Emergent properties, models, and laws of behavioral similarities within groups of twitter users
Abstract
Abstract DNA-inspired online behavioral modeling techniques have been proposed and successfully applied to a broad range of tasks. In this paper, we investigate the fundamental laws that drive the occurrence of behavioral similarities among Twitter users, employing a DNA-inspired technique. Our findings are multifold. First, we demonstrate that, despite apparently featuring little to no similarities, the online behaviors of Twitter users are far from being uniformly random. Secondly, we benchmark different behavioral models through a number of simulations. We characterize the main properties of such models and we identify those models that better resemble human behaviors in Twitter. Then, we demonstrate that the number and the extent of behavioral similarities within a group of Twitter users obey a log-normal law, and we leverage this characterization to propose a novel bot detection system. In a nutshell, the results shed light on the fundamental properties that drive the online behaviors of groups of Twitter users, through the lenses of DNA-inspired behavioral modeling techniques. This study is based on a wealth of data gathered over several months that, for the sake of reproducibility, are publicly available for research purposes.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Human behaviors are intrinsically more heterogeneous than automated ones.
“The rationale behind this approach is based on evidence suggesting that human behaviors are intrinsically more heterogeneous than automated ones [7].”
From A decade of social bot detection (2020) · cites this paper as [7]
Cites
Emergence of Scaling in Random Networks
Barabási, A.-L., & Albert, R. (1999) · Science · doi:10.1126/science.286.5439.509 · Cited by 36,135 · see citing works
The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162 · see citing works
An introduction to ROC analysis
Fawcett, T. (2006) · Pattern Recognition Letters · doi:10.1016/j.patrec.2005.10.010 · Cited by 21,879 · see citing works
An Inquiry into the Nature and Causes of the Wealth of Nations
Smith, A. C. (1776) · Oxford University Press eBooks · doi:10.1093/oseo/instance.00043218 · Cited by 13,953 · see citing works
Fortunato, S. (2010) · Physics Reports · doi:10.1016/j.physrep.2009.11.002 · Cited by 11,293 · see citing works
Power-Law Distributions in Empirical Data
Clauset, A., Shalizi, C. R., & Newman, M. E. J. (2009) · SIAM Review · doi:10.1137/070710111 · Cited by 8,019 · see citing works
Power-law distributions in empirical data
Clauset, A. J., Shalizi, C. R., & Newman, M. E. J. (2018) · Figshare · doi:10.1184/r1/6586835.v1 · Cited by 6,736 · see citing works
Agrawal, R., & Srikant, R. (2002) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde.1995.380415 · Cited by 5,150 · see citing works
Algorithms on strings, trees, and sequences computer science and computational biology
Gusfield, D. (1997) · Cited by 3,490 · see citing works
Log-normal distributions across the sciences: keys and clues
LIMPERT, E., STAHEL, W. A., & ABBT, M. (2001) · BioScience · doi:10.1641/0006-3568(2001)051[0341:lndats]2.0.co;2 · Cited by 2,621 · see citing works
The origin of bursts and heavy tails in human dynamics
Barabási, A.-L. (2005) · Nature · doi:10.1038/nature03459 · Cited by 2,210 · see citing works
Random graph models of social networks
Newman, M. E. J., Watts, D. J., & Strogatz, S. H. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.012582999 · Cited by 1,340 · see citing works
Online Human-Bot Interactions: Detection, Estimation, and Characterization
Varol, O., Ferrara, E., Davis, C., Menczer, F., & Flammini, A. (2017) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v11i1.14871 · Cited by 928 · see citing works
Short text classification in twitter to improve information filtering
Sriram, B., Fuhry, D., Demir, E., Ferhatosmanoğlu, H., & Demirbaş, M. (2010) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/1835449.1835643 · Cited by 734 · see citing works
Social media for large studies of behavior
Ruths, D., & Pfeffer, J. (2014) · Science · doi:10.1126/science.346.6213.1063 · Cited by 648 · see citing works
Community detection in Social Media
Papadopoulos, S., Kompatsiaris, I. Y., Vakali, A. I., & Spyridonos, P. (2011) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-011-0224-z · Cited by 615 · see citing works
Deep neural networks for bot detection
Kudugunta, S., & Ferrara, E. (2018) · Information Sciences · doi:10.1016/j.ins.2018.08.019 · Cited by 506 · see citing works
Fitting Heavy Tailed Distributions: The poweRlaw Package
Gillespie, C. S. (2015) · Journal of Statistical Software · doi:10.18637/jss.v064.i02 · Cited by 483 · see citing works
Fame for sale: Efficient detection of fake Twitter followers
Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., & Tesconi, M. (2015) · Decision Support Systems · doi:10.1016/j.dss.2015.09.003 · Cited by 461 · see citing works
Self-citationA Lognormal Model for Response Times on Test Items
van der Linden, W. J. (2006) · Journal of Educational and Behavioral Statistics · doi:10.3102/10769986031002181 · Cited by 386 · see citing works
Understanding user behavior in online social networks: a survey
Jin, L., Chen, Y., Wang, T., Hui, P., & Vasilakos, A. V. (2013) · IEEE Communications Magazine · doi:10.1109/mcom.2013.6588663 · Cited by 315 · see citing works
Manifestations of user personality in website choice and behaviour on online social networks
Kosiński, M., Bachrach, Y., Kohli, P., Stillwell, D. J., & Graepel, T. (2013) · Machine Learning · doi:10.1007/s10994-013-5415-y · Cited by 267 · see citing works
The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race
Cresci, S., Pietro, R. D., Petrocchi, M., Spognardi, A., & Tesconi, M. (2017) · Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU) · doi:10.48550/arxiv.1701.03017 · Cited by 237 · see citing works
Self-citationOrigins of power-law degree distribution in the heterogeneity of human activity in social networks
Muchnik, L., Pei, S., Parra, L. C., Reis, S. D. S., Andrade, J. S., Havlin, S., & Makse, H. A. (2013) · Scientific Reports · doi:10.1038/srep01783 · Cited by 227 · see citing works
Prediction of Human Activity by Discovering Temporal Sequence Patterns
Li, K., & Fu, Y. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.2297321 · Cited by 212 · see citing works
In-depth behavior understanding and use: The behavior informatics approach
Cao, L. (2010) · Information Sciences · doi:10.1016/j.ins.2010.03.025 · Cited by 209 · see citing works
Random graphs as models of networks
Newman, M. E. J. (2002) · doi:10.1002/3527602755.ch2 · Cited by 197 · see citing works
DNA-Inspired Online Behavioral Modeling and Its Application to Spambot Detection
Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., & Tesconi, M. (2016) · IEEE Intelligent Systems · doi:10.1109/mis.2016.29 · Cited by 194 · see citing works
Self-citationWilson, C., Sala, A., Puttaswamy, K. P. N., & Zhao, B. Y. (2012) · ACM Transactions on the Web · doi:10.1145/2382616.2382620 · Cited by 171 · see citing works
Color Set Size problem with applications to string matching
Chi, L., & Hui, K. (1992) · Lecture notes in computer science · doi:10.1007/3-540-56024-6_19 · Cited by 168 · see citing works
Social fingerprinting: detection of spambot groups through DNA-inspired behavioral modeling
Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., & Tesconi, M. (2017) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2017.2681672 · Cited by 168 · see citing works
Self-citationRTbust: Exploiting Temporal Patterns for Botnet Detection on Twitter
Mazza, M., Cresci, S., Avvenuti, M., Quattrociocchi, W., & Tesconi, M. (2019) · Proceedings of the 10th ACM Conference on Web Science · doi:10.1145/3292522.3326015 · Cited by 163 · see citing works
Self-citationCoupled Behavior Analysis with Applications
Cao, L., Ou, Y., & Yu, P. S. (2011) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2011.129 · Cited by 150 · see citing works
The dynamics of health behavior sentiments on a large online social network
Salathé, M., Vu, D. Q., Khandelwal, S., & Hunter, D. R. (2013) · EPJ Data Science · doi:10.1140/epjds16 · Cited by 116 · see citing works
Cashtag Piggybacking: Uncovering Spam and Bot Activity in Stock Microblogs on Twitter
Cresci, S., Lillo, F., Regoli, D., Tardelli, S., & Tesconi, M. (2019) · ACM Transactions on the Web · doi:10.1145/3313184 · Cited by 105 · see citing works
Self-citationVideo interactions in online video social networks
Benevenuto, F., Rodrigues, T., Almeida, V. A. F., Almeida, J. M., & Ross, K. W. (2009) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/1596990.1596994 · Cited by 84 · see citing works
$FAKE: Evidence of Spam and Bot Activity in Stock Microblogs on Twitter
Cresci, S., Lillo, F., Regoli, D., Tardelli, S., & Tesconi, M. (2018) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v12i1.15073 · Cited by 74 · see citing works
Self-citationWright, F. A., Huang, H., Guan, X., Gamiel, K., Jeffries, C. D., Barry, W. T., de Villena, F. P., Sullivan, P. F., Wilhelmsen, K. C., & Zou, F. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm386 · Cited by 67 · see citing works
A Large-scale Behavioural Analysis of Bots and Humans on Twitter
Gilani, Z., Farahbakhsh, R., Gareth Tyson, A., & Crowcroft, J. (2019) · ACM Transactions on the Web · doi:10.1145/3298789 · Cited by 62 · see citing works
On the capability of evolved spambots to evade detection via genetic engineering
Cresci, S., Petrocchi, M., Spognardi, A., & Tognazzi, S. (2018) · Online Social Networks and Media · doi:10.1016/j.osnem.2018.10.005 · Cited by 53 · see citing works
Self-citationScalable Learning of Collective Behavior
Tang, L., Wang, X., & Liu, H. (2011) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2011.38 · Cited by 51 · see citing works
Structured Learning from Heterogeneous Behavior for Social Identity Linkage
Liu, S., Wang, S., & Zhu, F. (2015) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2015.2397434 · Cited by 50 · see citing works
Near-Optimal Algorithms for Controlling Propagation at Group Scale on Networks
Zhang, Y., Adiga, A., Saha, S. K., Vullikanti, A. K. S., & Prakash, B. A. (2016) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2016.2605088 · Cited by 48 · see citing works
Lognormal Infection Times of Online Information Spread
Doerr, C., Blenn, N., & Mieghem, P. V. (2013) · PLoS ONE · doi:10.1371/journal.pone.0064349 · Cited by 45 · see citing works
Spotting Suspicious Behaviors in Multimodal Data: A General Metric and Algorithms
Jiang, M., Beutel, A., Cui, P., Hooi, B., Yang, S., & Faloutsos, C. (2016) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2016.2555310 · Cited by 45 · see citing works
Strength in Numbers: Robust Tamper Detection in Crowd Computations
Viswanath, B., Bashir, M. A., Zafar, M. B., Bouget, S., Guha, S., Gummadi, K. P., Kate, A., & Mislove, A. (2015) · Proceedings of the 2015 ACM on Conference on Online Social Networks · doi:10.1145/2817946.2817964 · Cited by 43 · see citing works
Avvenuti, M., Bellomo, S., Cresci, S., Polla, M. N. L., & Tesconi, M. (2017) · International World Wide Web Conference (WWW) · doi:10.1145/3041021.3051155 · Cited by 42 · see citing works
Self-citationLognormal distribution in the digg online social network
Mieghem, P. V., Blenn, N., & Doerr, C. (2011) · The European Physical Journal B · doi:10.1140/epjb/e2011-20124-0 · Cited by 40 · see citing works
Linear Time Algorithms for Generalizations of the Longest Common Substring Problem
Arnold, M., & Ohlebusch, E. (2009) · Algorithmica · doi:10.1007/s00453-009-9369-1 · Cited by 33 · see citing works
Caliò, A., Interdonato, R., Pulice, C., et al. (2018) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2018.2820010 · Cited by 33 · see citing works
Long trend dynamics in social media
Wang, C., & Huberman, B. A. (2012) · EPJ Data Science · doi:10.1140/epjds2 · Cited by 32 · see citing works
Model Selection, Transformations and Variance Estimation in Nonlinear Regression
Bunke, O., Droge, B., & Polzehl, J. (1999) · Statistics · doi:10.1080/02331889908802692 · Cited by 30 · see citing works
Cresci, S., Petrocchi, M., Spognardi, A., & Tognazzi, S. (2018) · International World Wide Web Conference (WWW) · doi:10.1145/3184558.3191595 · Cited by 28 · see citing works
Self-citationTowards Optimal Connectivity on Multi-Layered Networks
Chen, C., He, J., Bliss, N. T., & Tong, H. (2017) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2017.2719026 · Cited by 26 · see citing works
(2017) · doi:10.1109/dsaa.2017.57 · Cited by 21 · see citing works
Gender homophily in online book networks
Bucur, D. (2019) · Information Sciences · doi:10.1016/j.ins.2019.01.003 · Cited by 20 · see citing works
Combinatorial Pattern Matching
(1993) · Lecture notes in computer science · doi:10.1007/bfb0029791 · Cited by 18 · see citing works
Topic Models for Unsupervised Cluster Matching
Iwata, T., Hirao, T., & Ueda, N. (2017) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2017.2778720 · Cited by 18 · see citing works
Better Safe Than Sorry: An Adversarial Approach to Improve Social Bot DetectionPreprint
Cresci, S., Petrocchi, M., Spognardi, A., & Tognazzi, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.05132 · Cited by 17 · see citing works
Self-citationA Robust Elicitation Algorithm for Discovering DNA Motifs Using Fuzzy Self-Organizing Maps
Wang, D., & Tapan, S. (2013) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2013.2275733 · Cited by 16 · see citing works
Learning Multiple Factors-Aware Diffusion Models in Social Networks
Chou, C., & Chen⋆, M.-S. (2017) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2017.2786209 · Cited by 16 · see citing works
Social media data analysis for revealing collective behaviors
Zhou, A., Qian, W., & Ma, H. (2012) · ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2339530.2339746 · Cited by 14 · see citing works
The Lognormal Distribution of the Duration of Strikes
Lawrence, R. J. (1984) · Journal of the Royal Statistical Society Series A (General) · doi:10.2307/2981578 · Cited by 11 · see citing works
Jeong, J., & Moon, S. B. (2017) · International World Wide Web Conference (WWW) · doi:10.1145/3041021.3051115 · Cited by 6 · see citing works
Online Behavioral Analysis and Modeling [Guest Editorial]
Cui, P., Liu, H., Aggarwal, C. C., et al. (2016) · IEEE Intelligent Systems · doi:10.1109/mis.2016.15 · Cited by 3 · see citing works
Towards the Modeling of Behavioral Trajectories of Users in Online Social MediaPreprint
Bessi, A. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.05778 · Cited by 1 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
A decade of social bot detection
Cresci, S. (2020) · Communications of the ACM · doi:10.1145/3409116 · Cited by 323 · see citing works
“The rationale behind this approach is based on evidence suggesting that human behaviors are intrinsically more heterogeneous than automated ones [7].”
BackgroundSelf-citation
- Authors
- Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., & Tesconi, M.
- Affiliations
- Institute of Informatics and Telematics (IT)
- IMT School for Advanced Studies Lucca (IT)
- Sapienza University of Rome (IT)
- Corresponding author
- Spognardi, Angelo
- Venue
- Computer Communications
- Publisher
- Elsevier BV
- Published
- 2020-01
- Volume
- 150
- Pages
- 47-61
- ISSN
- 0140-3664 (ISSN-L)
- OpenAlex
- W2982047289
- Semantic Scholar
- 85f75b2bc202ca5bec204cf43add6d8810166a9f
- Cited by
- 40
- References
- 66
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 7 · 2023: 7 · 2022: 9 · 2021: 5 · 2020: 8
- FWCI
- 6.84
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Computer Communications (publishedVersion)
- CINECA IRIS Institutial research information system (University of Pisa) (submittedVersion)
- IRIS Research product catalog (Sapienza University of Rome) (submittedVersion)
- Funders
- Dipartimento di Informatica, Sapienza Università di Roma
- Scuola IMT Alti Studi Lucca
- Ministero dell’Istruzione, dell’Università e della Ricerca
- Topics
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Keywords
- Twitter users, online behavior, behavioral similarity, lognormal distribution, bot detection, behavioral models, social media behavior, emergent properties
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
