Abstract
Social media platform plays a major role in everyone’s day-to-day life activities. Twitter is one of the vast growing platforms but it is also subjected to attacks such as Spamming and Combat Twitter attacks. The spamming is the use of the system to send an unsolicited message, especially the advertisement, sending messages repeatedly on same site which leads to major loss for customers and organization. In literature, the existing techniques for detecting the twitter spam text tweet suffer due to an issue such as limited work performance and data sets which leads to inefficiency of system. In order to solve these problems, we proposed a framework to detect the text-based spam tweets using Naive Bayes Classification algorithm and Artificial Neural Network. Performance study of these two algorithms shows that Artificial Neural Network performs better than Naive Bayes Classification algorithm.
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References
Gupta, H., Jamal, M. S., Madisetty, S., & Desarkar, M. S. A framework for real-time spam detection in twitter.
Katpatal, R., et al. (2018). An efficient approach of spam detection in twitter. In Proceedings of the International Conference on Inventive Research in Computing Applications (ICIRCA 2018). IEEE Xplore Compliant Part Number: CFP18N67-ART. ISBN: 978-1-5386-2456-2.
Kaur, P., et al. (2016). Spam detection on twitter: A survey. In International Conference on Computing for Sustainable Global Development (INDIACom).
Lee, K., Caverlee, J., & Webb, S. (2010). Uncovering social spammers: Social honeypots + machine learning. In Proceedings of 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 435–442).
Song, J., Lee, S., & Kim, J. (2011). Spam ltering in twitter using sender-receiver relationship. In Proceedings of 14th International Conference Recent Advances Intrusion Detection (pp. 301–317).
Junnarkar, A. (2018). An efficient approach of spam detection in twitter. In Proceedings of the International Conference on Inventive Research in Computing Applications (ICIRCA 2018). IEEE Xplore Compliant Part Number: CFP18N67-ART; ISBN: 978-1-5386-2456-2.
Chen, C., Zhang, J., Chen, X., Xiang, Y., & Zhou, W. (2015). 6 million spam tweets: A large ground truth for timely twitter spam detection. In IEEE ICC-Communication and Information System Security Symposium.
Chen, C., Zhang, J., Xie, Y., Xiang, Y., Zhou, W., Hassan, M. M., AlElaiwi, A., & Alrubaian, M. (2015). A performance evaluation of machine learning based streaming spam tweet detection. IEEE Transaction on Computational Social System, 2(3).
Mateen, M., Islam, M. A., & Iqbal, M. A. (2017). A hybrid approach for spam detection for twitter. In Proceedings of 2017 14th International Bhurban Conference on Applied Sciences & Technology (IBCAST). Islamabad, Pakistan, 10th–14th January.
Madisetty, S., & Desarkar, M. S. (2018). A neural network-based ensemble approach for spam detection in twitter. IEEE Transaction on Computational Social Systems.
Chen, W., et al. Real-time twitter content polluter detection based on direct features. 978-1-4673-8611-1/15/$31.00 ©2015. IEEE.
Kamble, S., & Sangve, S. M. (2018). Real time detection of drifted twitter spam based on statistical features. In IEEE International Conference on Information, Communication, Engineering and Technology (ICICET).
Alon, Z., et al. (2018). Detecting spam accounts on twitter. IEEE/ACM ASONAM 2018, Barcelona, Spain. 978-1-5386-6051-5/18/$31.00 ©2018 IEEE, August 28–31.
Hasan, K. M. A., Sabuj, M. S., & Afrin, Z. (2015). Opinion mining using Naive Bayes. In 2015 IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE). BUET, Dhaka, Bangladesh, 19–20 December.
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Mardi, V., Kini, A., Sukanya, V.M., Rachana, S. (2020). Text-Based Spam Tweets Detection Using Neural Networks. In: Sharma, H., Govindan, K., Poonia, R., Kumar, S., El-Medany, W. (eds) Advances in Computing and Intelligent Systems. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-0222-4_37
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DOI: https://doi.org/10.1007/978-981-15-0222-4_37
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