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SoBigData day @EUI, Florence, 11-10-2017
The rise of novel Twitter social spambots SoBigData day @EUI, - - PowerPoint PPT Presentation
The rise of novel Twitter social spambots SoBigData day @EUI, - - PowerPoint PPT Presentation
The rise of novel Twitter social spambots SoBigData day @EUI, Florence, 11-10-2017 Marinella Petrocchi IIT-CNR, Pisa, Italy SPAMBOTS & SOCIAL NETWORKS spambot AN OPEN PROBLEM Spambots (Semi-)automated accounts with (often) harmful
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THE RISE OF THE SOCIAL BOTS
They escape detection techniques, by evolving: On Twitter: fake followers (till 2012) 1st evolution (2012-2014) current (?) wave (2015-2017) New spambots are almost indistinguishable from genuine accounts
- E. Ferrara, O. Varol, C. Davis, F. Menczer, and A. Flammini, “The rise of social bots,” Communica)ons of the ACM, vol. 59, no. 7, pp. 96–104, 2016
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FAKE FOLLOWERS
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NAIVE FAKE ACCOUNTS WERE EASY TO BUY
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SOCIAL SPAMBOTS
The new wave
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Undistinguishable from genuine accounts if analyzed one-by-one Analysis of the online behavior of large groups of users, with the goal of detecting possible spambots among them
SOCIAL SPAMBOTS
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MODELING THE ONLINE BEHAVIOR OF USERS
The idea
Behaviour Sequence of actions performed by an account Digital DNA Each type of action is associated to a character (e.g., A, B, C) The online behaviour of an account is modeled as a sequence of characters (i.e., a string, similarly to biologic DNA) according to the sequence of actions performed by that account
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Encoding T tweet, R retweet, P reply T R R R R P …RRTRPR Timeline of a Twitter account
MODELING THE ONLINE BEHAVIOR OF USERS
The idea
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…RRTRPRTPRRPRTPRPTPRRTRPR …RPRTPTTRPTRPTPRRRRTPPRPP …TTTRRRPPTPRPTPRTRPTRRRTP …PRTRPRTPPPPRTPRRPRTPPRRT …TRTRPRTPRRPRTPRPTPTPPRTT …TRPPRTPPTRPPTPRRTTTPPRPR
DIGITAL DNA VS BIOLOGIC DNA
T tweet, R retweet, P reply …AGTCTCCATTTTCAGGTCGTA …GTTTAAGATCGCCTCATCACC …AGGCAATTCGCCTGAACTGG …AGTCTCGATCCTTTCCTCGTT …AAAATCGAACGCCTTGTCGG …ATTCTCCATCGCCTAAACAAC A adenine, G guanine, T thymine, C cytosine
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…TRRRPRRTRRPRTPRPTPRRTRPR …RPRTPTTRRRPRRTPRRRRTPPRP …TTTRRRPRRRPRRTRTRPTRRRTP …PRTRPRTPPPPRTPRRRRRPRRTR
SIMILARITY BETWEEN DIGITAL DNA SEQUENCES
Intuition Automated accounts (spambots) have similar DNA sequences LCS (longest common substring) Longest substring between N sequences of digital DNA
RRRPRRT
(length: 7 characters)
Spambots characterization
- M. Arnold and E. Ohlebusch, “Linear Lme algorithms for generalizaLons
- f the longest common substring problem,” Algorithmica, vol. 60, no. 4,
- pp. 806–818, 2011
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LCS: SPAMBOTS VS HUMANS
LCS: similarity measure
Spambots characterization
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LCS: SPAMBOTS + HUMANS (MIXED GROUP)
Spambots detection
- 1. accounts with high
similarity
- 2. steep decrease in
similarity
- 3. accounts with low
similarity
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DETECTION TECHNIQUES
Unsupervised approach
Spambots detection
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- 2. Supervised
approach
DETECTION TECHNIQUES
Spambots detection
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DATASETS
Evaluation datasets: 1. Mixed1 (1982 accounts): 50% Bot1, 50% human 2. Mixed2 (928 accounts): 50% Bot2, 50% human
Spambots detection
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EVALUATION
- C. Yang, R. Harkreader, and G. Gu, “Empirical evaluaLon and new design for
fighLng evolving TwiVer spammers,” IEEE Transac)ons on Informa)on Forensics and Security, vol. 8, no. 8, pp. 1280–1293, 2013
- F. Ahmed, and M. Abulaish, “A generic staLsLcal approach for spam detecLon in online social networks,” Computer Communica)ons, vol.
36, no. 10, pp. 1120–1129, 2013
- Z. Miller, B. Dickinson, W. Deitrick, W. Hu, and A. H. Wang, “TwiVer spammer
detecLon using data stream clustering,” Informa)on Sciences, vol. 260, pp. 64– 73, 2014 Spambots detection
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TAKE-HOME MESSAGES
- New evolutionary wave: social spambots
- Current techniques fail in detecting them
- Detection via digital DNA analysis: effective and efficient (lightweight
features – no graphs – linear complexity algorithms)
Stefano Cresci, Roberto Di Pietro, Marinella Petrocchi, Angelo Spognardi, Maurizio Tesconi: “The Paradigm Shi? of social spambots: Evidence, theories, and tools for the arms race”, WWW 2017 Stefano Cresci, Roberto Di Pietro, Marinella Petrocchi, Angelo Spognardi, Maurizio Tesconi: “Social Fingerprin)ng: Detec)on of spambots groups thorugh DNA inspired behavioral modeling” IEEE TransacLons on Dependable and Secure CompuLng, 2017 Stefano Cresci, Roberto Di Pietro, Marinella Petrocchi, Angelo Spognardi, Maurizio Tesconi: “ExploiLng digital DNA for the analysis of similariLes in TwiVer behaviours” IEEE Data Science and AnalyLcs, 2017
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THANK YOU!
Questions?