Zafar Gilani
ABSTRACT
Online social networks (OSNs) have seen a remarkable rise in the presence of surreptitious automated accounts. Massive human user-base and business-supportive operating model of social networks (such as Twitter) facilitates the creation of automated agents. In this paper we outline a systematic methodology and train a classifier to categorise Twitter accounts into 'automated' and 'human' users. To improve classification accuracy we employ a set of novel steps. First, we divide the dataset into four popularity bands to compensate for differences in types of accounts. Second, we create a large ground truth dataset using human annotations and extract relevant features from raw tweets. To judge accuracy of the procedure we calculate agreement among human annotators as well as with a bot detection research tool. We then apply a Random Forests classifier that achieves an accuracy close to human agreement. Finally, as a concluding step we perform tests to measure the efficacy of our results.
- F. Benevenuto, G. Magno, T. Rodrigues, and V. Almeida. Detecting spammers on twitter. In Collaboration, electronic messaging, anti-abuse and spam conference (CEAS), volume 6, page 12, 2010.Google Scholar
- Y. Boshmaf, I. Muslukhov, K. Beznosov, and M. Ripeanu. The socialbot network: When bots socialize for fame and money. In Proceedings of the 27th Annual Computer Security Applications Conference, ACSAC '11, pages 93--102, New York, NY, USA, 2011. ACM. Google ScholarDigital Library
- Z. Chu, S. Gianvecchio, H. Wang, and S. Jajodia. Who is tweeting on twitter: Human, bot, or cyborg? In Proceedings of the 26th Annual Computer Security Applications Conference, ACSAC '10, pages 21--30, New York, NY, USA, 2010. ACM. Google ScholarDigital Library
- J. Cohen. A coefficient of agreement for nominal scales. Educational and psychological measurement, 20(1):37--46, 1960. Google ScholarCross Ref
- C. A. Davis, O. Varol, E. Ferrara, A. Flammini, and F. Menczer. Botornot: A system to evaluate social bots. In Proceedings of the 25th International Conference Companion on World Wide Web, WWW '16 Companion, pages 273--274, Republic and Canton of Geneva, Switzerland, 2016. International World Wide Web Conferences Steering Committee. Google ScholarDigital Library
- C. Edwards, A. Edwards, P. R. Spence, and A. K. Shelton. Is that a bot running the social media feed? testing the differences in perceptions of communication quality for a human agent and a bot agent on twitter. Computers in Human Behavior, 33:372--376, 2014. Google ScholarDigital Library
- E. Ferrara, O. Varol, C. Davis, F. Menczer, and A. Flammini. The rise of social bots. Commun. ACM, 59(7):96--104, June 2016. Google ScholarDigital Library
- C. Freitas, F. Benevenuto, S. Ghosh, and A. Veloso. Reverse engineering socialbot infiltration strategies in twitter. In Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015, ASONAM '15, pages 25--32, New York, NY, USA, 2015. ACM. Google ScholarDigital Library
- Z. Gilani, R. Farahbakhsh, and J. Crowcroft. Do bots impact twitter activity? In Proceedings of the 26th International Conference on World Wide Web Companion, WWW '17 Companion, pages 781--782, Republic and Canton of Geneva, Switzerland, 2017. International World Wide Web Conferences Steering Committee.Google Scholar
- Z. Gilani, R. Farahbakhsh, G. Tyson, L. Wang, and J. Crowcroft. An in-depth characterisation of bots and humans on twitter. arXiv preprint arXiv:1704.01508, 2017.Google Scholar
- Z. Gilani, L. Wang, J. Crowcroft, M. Almeida, and R. Farahbakhsh. Stweeler: A framework for twitter bot analysis. In Proceedings of the 25th International Conference Companion on World Wide Web, WWW '16 Companion, pages 37--38, Republic and Canton of Geneva, Switzerland, 2016. International World Wide Web Conferences Steering Committee. Google ScholarDigital Library
- B. Krishnamurthy, P. Gill, and M. Arlitt. A few chirps about twitter. In Proceedings of the First Workshop on Online Social Networks, WOSN '08, pages 19--24, New York, NY, USA, 2008. ACM. Google ScholarDigital Library
- J. R. Landis and G. G. Koch. The measurement of observer agreement for categorical data. biometrics, pages 159--174, 1977.Google Scholar
- K. Lee, J. Caverlee, and S. Webb. Uncovering social spammers: Social honeypots + machine learning. In Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR '10, pages 435--442, New York, NY, USA, 2010. ACM. Google ScholarDigital Library
- K. Lee, B. D. Eoff, and J. Caverlee. Seven months with the devils: A long-term study of content polluters on twitter. In ICWSM, 2011.Google Scholar
- S. Savage, A. Monroy-Hernandez, and T. Höllerer. Botivist: Calling volunteers to action using online bots. In Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing, CSCW '16, pages 813--822, New York, NY, USA, 2016. ACM. Google ScholarDigital Library
- A. Sordoni, M. Galley, M. Auli, C. Brockett, Y. Ji, M. Mitchell, J.-Y. Nie, J. Gao, and W. B. Dolan. A neural network approach to context-sensitive generation of conversational responses. In HLT-NAACL, pages 196--205. Association for Computational Linguistics, May--June 2015. Google ScholarCross Ref
- G. Stringhini, C. Kruegel, and G. Vigna. Detecting spammers on social networks. In Proceedings of the 26th Annual Computer Security Applications Conference, ACSAC '10, pages 1--9, New York, NY, USA, 2010. ACM. Google ScholarDigital Library
- V. S. Subrahmanian, A. Azaria, S. Durst, V. Kagan, A. Galstyan, K. Lerman, L. Zhu, E. Ferrara, A. Flammini, and F. Menczer. The darpa twitter bot challenge. Computer, 49(6):38--46, June 2016. Google ScholarDigital Library
- B. Viswanath, M. A. Bashir, M. Crovella, S. Guha, K. P. Gummadi, B. Krishnamurthy, and A. Mislove. Towards detecting anomalous user behavior in online social networks. In Usenix Security, volume 14, 2014.Google Scholar
- J. Yan. Bot, cyborg and automated turing test. In International Workshop on Security Protocols, pages 190--197. Springer, 2006.Google Scholar
- Classification of Twitter Accounts into Automated Agents and Human Users
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