The DOI matches its Crossref record; 57 of the 71 references it cites were found in a registry or an index.
Online Human-Bot Interactions: Detection, Estimation, and Characterization
Open accessAbstract
Increasing evidence suggests that a growing amount of social media content is generated by autonomous entities known as social bots. In this work we present a framework to detect such entities on Twitter. We leverage more than a thousand features extracted from public data and meta-data about users: friends, tweet content and sentiment, network patterns, and activity time series. We benchmark the classification framework by using a publicly available dataset of Twitter bots. This training data is enriched by a manually annotated collection of active Twitter users that include both humans and bots of varying sophistication. Our models yield high accuracy and agreement with each other and can detect bots of different nature. Our estimates suggest that between 9% and 15% of active Twitter accounts are bots. Characterizing ties among accounts, we observe that simple bots tend to interact with bots that exhibit more human-like behaviors. Analysis of content flows reveals retweet and mention strategies adopted by bots to interact with different target groups. Using clustering analysis, we characterize several subclasses of accounts, including spammers, self promoters, and accounts that post content from connected applications.
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In the citing paper
“A decade of research on social bot detection 13-15 has established that automated accounts exhibit distinctive temporal, linguistic, and network signatures 14,31 .”
From The Moltbook Illusion: Separating Human Influence from Emergent Behavior in AI Agent Societies (2026) · cites this paper as [14]
In the citing paper
“Various characteristics, ranging from account metadata to social network structure, are then considered during the process (Ferrara et al., 2016;Varol et al., 2017).”
From Anatomy of an AI-powered malicious social botnet (2024) · cites this paper as Varol et al., 2017
In the citing paper
“These features are shown to be effective in identifying social bots and are described in detail in the literature [42, 43, 48].”
From Detection of Novel Social Bots by Ensembles of Specialized Classifiers (2020) · cites this paper as [43]
Citations
Cites
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“We benchmarked our system using several off-the-shelf algorithms provided in the scikit-learn library (Pedregosa et al. 2011).”
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Twitter mood predicts the stock market
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Ferrara, E., Varol, O., Davis, C., Menczer, F., & Flammini, A. (2016) · Communications of the ACM · doi:10.1145/2818717 · Cited by 1,973
Self-citationNorms of valence, arousal, and dominance for 13,915 English lemmas
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Social bots distort the 2016 U.S. Presidential election online discussion
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“Features extracted from user meta-data have been used to classify users and patterns before (Mislove et al. 2011; Ferrara et al. 2016a).”
InfluentialSelf-citationBotOrNot: A System to Evaluate Social Bots
Davis, C. A., Varol, O., Ferrara, E., Flammini, A., & Menczer, F. (2016) · Proceedings of the 25th International Conference Companion on World Wide Web - WWW 16 Companion · doi:10.1145/2872518.2889302 · Cited by 909 · cited 2×
“This system leverages more than one thousand features to evaluate the extent to which a Twitter account exhibits similarity to the known characteristics of social bots (Davis et al. 2016).”
BackgroundMethodSelf-citationIs the Sample Good Enough? Comparing Data from Twitter's Streaming API with Twitter's Firehose
Morstatter, F., Pfeffer, J., Liu, H., & Carley, K. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v7i1.14401 · Cited by 863 · cited 9×
“For each of these accounts, we collected their tweets through the Twitter Search API.”
MethodInfluentialUnderstanding the Demographics of Twitter Users
Mislove, A. E., Lehmann, S., Ahn, Y., Onnela, J.-P. “., & Rosenquist, J. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v5i1.14168 · Cited by 761 · cited 2×
“Features extracted from user meta-data have been used to classify users and patterns before (Mislove et al. 2011; Ferrara et al. 2016a).”
MethodWalking in Facebook: A Case Study of Unbiased Sampling of OSNs
Gjoka, M., Kurant, M., Butts, C. T., & Markopoulou, A. P. (2010) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infcom.2010.5462078 · Cited by 692 · cited 2×
“This approach avoids known biases of other methods such as snowball and breadth-first sampling, which rely on the selection of an initial group of users (Gjoka et al. 2010; Morstatter et al. 2013).”
MethodScience vs Conspiracy: Collective Narratives in the Age of Misinformation
Bessi, A., Coletto, M., Davidescu, G. A., Scala, A., Caldarelli, G., & Quattrociocchi, W. (2015) · PLOS ONE · doi:10.1371/journal.pone.0118093 · Cited by 645 · cited 1×
“…political support (Ratkiewicz et al. 2011), promote terrorist propaganda and recruitment (Berger and Morgan 2015; Abokhodair, Yoo, and Mc-Donald 2015; Ferrara et al. 2016c), manipulate the stock market (Ferrara et al. 2016a), and disseminate rumors and conspiracy theories (Bessi et al. 2015).”
BackgroundDetecting Automation of Twitter Accounts: Are You a Human, Bot, or Cyborg?
Chu, Z., Gianvecchio, S., Wang, H., & Jajodia, S. (2012) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2012.75 · Cited by 634 · cited 2×
“Second, increasing evidence suggests the presence on social media of hybrid human-bot accounts (sometimes referred to as cyborgs) that perform automated actions with some human supervision (Chu et al. 2012; Clark et al. 2016).”
BackgroundDetecting and Tracking Political Abuse in Social Media
Ratkiewicz, J., Conover, M., Meiss, M., Gonçalves, B., Flammini, A., & Menczer, F. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v5i1.14127 · Cited by 576 · cited 4×
“In fact, the usage of network features sig-nificantly helps in tasks like political astroturf detection (Ratkiewicz et al. 2011).”
BackgroundInfluentialSelf-citationMitchell, L., Frank, M. R., Harris, K. D., Dodds, P. S., & Danforth, C. M. (2013) · PLoS ONE · doi:10.1371/journal.pone.0064417 · Cited by 539
Chu, Z., Gianvecchio, S., Wang, H., & Jajodia, S. (2010) · Annual Computer Security Applications Conference · doi:10.1145/1920261.1920265 · Cited by 498 · cited 2×
“Chu et al. built a classification system identifying accounts controlled by humans, bots, and cyborgs (Chu et al. 2010; Chu et al. 2012).”
BackgroundThe DARPA Twitter Bot Challenge
Subrahmanian, V. S., Azaria, A., Durst, S., Kagan, V., Galstyan, A., Lerman, K., Zhu, L., Ferrara, E., Flammini, A., & Menczer, F. (2016) · Computer · doi:10.1109/mc.2016.183 · Cited by 480 · cited 1×
“Wald et al. studied the factors affecting the likelihood of a users being targeted by social bots (Wald et al. 2013).”
BackgroundSelf-citationSeven Months with the Devils: A Long-Term Study of Content Polluters on Twitter
Lee, K., Eoff, B. D., & Caverlee, J. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v5i1.14106 · Cited by 461 · cited 3×
“Lee et al. used a honeypot approach to collect the largest sample of bot accounts available to date (Lee, Eoff, and Caverlee 2011).”
BackgroundMethodBoshmaf, Y., Muslukhov, I., Beznosov, K., & Ripeanu, M. (2011) · Annual Computer Security Applications Conference · doi:10.1145/2076732.2076746 · Cited by 458 · cited 1×
“A growing body of research is addressing social bot activity, its implications on the social network, and the detection of these accounts (Lee, Eoff, and Caverlee 2011; Boshmaf et al. 2011; Beutel et al. 2013; Yang et al. 2014; Ferrara et al. 2016a; Chavoshi, Hamooni, and Mueen 2016).”
BackgroundNo country for old members: user lifecycle and linguistic change in online communities
Danescu-Niculescu-Mizil, C., West, R., Jurafsky, D., Leskovec, J., & Potts, C. (2013) · The Web Conference · doi:10.1145/2488388.2488416 · Cited by 457 · cited 1×
“Many recent papers have demonstrated the importance of content and language features in revealing the nature of social media conversations (Danescu-Niculescu-Mizil et al. 2013; McAuley and Leskovec 2013; Mocanu et al. 2013; Botta, Moat, and Preis 2015; Letchford, Moat, and Preis 2015; Das et al. 2016).”
BackgroundAiding the detection of fake accounts in large scale social online services
Cao, Q., Sirivianos, M., Yang, X., & Pregueiro, T. (2012) · Cited by 449
CopyCatch: stopping group attacks by spotting lockstep behavior in social networks
Beutel, A., Xu, W., Guruswami, V., Palow, C., & Faloutsos, C. (2013) · Proceedings of the 22nd international conference on World Wide Web · doi:10.1145/2488388.2488400 · Cited by 335 · cited 3×
“decomposed event data in time, user, and activity dimensions to extract similar behaviors (Beutel et al. 2013).”
BackgroundThe Twitter of babel: mapping world languages through microblogging platforms
Mocanu, D., Baronchelli, A., Perra, N., Gonçalves, B., Zhang, Q., & Vespignani, A. (2013) · PLoS ONE · doi:10.1371/journal.pone.0061981 · Cited by 293
Uncovering social network Sybils in the wild
Yang, Z., Wilson, C., Wang, X., Gao, T., Zhao, B. Y., & Dai, Y. (2014) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/2556609 · Cited by 290
You are how you click: Clickstream analysis for Sybil detection
Wang, G., Konolige, T., Wilson, C., Wang, X., Zheng, H.-T., & Zhao, B. Y. (2013) · Cited by 256
The ISIS Twitter census: defining and describing the population of ISIS supporters on Twitter
Berger, J. M., & Morgan, J. (2015) · Cited by 240
Quantifying the effect of sentiment on information diffusion in social media
Ferrara, E., & Yang, Z. (2015) · PeerJ Computer Science · doi:10.7717/peerj-cs.26 · Cited by 233 · cited 1×
“Sentiment extracted from social media conversations has been used to forecast offline events including financial market fluctuations (Bollen, Mao, and Zeng 2011), and is known to affect information spreading (Mitchell et al. 2013; Ferrara and Yang 2015).”
Self-citationTweets as impact indicators: Examining the implications of automated “bot” accounts on Twitter
Haustein, S., Bowman, T. D., Holmberg, K., Tsou, A., Sugimoto, C. R., & Larivière, V. (2016) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.23456 · Cited by 231 · cited 2×
“Many social bots perform useful functions, such as dissemination of news and publications (Lokot and Diakopoulos 2016; Haustein et al. 2016) and coordination of volunteer activities (Savage, Monroy-Hernandez, and Höllerer”
BackgroundSocial Turing Tests: Crowdsourcing Sybil DetectionPreprint
Wang, G., Mohanlal, M., Wilson, C., Wang, X., Metzger, M. J., Zheng, H.-T., & Zhao, B. Y. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1205.3856 · Cited by 209 · cited 3×
“analyzed sybil attacks using annotations by experts and crowd-sourcing workers to evaluate consistency and effectiveness of different detection systems (Wang et al. 2013b).”
BackgroundMethodLokot, T. (2016) · doi:10.1080/21670811.2015.1081822 · Cited by 209
The advantage of short paper titles
Letchford, A., Moat, H. S., & Preis, T. (2015) · Royal Society Open Science · doi:10.1098/rsos.150266 · Cited by 184 · cited 1×
“Sentiment analysis is a powerful tool to describe the emotions conveyed by a piece of text, and more broadly the attitude or mood of an entire conversation.”
MethodSoK: The Evolution of Sybil Defense via Social Networks
Alvisi, L., Clement, A., Epasto, A., Lattanzi, S., & Panconesi, A. (2013) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp.2013.33 · Cited by 177 · cited 2×
“surveyed the evolution of sybil defense protocols that leverage the structural properties of the social graph (Alvisi et al. 2013).”
BackgroundDissecting a Social Botnet: Growth, Content and Influence in TwitterPreprint
Abokhodair, N., Yoo, D., & McDonald, D. W. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1604.03627 · Cited by 169
Dissecting a Social Botnet: Growth, Content and Influence in Twitter
Abokhodair, N., Yoo, D., & McDonald, D. W. (2015) · Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing · doi:10.1145/2675133.2675208 · Cited by 162
Predicting Online Extremism, Content Adopters, and Interaction Reciprocity
Ferrara, E., Wang, W.-Q., Varol, O., Flammini, A., & Galstyan, A. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-47874-6_3 · Cited by 149
Self-citationFrom Amateurs to Connoisseurs: Modeling the Evolution of User Expertise through Online ReviewsPreprint
McAuley, J., & Leskovec, J. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1303.4402 · Cited by 143
Quantifying crowd size with mobile phone and Twitter data
Botta, F., Moat, H. S., & Preis, T. (2015) · Royal Society Open Science · doi:10.1098/rsos.150162 · Cited by 129
Positivity of the English Language
Kloumann, I. M., Danforth, C. M., Harris, K. D., Bliss, C. A., & Dodds, P. S. (2012) · PLoS ONE · doi:10.1371/journal.pone.0029484 · Cited by 121
Savage, S., Monroy‐Hernández, A., & Höllerer, T. (2016) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/2818048.2819985 · Cited by 120
Sifting robotic from organic text: A natural language approach for detecting automation on Twitter
Clark, E. M., Williams, J. R., Jones, C. A., Galbraith, R. A., Danforth, C. M., & Dodds, P. S. (2015) · Journal of Computational Science · doi:10.1016/j.jocs.2015.11.002 · Cited by 112 · cited 3×
“Second, increasing evidence suggests the presence on social media of hybrid human-bot accounts (sometimes referred to as cyborgs) that perform automated actions with some human supervision (Chu et al. 2012; Clark et al. 2016).”
BackgroundBotOrNot: A System to Evaluate Social BotsPreprint
Davis, C. A., Varol, O., Ferrara, E., Flammini, A., & Menczer, F. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1602.00975 · Cited by 105 · cited 2×
“This system leverages more than one thousand features to evaluate the extent to which a Twitter account exhibits similarity to the known characteristics of social bots (Davis et al. 2016).”
BackgroundMethodSelf-citationPeople Are Strange When You're a Stranger: Impact and Influence of Bots on Social Networks
Aiello, L. M., Deplano, M., Schifanella, R., & Ruffo, G. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v6i1.14236 · Cited by 97 · cited 1×
“Also known as “sybil” accounts, social bots can pollute on-line discussion by lending false credibility to their messages and influence other users (Ferrara et al. 2016a; Aiello et al. 2012).”
BackgroundIdentifying Correlated Bots in Twitter
Chavoshi, N., Hamooni, H., & Mueen, A. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-47874-6_2 · Cited by 90 · cited 2×
“Prior research suggests that the temporal signature of content production and consumption may reveal important information about online campaigns and their evolution (Ghosh, Surachawala, and Lerman 2011; Ferrara et al. 2016b; Chavoshi, Hamooni, and Mueen 2016).”
BackgroundEntropy-based Classification of 'Retweeting' Activity on TwitterPreprint
Ghosh, R., Surachawala, T., & Lerman, K. (2011) · arXiv (Cornell University) · doi:10.48550/arxiv.1106.0346 · Cited by 84 · cited 1×
“Prior research suggests that the temporal signature of content production and consumption may reveal important information about online campaigns and their evolution (Ghosh, Surachawala, and Lerman 2011; Ferrara et al. 2016b; Chavoshi, Hamooni, and Mueen 2016).”
BackgroundVaporous Marketing: Uncovering Pervasive Electronic Cigarette Advertisements on Twitter
Clark, E. M., Jones, C. A., Williams, J. R., Kurti, A. N., Norotsky, M. C., Danforth, C. M., & Dodds, P. S. (2016) · PLoS ONE · doi:10.1371/journal.pone.0157304 · Cited by 83 · cited 1×
“Recent studies quantify the extent to which automated systems can dominate discussions on Twitter about topics ranging from electronic cigarettes (Clark et al. 2015) to elections (Bessi and Ferrara 2016).”
BackgroundPredicting susceptibility to social bots on Twitter
Wald, R., Khoshgoftaar, T. M., Napolitano, A., & Sumner, C. (2013) · IEEE International Conference on Information Reuse and Integration (IRI) · doi:10.1109/iri.2013.6642447 · Cited by 69 · cited 2×
“Wald et al. studied the factors affecting the likelihood of a users being targeted by social bots (Wald et al. 2013).”
BackgroundDetection of Promoted Social Media Campaigns
Ferrara, E., Varol, O., Menczer, F., & Flammini, A. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v10i1.14772 · Cited by 68 · cited 6×
“Features extracted from user meta-data have been used to classify users and patterns before (Mislove et al. 2011; Ferrara et al. 2016a).”
BackgroundMethodInfluentialSelf-citationPeople are Strange when you're a Stranger: Impact and Influence of Bots on Social NetworksPreprint
Aiello, L. M., Deplano, M., Schifanella, R., et al. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1407.8134 · Cited by 58 · cited 1×
“Also known as “sybil” accounts, social bots can pollute on-line discussion by lending false credibility to their messages and influence other users (Ferrara et al. 2016a; Aiello et al. 2012).”
BackgroundThe Socialbot Network: When Bots Socialize for Fame and Money
Boshmaf, Y., Muslukhov, I., Beznosov, K., & Ripeanu, M. (2012) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.3264605 · Cited by 44
Cues to Deception in Social Media Communications
Briscoe, E., Appling, D. S., & Hayes, H. (2014) · Hawaii International Conference on System Sciences (HICSS) · doi:10.1109/hicss.2014.186 · Cited by 39 · cited 1×
“Sentiment extracted from social media conversations has been used to forecast offline events including financial market fluctuations (Bollen, Mao, and Zeng 2011), and is known to affect information spreading (Mitchell et al. 2013; Ferrara and Yang 2015).”
A categorization scheme for socialbot attacks in online social networksPreprint
Mitter, S., Wagner, C., & Strohmaier, M. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1402.6288 · Cited by 22 · cited 1×
“Social bots also vary greatly in terms of their behavior, intent, and vulnerabilities, as illustrated in a categorization scheme for bot attacks (Mitter, Wagner, and Strohmaier 2013).”
BackgroundDiscovery, Retrieval, and Analysis of 'Star Wars' botnet in TwitterPreprint
Echeverría, J., & Zhou, S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1701.02405 · Cited by 15
Tweets as impact indicators: Examining the implications of automated bot accounts on TwitterPreprint
Haustein, S., Bowman, T. D., Holmberg, K., et al. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1410.4139 · Cited by 14 · cited 2×
“Many social bots perform useful functions, such as dissemination of news and publications (Lokot and Diakopoulos 2016; Haustein et al. 2016) and coordination of volunteer activities (Savage, Monroy-Hernandez, and Höllerer”
BackgroundBotivist: Calling Volunteers to Action Using Online Bots
Savage, S., Monroy-Hernandez, A., & Hollerer, T. (2015) · doi:10.48550/arxiv.1509.06026 · Cited by 14 · cited 1×
“Many social bots perform useful functions, such as dissemination of news and publications (Lokot and Diakopou-los 2016; Haustein et al. 2016) and coordination of volunteer activities (Savage, Monroy-Hernandez, and H¨ollerer 2016).”
BackgroundInformation dissemination in heterogeneous-intent networks
Das, A., Gollapudi, S., Kıcıman, E., & Varol, O. (2016) · ACM Web Science Conference (WebSci) · doi:10.1145/2908131.2908161 · Cited by 12 · cited 3×
“Here we demonstrate that accounts controlled by software exhibit behaviors that reflects their intents and modus operandi (Bakshy et al. 2011; Das et al. 2016), and that such behaviors can be detected by supervised machine learning techniques.”
BackgroundMethodSelf-citationThe `Star Wars' botnet with >350k Twitter bots
Echeverría, J., & Zhou, S. (2017) · Cited by 7
Cited by
Sorted by citations
Fake news on Twitter during the 2016 U.S. presidential election
Grinberg, N., Joseph, K., Friedland, L., Swire-Thompson, B., & Lazer, D. (2019) · Science · doi:10.1126/science.aau2706 · Cited by 1,803
“In order to further guarantee that the sample did not include bots, we identified accounts judged by BorOrNot as bot-like (35).”
BackgroundMethodThe future of social media in marketing
Appel, G., Grewal, L., Hadi, R., & Stephen, A. T. (2020) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-019-00695-1 · Cited by 1,893
“For example, experts estimate that up to 15% of active Twitter accounts are bots (Varol et al.”
BackgroundTucker, J. A., Guess, A., Barbera, P., Vaccari, C., Siegel, A., Sanovich, S., Stukal, D., & Nyhan, B. (2018) · doi:10.2139/ssrn.3144139 · Cited by 1,222
The spread of low-credibility content by social bots
Shao, C., Ciampaglia, G. L., Varol, O., Yang, K.-C., Flammini, A., & Menczer, F. (2018) · Nature Communications · doi:10.1038/s41467-018-06930-7 · Cited by 1,125
Self-citationInfluence of fake news in Twitter during the 2016 US presidential election
Bovet, A., & Makse, H. A. (2019) · CUNY Academic Works (City University of New York) · Cited by 882
Metadata
- Authors
- Varol, O., Ferrara, E., Davis, C., Menczer, F., & Flammini, A.
- Affiliations
- Indiana University (US)
- University of Southern California (US)
- Venue
- Proceedings of the International AAAI Conference on Web and Social Media
- Published
- 2017-05-03
- Volume
- 11
- Issue
- 1
- Pages
- 280-289
- ISSN
- 2162-3449 (ISSN-L)
- 2334-0770
- OpenAlex
- W2595521492
- Semantic Scholar
- 6a025e1e4d7269b20dadc91e14f28a93124fc145
- Cited by
- 981
- References
- 71
- Citations by year
- 2026: 90 · 2025: 81 · 2024: 84 · 2023: 105 · 2022: 96 · 2021: 103 · 2020: 164 · 2019: 126 · 2018: 104 · 2017: 28
- FWCI
- 75.86
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Proceedings of the International AAAI Conference on Web and Social Media (publishedVersion)
- Funders
- National Science Foundation (1101743)
- James S. McDonnell Foundation
- Defense Advanced Research Projects Agency (W911NF-12-1-0037)
- Office of Naval Research (N15A-020-0053)
- Topics
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Keywords
- social bots, bot detection, Twitter, human-bot interaction, botnets, spammer account, self-promotion, user activity patterns, social media
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.
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