Deep learning for comment spam and scams

Research Article
Open access

Deep learning for comment spam and scams

Jiangchuan Liu 1*
  • 1 BASIS Bilingual School, Shenzhen, China    
  • *corresponding author Jiangchuan.Liu90678-bbsz@basischina.com
Published on 14 June 2023 | https://doi.org/10.54254/2755-2721/6/20230734
ACE Vol.6
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-59-1
ISBN (Online): 978-1-915371-60-7

Abstract

As the popularity of online social networks has increased, so has the manner in which people purchase online. Online reviews on the purchasing of items or the provision of services have become the primary source of user opinion. When a substantial body of knowledge assesses a product or service, that body's effect on the market is significant. Because of this, concerns have been raised among manufacturers and merchants, who frequently compose rubbish reviews to promote or denigrate the quality of specific items or services in order to make a profit or maintain their reputation. You may choose to promote or denigrate certain products or services that have been targeted. This kind of commentary is known as "trash remark." Comment spam violates the interests of enterprises to a significant level, and it possesses a defensive mechanism that combines the "Deepfake" of artificial intelligence technology, which renders conventional security measures inapplicable. Deep learning is the approach that will be most effective in resolving this issue. Researchers assess this research according on how they extract features from the commentary dataset and how they extract features from the commentary dataset, employing a variety of methodologies and strategies to find solutions to the problems. The authors also looked at the key deep learning technologies that have been suggested as a solution to the issue of spam detection in the primary machine, as well as the performance of various deep learning technologies. At the same time, the fact that there is not a lot of spam online review data makes this sort of research a highly significant one. The purpose of this article is to offer an insightful and all-encompassing analysis of comparative research on the topic of spam detection that has recently been conducted.

Keywords:

Machine learning, Deep learning Big data, Deepfake, Social spam, Spambot, Spammer.

Liu,J. (2023). Deep learning for comment spam and scams. Applied and Computational Engineering,6,18-23.
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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.

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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About volume

Volume title: Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

ISBN：978-1-915371-59-1(Print) / 978-1-915371-60-7(Online)
Editor：Omer Burak Istanbullu
Conference website: http://www.confspml.org
Conference date: 25 February 2023
Series: Applied and Computational Engineering
Volume number: Vol.6
ISSN：2755-2721(Print) / 2755-273X(Online)

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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.

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