Balancing Progress and Challenges: Enhancing Trust and Security in Social Networks
DOI:
https://doi.org/10.63682/jns.v14i15S.4056Keywords:
Fake User Detection, Spam Account Identification, Social Network Security, Behavioral Analysis, Machine Learning in Social MediaAbstract
The utilization of social networks as a means of communication is becoming an increasingly significant practice in this day and age, when digital technology is such an integral part of the everyday life of people all over the world. However, the prevalence of spammers and fake user accounts poses significant threats to the integrity of these platforms, as well as to the safety and trust of the users of these platforms. These threats are a direct result of the fact that spammers are easily accessible. When it comes to these dangers, which are a direct result of themselves, the prevalence of spammers is directly responsible for contributing to them. The existence of spammers in every location that has access to the internet is the root cause of these issues, which can be traced back to their presence. Through the application of a variety of strategies, we will investigate advanced methods that can be utilized to identify and eliminate spammers and fake users. These methods can be utilized to eliminate spammers and fake users. In order to accomplish the purpose of this paper, an investigation into the nature of these methods will be carried out. The investigation into these methods makes use of a wide range of social networks in order to collect information about them. The implementation of a comprehensive framework that incorporates the methods of behaviour analysis, content examination, and account attributes is something that we propose as a potential solution to the problem. This framework would be implemented in order to address the issue. The application of a wide range of academic disciplines, such as natural language processing (NLP), network analysis, and machine learning, amongst others, became feasible in order to facilitate the development of this framework. The work that was done also includes a discussion of the difficulties, ethical considerations, and potential future directions that could be taken in the fight against this issue. This discussion is included in the work that can be found here. This discussion is included as a component of the findings of the study, which are included in the findings.
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Aggarwal, C. C. (2011). Social Network Data Analytics. Springer.
Benevenuto, F., et al. (2010). Detecting spammers on social networks. Proceedings of the Annual Computer Security Applications Conference.
Bhattacharyya, S., et al. (2012). Data mining for social network analysis. Springer Briefs in Computer Science.
Chen, T., et al. (2020). Graph Neural Networks: A Comprehensive Review. IEEE Transactions on Neural Networks and Learning Systems.
Ghosh, S., & Lerman, K. (2010). Predicting influential users in online social networks. arXiv preprint arXiv:1005.4882.
Girvan, M., & Newman, M. E. J. (2002). Community structure in social and biological networks. PNAS.
Goodfellow, I., et al. (2016). Deep Learning. MIT Press.
He, K., et al. (2016). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
Hinton, G. E., et al. (2012). Deep neural networks for acoustic modeling in speech recognition. IEEE Signal Processing Magazine.
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation.
Kim, Y. (2014). Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP).
Kingma, D. P., & Ba, J. (2015). Adam: A Method for Stochastic Optimization. arXiv preprint arXiv:1412.6980.
Leskovec, J., et al. (2009). Community detection in large networks. Social Networks.
Mikolov, T., et al. (2013). Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781.
Nadeau, D., & Sekine, S. (2007). A survey of named entity recognition and classification. Lingvisticae Investigationes.
Newman, M. E. J. (2010). Networks: An Introduction. Oxford University Press.
Papadopoulos, S., et al. (2011). Spam filtering in Twitter using language modeling. Proceedings of the 3rd ACM Workshop on Social Web Search and Mining.
Pearl, J. (2009). Causality: Models, Reasoning, and Inference. Cambridge University Press.
Pennington, J., et al. (2014). GloVe: Global Vectors for Word Representation. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP).
Rajpurkar, P., et al. (2016). SQuAD: 100,000+ Questions for Machine Comprehension of Text. arXiv preprint arXiv:1606.05250.
Rumelhart, D. E., et al. (1986). Learning representations by back-propagating errors. Nature.
Schuster, M., & Paliwal, K. K. (1997). Bidirectional recurrent neural networks. IEEE Transactions on Signal Processing.
Tang, J., et al. (2015). LINE: Large-scale Information Network Embedding. Proceedings of the 24th International Conference on World Wide Web.
Vaswani, A., et al. (2017). Attention Is All You Need. Advances in Neural Information Processing Systems (NeurIPS).
Wang, C., et al. (2011). User behaviour modeling for detecting spammers in social networks. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
Xu, C., et al. (2020). Federated learning for user privacy protection in social networks. IEEE Transactions on Neural Networks and Learning Systems.
Zhang, X., et al. (2018). Deep learning-based fake news detection. ACM Transactions on Intelligent Systems and Technology (TIST).
Zhou, J., et al. (2019). Graph neural networks: A review of methods and applications. arXiv preprint arXiv:1812.08434.
Zubiaga, A., et al. (2018). Detection and resolution of rumors in social media. ACM Computing Surveys.
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