagnetic Radiation Based Sree Lakshmi, P., Deepak, A., Muthuvel, S.K., Amarnatha Sarma, C Design and Analysis of Stepped Impedance Feed Elliptical PatchAntenna Smart Innovation, Systems and Technologies, 2023, 334, pp. 63
Gupta, A., Mazumdar, B.D., Mishra, M., ...Srivastava, S., Deepak, A., Role of cloud computing in management and education, Materials Today: Renewable Energy Assessment Using Novel Ensembling Approach. Journal of Nano-and Electronic Physics, 15(4)
Yang, C., Harkreader, R.C., Gu, G.: Die free or live hard? Empirical analysis and new style for fighting evolving Twitter spammers. In: Proceedings of the ordinal International Conference on Recent Advances in Intrusion Detection, RAID 2011, pp. 318–337
Springer, Heidelberg (2011) « Back 8. Automated Detection of Cyberbullying Using Machine Learning : Niraj Nirmal1, Pranil Sable2, Prathamesh Patil3, Prof. Satish Kuchiwale4 VOLUME: 07 ISSUE: 12 | DEC 2020 IRJET Mohan, Vijayarani. (2015). Preprocessing Techniques for Text Mining.
Xiang, Guang & Fan, Bin & Wang, Ling & Hong, Jason & Rose, Carolyn. (2012). Detecting offensive tweets via topical feature discovery over a large scale twitter corpus. 1980-1984. 10.1145/2396761.2398556.
Romanov, A., Semenov, A., Veijalainen, J.: Revealing pretend profiles in social networks by longitudinal information analysis. In: thirteenth International Conference on internet info Systems and Technologies, January 2017
Boshmaf, Y., Muslukhov, I., Beznosov, K., Ripeanu, M.: The socialbot network: once bots socialize for fame and cash. In: Proceedings of the twenty seventh Annual pc Security Applications Conference, pp. 93–102. ACM (2011)
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