Bots Sociais: Como robôs podem se tornar pessoas influentes no Twitter?

• Johnnatan Messias UFOP
• Fabrício Benevenuto UFMG
• Ricardo Oliveira UFOP

Abstract

Systems like Klout and Twitalyzer were developed as an attempt to measure the influence of user within social networks. Although the algorithms used by these systems are not known, they have been widely used to rank users according to their influence in the Twitter social network. As media companies might base their viral marketing campaigns on influence scores, in this paper, we investigate if these systems are vulnerable and easy to manipulate. Our approach consists of developing Twitter robot accounts able to interact with real users in order to verify strategies that can increase their influence scores according to different systems. Our results show that it is possible to become influential using very simple strategies, suggesting that these systems should review their influence score algorithms to avoid accounting with automatic activity.

References

Klout. http://www.klout.com, March 2012.

Twitalyzer. http://www.twitalyzer.com/, March 2012.

Purohit H., Ruan Y., Joshi A., Parthasarathy S., and Sheth A. Understanding user-community engagement by multi-faceted features: A case study on twitter. In Conference of Social Media Analytics Workshop at World Wide Web (WWW), 2011.

Yan J. L. S. and Kaziunas E. What is a tweet worth?: measuring the value of social media for an academic institution. In Proceedings of the 2012 iConference, iConference ’12, pages 565–566, New York, NY, USA, 2012. ACM.

David Leonhardt. A better way to measure twitter inuence. The New York Times, 2011.

Cha M., Haddadi H., Benevenuto F., and Gummadi K. P. Measuring User Inuence in Twitter: The Million Follower Fallacy. In AAAI ICWSM, May 2010.

Romero D. M., Galuba W., Asur S., and Huberman B. A. Inuence and passivity in social media. In ACM WWW, pages 113–114, 2011.

Weng J., Ee-Peng Lim, Jiang J., and He Q. Twitterrank: nding topic-sensitive inuential twitterers. In ACM WSDM, 2010.

Pal A. and Counts S. Identifying topical authorities in microblogs. In ACM WSDM, pages 45–54, 2011.

Ghosh S., Viswanath B., Kooti F., Sharma N. K., Gautam K., Benevenuto F., Ganguly N., and Gummadi K. Understanding and Combating Link Farming in the Twitter Social Network. In Proceedings of the 21st International World Wide Web Conference (WWW’12), Lyon, France, April 2012.

Boshmaf Y., Muslukhov I., Beznosov K., and Ripeanu M. The socialbot network: When bots socialize for fame and money. In ACSAC‘11 Proceedings of the 27th Annual Computer Security Applications Conference, pages 93–102, New York, NY, USA, 2011.

Messias J., Schmidt L., Oliveira R., and Benevenuto F. Sigam-me os bons! transformando robôs em pessoas inuentes no twitter. BraSNAM, 2012.