Abstract
Recent research has shown a substantial active presence of bots in online social networks (OSNs). In this article, we perform a comparative analysis of the usage and impact of bots and humans on Twitter—one of the largest OSNs in the world. We collect a large-scale Twitter dataset and define various metrics based on tweet metadata. Using a human annotation task, we assign “bot” and “human” ground-truth labels to the dataset and compare the annotations against an online bot detection tool for evaluation. We then ask a series of questions to discern important behavioural characteristics of bots and humans using metrics within and among four popularity groups. From the comparative analysis, we draw clear differences and interesting similarities between the two entities.
- Norah Abokhodair, Daisy Yoo, and David W. McDonald. 2015. Dissecting a social botnet: Growth, content and influence in Twitter. In Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work 8 Social Computing. ACM, 839--851. Google Scholar
Digital Library
- Luca Maria Aiello, Martina Deplano, Rossano Schifanella, and Giancarlo Ruffo. 2012. People are strange when you’re a stranger: Impact and influence of bots on social networks. In Proceedings of the 6th International AAAI Conference on Web and Social Media (ICWSM’12).Google Scholar
- Aris Anagnostopoulos, Ravi Kumar, and Mohammad Mahdian. 2008. Influence and correlation in social networks. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’08). ACM, 7--15. Google Scholar
Digital Library
- Marco Avvenuti, Salvatore Bellomo, Stefano Cresci, Mariantonietta Noemi La Polla, and Maurizio Tesconi. 2017. Hybrid crowdsensing: A novel paradigm to combine the strengths of opportunistic and participatory crowdsensing. In Proceedings of the 26th International Conference on World Wide Web Companion (WWW’17 Companion). 1413--1421. Google Scholar
Digital Library
- Alessandro Bessi and Emilio Ferrara. 2016. Social bots distort the 2016 U.S. Presidential election online discussion. First Monday 21, 11 (2016).Google Scholar
- D. Boyd, S. Golder, and G. Lotan. 2010. Tweet, tweet, retweet: Conversational aspects of retweeting on Twitter. In Proceedings of the 43rd Hawaii International Conference on System Sciences (HICSS’10). 1--10. Google Scholar
Digital Library
- Florian Brachten, Stefan Stieglitz, Lennart Hofeditz, Katharina Kloppenborg, and Annette Reimann. 2017. Strategies and influence of social bots in a 2017 German state election - A case study on Twitter. https://arxiv.org/abs/1710.07562.Google Scholar
- Nikan Chavoshi, Hossein Hamooni, and Abdullah Mueen. 2016. DeBot: Twitter bot detection via warped correlation. In Proceedings of the IEEE International Conference on Data Mining (ICDM’16). 817--822.Google Scholar
Cross Ref
- Nikan Chavoshi, Hossein Hamooni, and Abdullah Mueen. 2016. Identifying correlated bots in Twitter. In Social Informatics, Emma Spiro and Yong-Yeol Ahn (Eds.). Springer International Publishing, Cham, 14--21.Google Scholar
- Nikan Chavoshi, Hossein Hamooni, and Abdullah Mueen. 2017. Temporal patterns in bot activities. In Proceedings of the 26th International Conference on World Wide Web Companion (WWW’17 Companion). 1601--1606. Google Scholar
Digital Library
- Z. Chu, S. Gianvecchio, H. Wang, and S. Jajodia. 2012. Detecting automation of Twitter accounts: Are you a human, bot, or cyborg? IEEE Trans. Depend. Sec. Comput. 9, 6 (Nov. 2012), 811--824. Google Scholar
Digital Library
- Jacob Cohen. 1960. A coefficient of agreement for nominal scales. Educ. Psychol. Measure. 20, 1 (1960), 37--46.Google Scholar
Cross Ref
- Stefano Cresci, Roberto Di Pietro, Marinella Petrocchi, Angelo Spognardi, and Maurizio Tesconi. 2017. Exploiting digital DNA for the analysis of similarities in Twitter behaviours. In Proceedings of the 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA’17). 686--695.Google Scholar
Cross Ref
- Stefano Cresci, Roberto Di Pietro, Marinella Petrocchi, Angelo Spognardi, and Maurizio Tesconi. 2017. The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In Proceedings of the 26th International Conference on World Wide Web Companion (WWW’17 Companion). 963--972. Google Scholar
Digital Library
- Stefano Cresci, Roberto Di Pietro, Marinella Petrocchi, Angelo Spognardi, and Maurizio Tesconi. 2018. Social fingerprinting: Detection of spambot groups through DNA-inspired behavioral modeling. IEEE Trans. Depend. Sec. Comput. 15, 4 (2018), 561--576.Google Scholar
- Stefano Cresci, Fabrizio Lillo, Daniele Regoli, Serena Tardelli, and Maurizio Tesconi. 2018. Cashtag piggybacking: Uncovering spam and bot activity in stock microblogs on Twitter. CoRR abs/1804.04406 (2018). arxiv:1804.04406 http://arxiv.org/abs/1804.04406Google Scholar
- Stefano Cresci, Fabrizio Lillo, Daniele Regoli, Serena Tardelli, and Maurizio Tesconi. 2018. FAKE: Evidence of spam and bot activity in stock microblogs on Twitter. In Proceedings of the 12th International AAAI Conference on Web and Social Media (ICWSM’18).Google Scholar
- Stefano Cresci, Roberto Di Pietro, Marinella Petrocchi, Angelo Spognardi, and Maurizio Tesconi. 2015. Fame for sale: Efficient detection of fake Twitter followers. Decision Supp. Syst. 80, C (2015), 56--71. Google Scholar
Digital Library
- Stefano Cresci, Roberto Di Pietro, Marinella Petrocchi, Angelo Spognardi, and Maurizio Tesconi. 2016. DNA-Inspired online behavioral modeling and its application to spambot detection. IEEE Intell. Syst. 31, 5 (Sep. 2016), 58--64.Google Scholar
Cross Ref
- Nicolas Dugué, Anthony Perez, Maximilien Danisch, Florian Bridoux, Amélie Daviau, Tennessy Kolubako, Simon Munier, and Hugo Durbano. 2015. A reliable and evolutive web application to detect social capitalists. In Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM’15). ACM, 741--744. Google Scholar
Digital Library
- Chad Edwards, Autumn Edwards, Patric R. Spence, and Ashleigh K. Shelton. 2014. 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. Comput. Hum. Behav. 33 (2014), 372--376. Google Scholar
Digital Library
- Emilio Ferrara, Onur Varol, Clayton Davis, Filippo Menczer, and Alessandro Flammini. 2016. The rise of social bots. Commun. ACM 59, 7 (Jun. 2016), 96--104. Google Scholar
Digital Library
- Zafar Gilani, Reza Farahbakhsh, and Jon Crowcroft. 2017. Do bots impact Twitter activity? In Proceedings of the 26th International Conference on World Wide Web Companion (WWW’17 Companion). 781--782. Google Scholar
Digital Library
- Zafar Gilani, Reza Farahbakhsh, Gareth Tyson, Liang Wang, and Jon Crowcroft. 2017. Of bots and humans (on Twitter). In Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM’17). ACM, New York, NY, 349--354. Google Scholar
Digital Library
- Zafar Gilani, Liang Wang, Jon Crowcroft, Mario Almeida, and Reza Farahbakhsh. 2016. Stweeler: A framework for Twitter bot analysis. In Proceedings of the 25th International Conference Companion on World Wide Web (WWW’16 Companion). 37--38. Google Scholar
Digital Library
- Kyumin Lee, Brian David Eoff, and James Caverlee. 2011. Seven months with the devils: A long-term study of content polluters on Twitter. In Proceedings of the International AAAI Conference on Web and Social Media (ICWSM’11).Google Scholar
- Amanda Minnich, Nikan Chavoshi, Danai Koutra, and Abdullah Mueen. 2017. BotWalk: Efficient adaptive exploration of Twitter bot networks. In Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM’17). ACM, New York, NY, 467--474. Google Scholar
Digital Library
- B. Monsted, P. Sapiezynski, E. Ferrara, and S. Lehmann. 2017. Evidence of complex contagion of information in social media: An experiment using Twitter bots. PLoS ONE 12, 9 (2017).Google Scholar
- Alessandro Murgia, Daan Janssens, Serge Demeyer, and Bogdan Vasilescu. 2016. Among the machines: Human-bot interaction on social Q8A websites. In Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA’16). ACM, New York, NY, 1272--1279. Google Scholar
Digital Library
- Lauren Scissors, Moira Burke, and Steven Wengrovitz. 2016. What’s in a like?: Attitudes and behaviors around receiving likes on Facebook. In Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work 8 Social Computing (CSCW’16). ACM, 1501--1510. Google Scholar
Digital Library
- Onur Varol, Emilio Ferrara, Clayton A. Davis, Filippo Menczer, and Alessandro Flammini. 2017. Online human-bot interactions: Detection, estimation, and characterization. arXiv preprint arXiv:1703.03107 (2017).Google Scholar
- A. Vishwanath, Jiazhen Zhu, K. Hinton, R. Ayre, and R. S. Tucker. 2013. Estimating the energy consumption for packet processing, storage and switching in optical-IP routers. In Proceedings of the Annual Optical Fiber Communication Conferend and the National Fiber Optic Engineers Conference (OFC/NFOEC’13). 1--3.Google Scholar
- Claudia Wagner, Silvia Mitter, Christian Körner, and Markus Strohmaier. 2012. When social bots attack: Modeling susceptibility of users in online social networks. In Proceedings of the 2nd Workshop on Making Sense of Microposts Held in Conjunction with the 21st World Wide Web Conference.Google Scholar
- Xian Wu, Ziming Feng, Wei Fan, Jing Gao, and Yong Yu. 2013. Detecting Marionette Microblog Users for Improved Information Credibility. Springer, Berlin, 483--498.Google Scholar
Index Terms
A Large-scale Behavioural Analysis of Bots and Humans on Twitter
Recommendations
Of Bots and Humans (on Twitter)
ASONAM '17: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017Recent research has shown a substantial active presence of bots in online social networks (OSNs). In this paper we utilise our previous work (Stweeler) to comparatively analyse the usage and impact of bots and humans on Twitter, one of the largest OSNs ...
Behavioural detection with API call-grams to identify malicious PE files
SecurIT '12: Proceedings of the First International Conference on Security of Internet of ThingsPresent day malware shows stealthy and dynamic capability to avail administrative rights and control the victim computer [10]. Malware writers depend on evasion techniques like code obfuscation, packing, compression, encryption or polymorphism to avoid ...
Behavioural Correlation for Detecting P2P Bots
ICFN '10: Proceedings of the 2010 Second International Conference on Future NetworksIn the past few years, IRC bots, malicious programs which are remotely controlled by attackers through IRC servers, have become a major threat to the Internet and for users. These bots can be used in different malicious ways such as issuing distributed ...






Comments