The week's pick
Reseach Article
Classification of AI Powered Social Bots on Twitter by Sentiment Analysis and Data Mining through SVM
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 177 - Number 25 |
| Year of Publication: 2019 |
| Authors: Abu Foysal, Safat Islam, Touhidur Rahaman |
10.5120/ijca2019919701
|
Abu Foysal, Safat Islam, Touhidur Rahaman . Classification of AI Powered Social Bots on Twitter by Sentiment Analysis and Data Mining through SVM. International Journal of Computer Applications. 177, 25 ( Dec 2019), 13-19. DOI=10.5120/ijca2019919701
Abstract
In this paper, the behavior of twitter bots and their influence on the social media is investigated. As the user population increased on Twitter, it became an ideal platform for social manipulation and influencing perspectives. There has been a rise in autonomous entities, which are known to exploit Twitter’s API feature by performing actions such as tweeting, retweeting, liking, following, or messaging other users, that engage in social engineering. In this research, a framework based on existing research to detect these autonomous entities on Twitter is presented. For detection, tweet syntax analysis, user behavior along with sentiment analysis is performed. Sentiment analysis is an opinion mining technique which analyzes people’s opinions or sentiments. Crawling on Twitter is performed for random tweets, user specific tweets and features are extracted by aggregating the tweets by their senders. Based on the resultant information the human or bot training and classification is made. After successfully training with SVM, this model was able to detect Twitter bots with a precision of 0.75.
References
- Adams, T. AI-powered social bots. arXiv preprint arXiv:1706.05143 (2017)
- Ferrara, E., Varol, O., Davis, C.; Menczer, F., Flammini, A., The rise of social bots. Comm. ACM 59(7). (2016) 96-104
- Subrahmanian, V., Azaria, A., Durst, S., Kagan, V., Galstyan, A., Lerman, K., Zhu, L., Ferrara, E., Flammini, A., Menczer, F. The DARPA Twitter Bot Challenge. Computer, 49(6). (2016) 38-46.
- Kagan, V., Stevens, A., Subrahmanian, V. Using Twitter Sentiment to Forecast the 2013 Pakistani Election and the 2014 Indian Election. IEEE Intelligent Systems, 30(1) (2015) 2-5.
- Cotterell, J. Social Networks in Youth and Adolescence (Adolescence and Society). Routledge (2007)
- Doreian, P. and Stokman, F. Evolution of social networks, ser. The journal of mathematical sociology. Gordon & Breach Publishing Group, (1997), no. v. 1.
- Kwak, H., Lee, C., Park, H., and Moon, S. What is Twitter, a social network or a news media? in Proceedings of the 19th international conference on World wide web, ser. WWW ’10. New York, NY, USA: ACM, (2010) 591–600.
- Boyd, D., Golder, S., Lotan, G. Tweet, tweet, retweet: Conversational aspects of retweeting on Twitter, in 43rd Hawaii International Conference on System Sciences (HICSS). IEEE, (2010) 1–10.
- Wald, R., Khoshgoftaar, T. M., Napolitano, A., Sumner, C. Predicting susceptibility to social bots on Twitter. 10.1109/IRI.2013.6642447 (2013) 6-13.
- Chu, Z., Gianvecchio, S., Wang, H., Jajodia, S. Detecting automation of Twitter accounts: Are you a human, bot, or cyborg? IEEE Transactions on Dependable and Secure Computing (2012) 811–824.
- Krishnamurthy, B., Gill, P., Arlitt, M. A few chirps about twitter. Proceedings of the first workshop on Online social networks - WOSP (2008)
- Mislove, A., Marcon, M., Gummadi, K., Druschel, P., Bhattacharjee, B. Measurement and analysis of online social networks. Proceedings of the 7th ACM SIGCOMM conference on Internet measurement - IMC (2007)
- Yardi, S., Romero, D., Schoenebeck, G., Boyd, D. Detecting spam in a Twitter network. First Monday, 15(1) (2009)
- Varol, O., Ferrara, E., Davis, C. A., Flammini, A. Online Human-Bot Interactions: Detection, Estimation, and Characterization. AAAI Publications, Eleventh International AAAI Conference on Web and Social Media (2017)
- Wang, A. Detecting Spam Bots in Online Social Networking Sites: A Machine Learning Approach. Lecture Notes in Computer Science (2010) 335-342.
- Get TAGS. (2019). Get TAGS v6.1. [online] Available at: https://tags.hawksey.info/get-tags/ [Accessed 2 Apr. 2019]
- Lai, P. Extracting Strong Sentiment Trends from Twitter. Stanford Digital Library, Stanford University, Stanford, California, United States (2010)
- StreamHacker. Hierarchical Classification. [online] Available at: https://streamhacker.com/2011/01/05/ hierarchical-classification/ [Accessed 9 Nov. 2019]
- Manning, C., Schütze, H. Foundations of Statistical Natural Language Processing. Cambridge, Mass. MIT Press (1999)
- Olson, D. L., Delen, D. Advanced Data Mining Techniques, Springer, 1st edition ISBN 3-540-76916-1. (2008) 138.
- Chu, Z., Gianvecchio, S., Wang, H., Jajodia S. Who is Tweeting on Twitter: Human, Bot, or Cyborg? Proceeding ACSAC '10 Proceedings of the 26th Annual Computer Security Applications Conference (2010) 21-30
- Zhang, C. M., Paxson, V. Detecting and analyzing automated activity on twitter. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bio informatics), volume 6579 LNCS of PAM (2011)
- Kudugunta, S., Ferrara, E. Deep neural networks for bot detection. Inf. Sci., 467, (2018) 312-322.
Index Terms
Keywords