References
[1]. Crawford, M., Khoshgoftaar, T. M., Prusa, J. D., Richter, A. N., & Al Najada, H. (2015). Survey of review spam detection using machine learning techniques. Journal of Big Data, 2(1), 1-24.
[2]. Rao, S., Verma, A. K., & Bhatia, T. (2021). A review on social spam detection: challenges, open issues, and future directions. Expert Systems with Applications, 186, 115742.
[3]. Hussain, N., Turab Mirza, H., Rasool, G., Hussain, I., & Kaleem, M. (2019). Spam review detection techniques: A systematic literature review. Applied Sciences, 9(5), 987.
[4]. Asghar, M. Z., Ullah, A., Ahmad, S., & Khan, A. (2020). Opinion spam detection framework using hybrid classification scheme. Soft computing, 24(5), 3475-3498.
[5]. Alom, Z., Carminati, B., & Ferrari, E. (2020). A deep learning model for Twitter spam detection. Online Social Networks and Media, 18, 100079.
[6]. You, L., Peng, Q., Xiong, Z., He, D., Qiu, M., & Zhang, X. (2020). Integrating aspect analysis and local outlier factor for intelligent review spam detection. Future Generation Computer Systems, 102, 163-172.
[7]. Jain, G., Sharma, M., & Agarwal, B. (2019). Optimizing semantic LSTM for spam detection. International Journal of Information Technology, 11(2), 239-250.
[8]. Granik, M., & Mesyura, V. (2017, May). Fake news detection using naive Bayes classifier. In 2017 IEEE first Ukraine conference on electrical and computer engineering (UKRCON) (pp. 900-903). IEEE.
[9]. Xue, H., Li, F., Seo, H., & Pluretti, R. (2015, August). Trust-aware review spam detection. In 2015 IEEE Trustcom/BigDataSE/ISPA (Vol. 1, pp. 726-733). IEEE.
[10]. Chakraborty, M., Pal, S., Pramanik, R., & Chowdary, C. R. (2016). Recent developments in social spam detection and combating techniques: A survey. Information Processing & Management, 52(6), 1053-1073.
Cite this article
Liu,J. (2023). Deep learning for comment spam and scams. Applied and Computational Engineering,6,18-23.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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References
[1]. Crawford, M., Khoshgoftaar, T. M., Prusa, J. D., Richter, A. N., & Al Najada, H. (2015). Survey of review spam detection using machine learning techniques. Journal of Big Data, 2(1), 1-24.
[2]. Rao, S., Verma, A. K., & Bhatia, T. (2021). A review on social spam detection: challenges, open issues, and future directions. Expert Systems with Applications, 186, 115742.
[3]. Hussain, N., Turab Mirza, H., Rasool, G., Hussain, I., & Kaleem, M. (2019). Spam review detection techniques: A systematic literature review. Applied Sciences, 9(5), 987.
[4]. Asghar, M. Z., Ullah, A., Ahmad, S., & Khan, A. (2020). Opinion spam detection framework using hybrid classification scheme. Soft computing, 24(5), 3475-3498.
[5]. Alom, Z., Carminati, B., & Ferrari, E. (2020). A deep learning model for Twitter spam detection. Online Social Networks and Media, 18, 100079.
[6]. You, L., Peng, Q., Xiong, Z., He, D., Qiu, M., & Zhang, X. (2020). Integrating aspect analysis and local outlier factor for intelligent review spam detection. Future Generation Computer Systems, 102, 163-172.
[7]. Jain, G., Sharma, M., & Agarwal, B. (2019). Optimizing semantic LSTM for spam detection. International Journal of Information Technology, 11(2), 239-250.
[8]. Granik, M., & Mesyura, V. (2017, May). Fake news detection using naive Bayes classifier. In 2017 IEEE first Ukraine conference on electrical and computer engineering (UKRCON) (pp. 900-903). IEEE.
[9]. Xue, H., Li, F., Seo, H., & Pluretti, R. (2015, August). Trust-aware review spam detection. In 2015 IEEE Trustcom/BigDataSE/ISPA (Vol. 1, pp. 726-733). IEEE.
[10]. Chakraborty, M., Pal, S., Pramanik, R., & Chowdary, C. R. (2016). Recent developments in social spam detection and combating techniques: A survey. Information Processing & Management, 52(6), 1053-1073.