Bot stamina: examining the influence and staying power of bots in online social networks
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Abokhodair N, Yoo D, McDonald DW (2015) Dissecting a social botnet: growth, content and influence in twitter. In: Proc. of 18th ACM CSCW 2015, pp 839–851
Aiello LM, Deplano M, Schifanella R, Ruffo G (2014) People are strange when you’re a stranger: impact and influence of bots on social networks. In: Proc. of the 6th AAAI international Conf. On weblogs and social media. AAAI, Dublin, pp 10–17
Avvenuti M, Cresci S, Marchetti A et al (2016a) Predictability or early warning: using social media in modern emergency response. IEEE Internet Comput 20:4–6
Avvenuti M, Cresci S, Vigna FD, Tesconi M (2016b) Impromptu crisis mapping to prioritize emergency response. Computer 49:28–37
Bakshy E, Hofman JM, Mason WA, Watts DJ (2011) Everyone’s an influencer: quantifying influence on twitter. In: Proc of the Fourth ACM International Conf on Web Search and Data Mining. ACM, New York, pp 65–74
Beskow DM, Carley KM (2018, 2018) Bot-hunter: a tiered approach to Detecting & Characterizing Automated Activity on twitter. SBP-BRiMS 2018. Intl. Conf. on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation
Bessi A, Ferrara E (2016) Social bots distort the 2016 U.S. presidential election online discussion. First Monday 21(11)
Blackwell D, Leaman C, Tramposch R et al (2017) Extraversion, neuroticism, attachment style and fear of missing out as predictors of social media use and addiction. Personal Individ Differ 116:69–72
Boshmaf Y, Muslukhov I, Beznosov K, Ripeanu M (2013) Design and analysis of a social botnet. Comput Netw 57:556–578
Boyd D, Golder S, Lotan G (2010) Tweet, tweet, retweet: conversational aspects of retweeting on twitter. In: Proceedings of the 2010 43rd Hawaii international conference on system sciences. IEEE Computer Society, Washington, pp 1–10
Brin S, Page L (1998) The anatomy of a large-scale hypertextual web search engine. Comput Netw ISDN Syst 30:107–117
Broniatowski DA, Jamison AM, Qi S et al (2018) Weaponized health communication: twitter bots and Russian trolls amplify the vaccine debate. Am J Public Health 108:1378–1384
Cha M, Haddadi H, Benevenuto F, Gummadi KP (2010) Measuring user influence in twitter : the million follower fallacy. In: Proceedings of the fourth international AAAI conference on weblogs and social media (ICWSM 2010). AAAI Press, Washington, DC, pp 10–17
Chavoshi N, Hamooni H, Mueen A (2016) DeBot: twitter bot detection via warped correlation. In: 2016 IEEE 16th International Conference on Data Mining (ICDM), pp 817–822
Chavoshi N, Hamooni H, Mueen A (2017) Temporal patterns in bot activities. In: Proc. of 26th International Conf. on WWW, pp 1601–1606
Chavoshi N, Mueen A (2018) Model bots, not humans on social media. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) IEEE, pp 178–185
Chu Z, Gianvecchio S, Wang H, Jajodia S (2012) Detecting automation of twitter accounts: are you a human, bot, or cyborg? IEEE Trans Dependable Secure Comput 9:811–824
Ciampaglia GL (2018) Fighting fake news: a role for computational social science in the fight against digital misinformation. J Comput Soc Sc 1:147–153
Conover MD, Ratkiewicz J, Francisco M et al (2011) Political polarization on twitter. In: Fifth international AAAI conference on weblogs and social media, pp 10–17
Cresci S, Di Pietro R, Petrocchi M et al (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. International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, pp 963–972
Cresci S, Lillo F, Regoli D et al (2018a) $ FAKE: evidence of spam and bot activity in stock microblogs on twitter. In: Twelfth international AAAI conference on web and social media, pp 580–583
Cresci S, Lillo F, Regoli D et al (2019a) Cashtag piggybacking: uncovering spam and bot activity in stock microblogs on twitter. ACM Trans Web 13:11:1–11:27
Cresci S, Petrocchi M, Spognardi A, Tognazzi S (2018b) From reaction to Proaction: unexplored ways to the detection of evolving Spambots. In: WWW (Companion Volume), pp 1469–1470
Cresci S, Petrocchi M, Spognardi A, Tognazzi S (2019b) On the capability of evolved spambots to evade detection via genetic engineering. Online Soc Netw Media 9:1–16
Cresci S, Petrocchi M, Spognardi A, Tognazzi S (2019c) Better safe than sorry: an adversarial approach to improve social bot detection. In WebSci '19: Proc. of the 11th ACM Conference on Web Scienc. ACM, New York, pp 47-56
Cresci S, Pietro RD, Petrocchi M et al (2018c) Social fingerprinting: detection of Spambot groups through DNA-inspired behavioral modeling. IEEE Trans Dependable Secure Comp 15:561–576
Crooks A, Croitoru A, Stefanidis A, Radzikowski J (2013) #earthquake: twitter as a distributed sensor system. Trans GIS 17:124–147
Davis CA, Varol O, Ferrara E et al (2016) In WWW '16 Companion: Proc. of the 25th Intl. Conf. Companion on World Wide Web, IW3C2, Geneva, pp 273–274
Duh A, Slak Rupnik M, Korošak D (2018) Collective behavior of social bots is encoded in their temporal twitter activity. Big Data 6:113–123
Fuchs C (2005) The internet as a self-organizing socio-technological system. Cybernetics Human Knowing 12:37–81
Grinberg N, Joseph K, Friedland L et al (2019) Fake news on twitter during the 2016 U.S. presidential election. Science 363:374–378
Hagberg A, Schult D, Swart P (2008) Exploring network structure, dynamics, and function using NetworkX. In SciPy2008: Proc. of the 7th Python in science conference, pp 11–15
Hecking T, Steinert L, Masias VH, Ulrich Hoppe H (2018) Relational patterns in cross-media information diffusion networks. In: Cherifi C, Cherifi H, Karsai M, Musolesi M (eds) Complex Networks & Their Applications VI. Springer International Publishing, Cham, pp 1002–1014
Hegelich S, Janetzko D (2016) Are Social Bots on Twitter Political Actors? Empirical Evidence from a Ukrainian Social Botnet. In: Proc. Of the 10th Intl. Conf. on Web and Social Media (ICWSM), ICWSM, pp 579–582
Howard PN, Kollanyi B (2016) Bots, #StrongerIn, and #Brexit: computational propaganda during the UK-EU referendum. SSRN, https://doi.org/10.2139/ssrn.2798311
Howard PN, Woolley S, Calo R (2018) Algorithms, bots, and political communication in the US 2016 election: the challenge of automated political communication for election law and administration. J Inform Tech Polit 15:81–93
Kwak H, Lee C, Park H, Moon S (2010) What is twitter, a social network or a news media? In: Proceedings of the 19th international conference on world wide web. ACM, New York, pp 591–600
Mazza M, Cresci S, Avvenuti M et al (2019) RTbust: exploiting temporal patterns for botnet detection on twitter. In WebSci '19: Proc. of the 11th ACM Conference on Web Science. ACM, New York, pp 183–192
Mitchell A (2018) Americans still prefer watching to Reading the news - and mostly still through television. Pew Research Center, Washington, D.C.
Mønsted B, Sapieżyński P, Ferrara E, Lehmann S (2017) Evidence of complex contagion of information in social media: an experiment using twitter bots. PLoS One 12:e0184148
Morstatter F, Wu L, Nazer TH et al (2016) A new approach to bot detection: striking the balance between precision and recall. In: 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp 533–540
Murthy D, Powell AB, Tinati R et al (2016) Automation, algorithms, and politics| bots and political influence: a sociotechnical investigation of social network capital. Int J Commun 10:20
Perna D, Tagarelli A (2018) Learning to rank social bots. In: Proceedings of the 29th on hypertext and social media. ACM, New York, pp 183–191
Piraveenan M, Prokopenko M, Hossain L (2013) Percolation centrality: quantifying graph-theoretic impact of nodes during percolation in networks. PLoS One 8(1):e53095
Reece AG, Reagan AJ, Lix KLM et al (2017) Forecasting the onset and course of mental illness with twitter data. Sci Rep 7:13006
Riquelme F, González-Cantergiani P (2016) Measuring user influence on twitter: a survey. Inf Process Manag 52:949–975
Sakaki T, Okazaki M, Matsuo Y (2013) Tweet analysis for real-time event detection and earthquake reporting system development. IEEE Trans Knowl Data Eng 25:919–931
Schuchard R, Crooks A, Stefanidis A, Croitoru A (2019) Bots in nets: empirical comparative analysis of bot evidence in social networks. In: Aiello LM, Cherifi C, Cherifi H et al (eds) Complex networks and their applications VII. Springer International Publishing, Cham, pp 424–436
Shao C, Ciampaglia GL, Varol O et al (2018) The spread of low-credibility content by social bots. Nat Commun 9:4787
Stella M, Ferrara E, Domenico MD (2018) Bots increase exposure to negative and inflammatory content in online social systems. PNAS 115:12435–12440
Strohmaier M, Wagner C (2014) Computational social science for the world wide web. IEEE Intell Syst 29:84–88
Suárez-Serrato P, Roberts ME, Davis C, Menczer F (2016) On the influence of social bots in online protests. In: Spiro E, Ahn Y-Y (eds) Social informatics. Springer International Publishing, Berlin, pp 269–278
Sunstein CR (2018) #republic: divided democracy in the age of social media. Princeton University Press, Princeton, NJ
Tufekci Z (2014) Big questions for social media big data: representativeness, validity and other methodological pitfalls. In ICWSM ’14: Proc. of the 8th Intl. AAAI Conference on Weblogs and Social Media. AAAI, Palo Alto, pp 505–514.
Varol O, Ferrara E, Davis CA et al (2017) Online human-bot interactions: detection, estimation, and characterization. In: Proc. of the 11th international AAAI Conf. On web and social media. AAAI, Montréal, pp 280–289
Wasserman S, Faust K (1994) Social network analysis: methods and applications, 1st edn. Cambridge University Press, Cambridge