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2020 | OriginalPaper | Buchkapitel

Detecting Malicious Social Bots: Story of a Never-Ending Clash

verfasst von : Stefano Cresci

Erschienen in: Disinformation in Open Online Media

Verlag: Springer International Publishing

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Abstract

Recently, studies on the characterization and detection of social bots were published at an impressive rate. By looking back at over ten years of research and experimentation on social bots detection, in this paper we aim at understanding past, present, and future research trends in this crucial field. In doing so, we discuss about one of the nastiest features of social bots – that is, their evolutionary nature. Then, we highlight the switch from supervised bot detection techniques – focusing on feature engineering and on the analysis of one account at a time – to unsupervised ones, where the focus is on proposing new detection algorithms and on the analysis of groups of accounts that behave in a coordinated and synchronized fashion. These unsupervised, group-analyses techniques currently represent the state-of-the-art in social bot detection. Going forward, we analyze the latest research trend in social bot detection in order to highlight a promising new development of this crucial field.

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Vorheriges Kapitel What Is Abusive Language?

Nächstes Kapitel The Markets of Manipulation: The Trading of Social Bots on Clearnet and Darknet Markets

http://reports.weforum.org/global-risks-2017.

https://about.twitter.com/en_us/values/elections-integrity.html.

Source: https://www.dimensions.ai/.

https://newsroom.fb.com/news/2018/12/inside-feed-coordinated-inauthentic-behavior/.

https://help.twitter.com/en/rules-and-policies/platform-manipulation.

https://botometer.iuni.iu.edu/bot-repository/datasets.html.

Assenmacher, D., Adam, L., Frischlich, L., Trautmann, H., Grimme, C.: Openbots. arXiv preprint arXiv:1902.06691 (2019)

Avvenuti, M., Bellomo, S., Cresci, S., La Polla, M.N., Tesconi, M.: Hybrid crowdsensing: a novel paradigm to combine the strengths of opportunistic and participatory crowdsensing. In: ACM WWW Companion (2017)

Avvenuti, M., Cresci, S., Del Vigna, F., Fagni, T., Tesconi, M.: CrisMap: a big data crisis mapping system based on damage detection and geoparsing. Inf. Syst. Front. 20(5), 993–1011 (2018)CrossRef

Avvenuti, M., Cresci, S., Marchetti, A., Meletti, C., Tesconi, M.: Predictability or early warning: using social media in modern emergency response. IEEE Internet Comput. 20(6) (2016)CrossRef

Chavoshi, N., Hamooni, H., Mueen, A.: DeBot: Twitter bot detection via warped correlation. In: IEEE ICDM (2016)

Cresci, S.: Harnessing the social sensing revolution: challenges and opportunities. Ph.D. dissertation, University of Pisa (2018)

Cresci, S., D’Errico, A., Gazzé, D., Lo Duca, A., Marchetti, A., Tesconi, M.: Towards a DBpedia of tourism: the case of Tourpedia. In: ISWC (2014)

Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: Fame for sale: efficient detection of fake Twitter followers. Decis. Support Syst. 80 (2015)CrossRef

Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: evidence, theories, and tools for the arms race. In: ACM WWW Companion (2017)

10.

Cresci, S., Lillo, F., Regoli, D., Tardelli, S., Tesconi, M.: Cashtag piggybacking: uncovering spam and bot activity in stock microblogs on Twitter. ACM Trans. Web 13(2), 11 (2019)CrossRef

11.

Cresci, S., Petrocchi, M., Spognardi, A., Tognazzi, S.: From reaction to proaction: unexplored ways to the detection of evolving spambots. In: ACM WWW Companion (2018)

12.

Cresci, S., Petrocchi, M., Spognardi, A., Tognazzi, S.: Better safe than sorry: an adversarial approach to improve social bot detection. In: ACM WebSci (2019)

13.

Cresci, S., Pietro, R.D., Petrocchi, M., Spognardi, A., Tesconi, M.: Social fingerprinting: detection of spambot groups through DNA-inspired behavioral modeling. IEEE Trans. Dependable Secure Comput. 15(4), 561–576 (2018)

14.

D’Andrea, E., Ducange, P., Lazzerini, B., Marcelloni, F.: Real-time detection of traffic from Twitter stream analysis. IEEE Trans. Intell. Transp. Syst. 16(4), 2269–2283 (2015)CrossRef

15.

Davis, C.A., Varol, O., Ferrara, E., Flammini, A., Menczer, F.: BotOrNot: a system to evaluate social bots. In: ACM WWW Companion (2016)

16.

De Cristofaro, E., Kourtellis, N., Leontiadis, I., Stringhini, G., Zhou, S., et al.: LOBO: evaluation of generalization deficiencies in Twitter bot classifiers. In: ACM ACSAC (2018)

17.

Ferrara, E.: The history of digital spam. Commun. ACM 62(8), 82–91 (2019)CrossRef

18.

Ferrara, E., Varol, O., Davis, C., Menczer, F., Flammini, A.: The rise of social bots. Communun. ACM 59(7) (2016)CrossRef

19.

Goodfellow, I.J., McDaniel, P.D., Papernot, N.: Making machine learning robust against adversarial inputs. Communun. ACM 61(7), 56–66 (2018)CrossRef

20.

Grimme, C., Assenmacher, D., Adam, L.: Changing perspectives: is it sufficient to detect social bots? In: Meiselwitz, G. (ed.) SCSM 2018. LNCS, vol. 10913, pp. 445–461. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-91521-0_32CrossRef

21.

Grimme, C., Preuss, M., Adam, L., Trautmann, H.: Social bots: human-like by means of human control? Big Data 5(4) (2017)CrossRef

22.

Jiang, M., Cui, P., Beutel, A., Faloutsos, C., Yang, S.: Catching synchronized behaviors in large networks: a graph mining approach. ACM Trans. Knowl. Discov. From Data 10(4) (2016)CrossRef

23.

Kavanaugh, A.L., et al.: Social media use by government: from the routine to the critical. Gov. Inf. Q. 29(4), 480–491 (2012)CrossRef

24.

Ledig, C., et al.: Photo-realistic single image super-resolution using a generative adversarial network. In: IEEE ICCV (2017)

25.

de Lima Salge, C.A., Berente, N.: Is that social bot behaving unethically? Commun. ACM 60(9), 29–31 (2017)CrossRef

26.

Liu, S., Hooi, B., Faloutsos, C.: HoloScope: topology-and-spike aware fraud detection. In: ACM CIKM (2017)

27.

Mazza, M., Cresci, S., Avvenuti, M., Quattrociocchi, W., Tesconi, M.: RTbust: exploiting temporal patterns for botnet detection on Twitter. In: ACM WebSci (2019)

28.

Miller, Z., Dickinson, B., Deitrick, W., Hu, W., Wang, A.H.: Twitter spammer detection using data stream clustering. Inf. Sci. 260, 64–73 (2014)CrossRef

29.

Pandey, R., Castillo, C., Purohit, H.: Modeling human annotation errors to design bias-aware systems for social stream processing. In: IEEE/ACM ASONAM (2019)

30.

Pascual, S., Bonafonte, A., Serrà, J.: SEGAN: speech enhancement generative adversarial network. In: Interspeech (2017)

31.

Sahay, R., Mahfuz, R., Gamal, A.E.: A computationally efficient method for defending adversarial deep learning attacks. arXiv preprint arXiv:1906.05599 (2019)

32.

Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nat. Commun. 9(1) (2018)

33.

Starbird, K., Arif, A., Wilson, T.: Disinformation as collaborative work: surfacing the participatory nature of strategic information operations. In: ACM CSCW (2019)

34.

Stella, M., Ferrara, E., De Domenico, M.: Bots increase exposure to negative and inflammatory content in online social systems. Proc. Nat. Acad. Sci. 115(49) (2018)CrossRef

35.

Varol, O., Ferrara, E., Davis, C.A., Menczer, F., Flammini, A.: Online human-bot interactions: detection, estimation, and characterization. In: AAAI ICWSM (2017)

36.

Yang, C., Harkreader, R., Gu, G.: Empirical evaluation and new design for fighting evolving Twitter spammers. IEEE Trans. Inf. Forensics Secur. 8(8), 1280–1293 (2013)CrossRef

37.

Yang, K.C., Varol, O., Davis, C.A., Ferrara, E., Flammini, A., Menczer, F.: Arming the public with artificial intelligence to counter social bots. Hum. Behav. Emerg. Technol. 1(1), 48–61 (2019)CrossRef

38.

Yardi, S., Romero, D., Schoenebeck, G., et al.: Detecting spam in a Twitter network. First Monday 15(1) (2010)

Titel: Detecting Malicious Social Bots: Story of a Never-Ending Clash
verfasst von: Stefano Cresci
Verlag: Springer International Publishing
Buch: Disinformation in Open Online Media
Print ISBN: 978-3-030-39626-8

Electronic ISBN: 978-3-030-39627-5

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-3-030-39627-5_7

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