Detecting Automation of Twitter Accounts: Are You a Human, Bot, or Cyborg?
Abstract
Twitter is a new web application playing dual roles of online social networking and microblogging. Users communicate with each other by publishing text-based posts. The popularity and open structure of Twitter have attracted a large number of automated programs, known as bots, which appear to be a double-edged sword to Twitter. Legitimate bots generate a large amount of benign tweets delivering news and updating feeds, while malicious bots spread spam or malicious contents. More interestingly, in the middle between human and bot, there has emerged cyborg referred to either bot-assisted human or human-assisted bot. To assist human users in identifying who they are interacting with, this paper focuses on the classification of human, bot, and cyborg accounts on Twitter. We first conduct a set of large-scale measurements with a collection of over 500,000 accounts. We observe the difference among human, bot, and cyborg in terms of tweeting behavior, tweet content, and account properties. Based on the measurement results, we propose a classification system that includes the following four parts: 1) an entropy-based component, 2) a spam detection component, 3) an account properties component, and 4) a decision maker. It uses the combination of features extracted from an unknown user to determine the likelihood of being a human, bot, or cyborg. Our experimental evaluation demonstrates the efficacy of the proposed classification system.
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?
Some users mix manual postings with automated ones, resulting in accounts that blend human with bot behavior-dubbed cyborgs.
“some users mix manual postings with automated ones, resulting in accounts that blend human with bot behavior—dubbed cyborgs”
From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Chu et al., 2012
Facebook, Twitter and Reddit banned tens of thousands of accounts involved in coordinated activities since 2016.
“These results are backed by the same platforms where information operations took place - namely, Facebook 2 , Twitter 3 and Reddit 4 - that banned tens of thousands accounts involved in coordinated activities since 2016.”
From A decade of social bot detection (2020) · cites this paper as [3]
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538 · see citing works
Elements of Information Theory
Cover, T. M., & Thomas, J. A. (2001) · Wiley eBooks · doi:10.1002/0471200611 · Cited by 37,139 · see citing works
The WEKA data mining software: an update
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1656274.1656278 · Cited by 17,750 · see citing works
Elements of Information Theory
Cover, T. M., & Thomas, J. A. (2005) · doi:10.1002/047174882x · Cited by 13,791 · see citing works
I.—COMPUTING MACHINERY AND INTELLIGENCE
TURING, A. M. (1950) · Mind · doi:10.1093/mind/lix.236.433 · Cited by 10,290 · see citing works
Machine learning in automated text categorization
Sebastiani, F. (2002) · ACM Computing Surveys · doi:10.1145/505282.505283 · Cited by 7,927 · see citing works
The random subspace method for constructing decision forests
Ho, T. K. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.709601 · Cited by 6,874 · see citing works
What is Twitter, a social network or a news media?
Kwak, H., Lee, C., Park, H., & Moon, S. (2010) · Proceedings of the 19th international conference on World wide web · doi:10.1145/1772690.1772751 · Cited by 6,652 · see citing works
Computing Machinery and Intelligence (1950)
Turing, A. M. (2004) · doi:10.1093/oso/9780198250791.003.0017 · Cited by 6,120 · see citing works
Wiley Series in Probability and Statistics
(2008) · Wiley series in probability and statistics · doi:10.1002/series1345 · Cited by 4,270 · see citing works
(2024) · Physics Subject Headings (PhySH) · doi:10.29172/7c2a6982-6d72-4cd8-bba6-2fccb06a7011 · Cited by 4,081 · see citing works
Measurement and analysis of online social networks
Mislove, A., Marcon, M., Gummadi, K. P., Druschel, P., & Bhattacharjee, B. (2007) · Proceedings of the 7th ACM SIGCOMM conference on Internet measurement · doi:10.1145/1298306.1298311 · Cited by 3,057 · see citing works
Java, A., Song, X., Finin, T., & Tseng, B. L. (2007) · doi:10.1145/1348549.1348556 · Cited by 2,662 · see citing works
Twitter power: Tweets as electronic word of mouth
Jansen, B. J. J., Zhang, M., Sobel, K., & Chowdury, A. (2009) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.21149 · Cited by 1,664 · see citing works
I tube, you tube, everybody tubes
Cha, M., Kwak, H., Rodríguez, P., Ahn, Y., & Moon, S. B. (2007) · ACM Internet Measurement Conference (IMC) · doi:10.1145/1298306.1298309 · Cited by 1,571 · see citing works
Twitter adoption and use in mass convergence and emergency events
Hughes, A., & Palen, L. (2009) · International Journal of Emergency Management · doi:10.1504/ijem.2009.031564 · Cited by 1,002 · see citing works
Who says what to whom on twitter
Wu, S., Hofman, J. M., Mason, W., & Watts, D. J. (2011) · International World Wide Web Conference (WWW) · doi:10.1145/1963405.1963504 · Cited by 969 · see citing works
Zhao, D., & Rosson, M. B. (2009) · International ACM SIGGROUP Conference on Supporting Group Work (GROUP) · doi:10.1145/1531674.1531710 · Cited by 870 · see citing works
Analyzing Microarray Gene Expression Data
McLachlan, G. J., Do, K., & Ambroise, C. (2004) · Wiley series in probability and statistics · doi:10.1002/047172842x · Cited by 823 · see citing works
A measurement-driven analysis of information propagation in the flickr social network
Cha, M., Mislove, A. E., & Gummadi, K. P. (2009) · International World Wide Web Conference (WWW) · doi:10.1145/1526709.1526806 · Cited by 772 · see citing works
Krishnamurthy, B., Gill, P., & Arlitt, M. (2008) · Proceedings of the first workshop on Online social networks · doi:10.1145/1397735.1397741 · Cited by 697 · see citing works
Walking 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 679 · see citing works
Stone-Gross, B., Cova, M., Cavallaro, L., Gilbert, B., Szydlowski, M., Kemmerer, R. A., Kruegel, C., & Vigna, G. (2009) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/1653662.1653738 · Cited by 577 · see citing works
Grier, C., Thomas, K., Paxson, V., & Zhang, M. (2010) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/1866307.1866311 · Cited by 558 · see citing works
Starbird, K., Palen, L. A., Hughes, A., & Vieweg, S. E. (2010) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/1718918.1718965 · Cited by 439 · see citing works
Suspended accounts in retrospect: an analysis of twitter spam
Thomas, K., Grier, C., Song, D., & Paxson, V. (2011) · Proceedings of the 2011 ACM SIGCOMM conference on Internet measurement conference · doi:10.1145/2068816.2068840 · Cited by 439 · see citing works
Detecting spam in a Twitter network
Schoenebeck, S. Y., Romero, D. M., Schoenebeck, G. R., & boyd, D. (2009) · First Monday · doi:10.5210/fm.v15i1.2793 · Cited by 347 · see citing works
Characterizing residential broadband networks
Dischinger, M., Haeberlen, A., Gummadi, K. P., & Saroiu, S. (2007) · ACM Internet Measurement Conference (IMC) · doi:10.1145/1298306.1298313 · Cited by 341 · see citing works
Local area network characteristics, with implications for broadband network congestion management
Fowler, H. J., & Leland, W. E. (1991) · IEEE Journal on Selected Areas in Communications · doi:10.1109/49.103559 · Cited by 308 · see citing works
Measuring regularity by means of a corrected conditional entropy in sympathetic outflow
Porta, A., Baselli, G., Liberati, D., Montano, N., Cogliati, C. B., Gnecchi‐Ruscone, T., Malliani, A., & Cerutti, S. (1998) · Biological Cybernetics · doi:10.1007/s004220050414 · Cited by 289 · see citing works
Henzinger, M. R., Heydon, A., Mitzenmacher, M., & Najork, M. (2000) · Computer Networks · doi:10.1016/s1389-1286(00)00055-4 · Cited by 269 · see citing works
Detecting covert timing channels
Gianvecchio, S., & Wang, H. (2007) · Proceedings of the ACM Conference on Computer and Communications Security · doi:10.1145/1315245.1315284 · Cited by 180 · see citing works
Self-citationHuberman, B. A., & Hogg, T. (1986) · Physica D Nonlinear Phenomena · doi:10.1016/0167-2789(86)90308-1 · Cited by 175 · see citing works
An Entropy-Based Approach to Detecting Covert Timing Channels
Gianvecchio, S., & Wang, H. (2010) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2010.46 · Cited by 142 · see citing works
Self-citationData mining tasks and methods: Classification: decision-tree discovery
Kohavi, R., & Quinlan, J. R. (2002) · Oxford University Press eBooks · Cited by 108 · see citing works
Measurement and classification of humans and bots in internet chat
Gianvecchio, S., Xie, M., Wu, Z., & Wang, H. (2008) · Cited by 89 · see citing works
Self-citationHandbook of Data Mining and Knowledge Discovery
(2004) · Kybernetes · doi:10.1108/03684920410534524 · Cited by 89 · see citing works
Analysis of Twitter Lists as a Potential Source for Discovering Latent Characteristics of Users
Kim, D., Jo, Y., Moon, I., & Oh, A. H. (2010) · Cited by 89 · see citing works
Ending Spam: Bayesian Content Filtering and the Art of Statistical Language Classification
Zdziarski, J. (2005) · Cited by 84 · see citing works
Gianvecchio, S., Wu, Z., Xie, M., & Wang, H. (2009) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/1653662.1653694 · Cited by 78 · see citing works
Self-citationAn effective defense against email spam laundering
Xie, M., Yin, H., & Wang, H. (2006) · Proceedings of the ACM Conference on Computer and Communications Security · doi:10.1145/1180405.1180428 · Cited by 41 · see citing works
Self-citationBot, Cyborg and Automated Turing Test
Yan, J. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-04904-0_26 · Cited by 40 · see citing works
HoneyIM: Fast Detection and Suppression of Instant Messaging Malware in Enterprise-Like Networks
Xie, M., Wu, Z., & Wang, H. (2007) · doi:10.1109/acsac.2007.24 · Cited by 30 · see citing works
Self-citationTraffic Shaping of Spam Botnets
Husna, H., Phithakkitnukoon, S., & Dantu, R. (2008) · IEEE Consumer Communications and Networking Conference · doi:10.1109/ccnc08.2007.181 · Cited by 12 · see citing works
Bot, Cyborg and Automated Turing Test
Yan, J. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-04904-0_27 · Cited by 12 · see citing works
Flagged first, then by citations
1 of the 46 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Fake News Detection on Social Media: A Data Mining Perspective
Shu, K., Sliva, A., Wang, S., Tang, J., & Liu, H. (2017) · ACM SIGKDD Explorations Newsletter · doi:10.1145/3137597.3137600 · Cited by 3,304 · see citing works
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 · see citing works
In AI we trust? Perceptions about automated decision-making by artificial intelligence
Araujo, T., Helberger, N., Kruikemeier, S., & de Vreese, C. H. (2020) · AI & SOCIETY · doi:10.1007/s00146-019-00931-w · Cited by 1,038 · see citing works
An overview of online fake news: Characterization, detection, and discussion
Zhang, X., & Ghorbani, A. A. (2020) · Information Processing & Management · doi:10.1016/j.ipm.2019.03.004 · Cited by 997 · 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
“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).”
BackgroundSocial Data: Biases, Methodological Pitfalls, and Ethical Boundaries
Olteanu, A., Castillo, C., Diaz, F., & Kıcıman, E. (2019) · Frontiers in Big Data · doi:10.3389/fdata.2019.00013 · Cited by 763 · see citing works
“In fact, some users mix manual postings with automated ones, resulting in accounts that blend human with bot behavior—dubbed cyborgs (Chu et al., 2012).”
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 · see citing works
Li, L., Zhang, Q., Wang, X., Zhang, J. J., Wang, T., Gao, T., Duan, W., Tsoi, K. K., & Wang, F. (2020) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2020.2980007 · Cited by 484 · see citing works
The 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 · 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
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
How Does Social Media Impact Bitcoin Value? A Test of the Silent Majority Hypothesis
Mai, F., Shan, Z., Bai, Q., Wang, X., & Chiang, R. H. L. (2018) · Journal of Management Information Systems · doi:10.1080/07421222.2018.1440774 · Cited by 390 · see citing works
Howard, P. N., Woolley, S., & Calo, R. (2018) · Journal of Information Technology & Politics · doi:10.1080/19331681.2018.1448735 · Cited by 337 · see citing works
Fine-tuned Language Models for Text Classification.Preprint
Howard, J., & Ruder, S. (2018) · arXiv (Cornell University) · Cited by 357 · see citing works
Political Astroturfing on Twitter: How to Coordinate a Disinformation Campaign
Keller, F. B., Schoch, D., Stier, S., & Yang, J. (2020) · Political Communication · doi:10.1080/10584609.2019.1661888 · Cited by 378 · see citing works
A decade of social bot detection
Cresci, S. (2020) · Communications of the ACM · doi:10.1145/3409116 · Cited by 323 · see citing works
“ounts that are simultaneously driven by algorithms and by human intervention led to even more fine-grained definitions and cyborgs were introduced as either bot-assisted humans or human-assisted bots [3]. Instead, social scientists are typically more interested in the social or political implications of the use of bot…”
BackgroundUsing sentiment to detect bots on Twitter: Are humans more opinionated than bots?
Dickerson, J. P., Kagan, V., & Subrahmanian, V. S. (2014) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1109/asonam.2014.6921650 · Cited by 278 · see citing works
A survey of Twitter research: Data model, graph structure, sentiment analysis and attacks
Antonakaki, D., Fragopoulou, P., & Ioannidis, S. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.114006 · Cited by 279 · see citing works
DeBot: Twitter Bot Detection via Warped Correlation
Chavoshi, N., Hamooni, H., & Mueen, A. (2016) · 2016 IEEE 16th International Conference on Data Mining (ICDM) · doi:10.1109/icdm.2016.0096 · Cited by 260 · see citing works
An Investigation of Brand-Related User-Generated Content on Twitter
Liu, X., Burns, A. C., & Hou, Y. (2017) · Journal of Advertising · doi:10.1080/00913367.2017.1297273 · Cited by 244 · see citing works
CREDBANK: A Large-Scale Social Media Corpus With Associated Credibility Annotations
Mitra, T., & Gilbert, É. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v9i1.14625 · Cited by 236 · see citing works
Detection of Bots in Social Media: A Systematic Review
Orabi, M., Mouheb, D., Aghbari, Z. A., & Kamel, I. (2020) · Information Processing & Management · doi:10.1016/j.ipm.2020.102250 · Cited by 232 · see citing works
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 · see citing works
Bots and Misinformation Spread on Social Media: Implications for COVID-19
Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M. M., Ungar, L., Schwartz, H. A., Epstein, D. H., Leggio, L., & Curtis, B. L. (2021) · Journal of Medical Internet Research · doi:10.2196/26933 · Cited by 238 · see citing works
Fake News: A Survey of Research, Detection Methods, and Opportunities.Preprint
Zhou, X., & Zafarani, R. (2018) · arXiv (Cornell University) · Cited by 212 · see citing works
TwiBot-20: A Comprehensive Twitter Bot Detection Benchmark
Feng, S., Wan, H., Wang, N., Li, J., & Luo, M. (2021) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3459637.3482019 · Cited by 199 · see citing works
Roß, B., Pilz, L., Cabrera, B., Brachten, F., Neubaum, G., & Stieglitz, S. (2019) · European Journal of Information Systems · doi:10.1080/0960085x.2018.1560920 · Cited by 197 · see citing works
A new direction in social network analysis: Online social network analysis problems and applications
Can, Ü., & Alataş, B. (2019) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2019.122372 · Cited by 193 · see citing works
Analysis of political discourse on twitter in the context of the 2016 US presidential elections
Yaqub, U., Chun, S. A., Atluri, V., & Vaidya, J. S. (2017) · Government Information Quarterly · doi:10.1016/j.giq.2017.11.001 · Cited by 192 · see citing works
Detecting malicious tweets in trending topics using a statistical analysis of language
Martínez-Romo, J., & Araujo, L. (2012) · Expert Systems with Applications · doi:10.1016/j.eswa.2012.12.015 · Cited by 185 · see citing works
#FluxFlow: Visual Analysis of Anomalous Information Spreading on Social Media
Zhao, J., Cao, N., Wen, Z., Song, Y., Lin, Y., & Collins, C. M. (2014) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2014.2346922 · Cited by 178 · see citing works
Twitter spam detection: Survey of new approaches and comparative study
Wu, T., Wen, S., Xiang, Y., & Zhou, W. (2017) · Computers & Security · doi:10.1016/j.cose.2017.11.013 · Cited by 174 · 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
Social media, sentiment and public opinions: Evidence from #Brexit and #USElection
Gorodnichenko, Y., Pham, T., & Talavera, O. (2021) · European Economic Review · doi:10.1016/j.euroecorev.2021.103772 · Cited by 172 · see citing works
Malicious accounts: Dark of the social networks
Adewole, K. S., Anuar, N. B., Kamsin, A., Varathan, K. D., & Razak, S. A. (2016) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2016.11.030 · Cited by 163 · see citing works
A Survey on Computational Propaganda Detection
Martino, G. D. S., Cresci, S., Barrón‐Cedeño, A., Yu, S., Pietro, R. D., & Nakov, P. (2020) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2020/672 · Cited by 150 · see citing works
Detecting Bots on Russian Political Twitter
Stukal, D., Sanovich, S., Bonneau, R., & Tucker, J. A. (2017) · Big Data · doi:10.1089/big.2017.0038 · Cited by 140 · see citing works
Hajli, N., Saeed, U., Tajvidi, M., & Shirazi, F. (2021) · British Journal of Management · doi:10.1111/1467-8551.12554 · Cited by 145 · see citing works
Reverse Engineering Socialbot Infiltration Strategies in Twitter
Freitas, C. A., Benevenuto, F., Ghosh, S., & Veloso, A. (2015) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1145/2808797.2809292 · Cited by 138 · see citing works
Combating disinformation in a social media age
Shu, K., Bhattacharjee, A., Alatawi, F., Nazer, T. H., Ding, K., Karami, M., & Liu, H. (2020) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1385 · Cited by 198 · see citing works
The unbearable emptiness of tweeting—About journal articles
Robinson‐García, N., Costas, R., Isett, K. R., Melkers, J. E., & Hicks, D. M. (2017) · PLoS ONE · doi:10.1371/journal.pone.0183551 · Cited by 130 · see citing works
A Needle in a Haystack: What Do Twitter Users Say about Software?
Guzmán, E., Alkadhi, R., & Seyff, N. (2016) · IEEE International Requirements Engineering Conference (RE) · doi:10.1109/re.2016.67 · Cited by 128 · see citing works
Liu, X. (2020) · Industrial Marketing Management · doi:10.1016/j.indmarman.2019.02.021 · Cited by 140 · see citing works
Dasilva, J. P., Ayerdi, K. M., & Galdospín, T. M. (2020) · El Profesional de la Informacion · doi:10.3145/epi.2020.may.08 · Cited by 122 · see citing works
A one-class classification approach for bot detection on Twitter
Rodríguez‐Ruiz, J., Mata-Sánchez, J. I., Monroy, R., Loyola‐González, O., & López‐Cuevas, A. (2020) · Computers & Security · doi:10.1016/j.cose.2020.101715 · Cited by 121 · see citing works
Sanovich, S., Стукал, Д., & Tucker, J. A. (2018) · Comparative Politics · doi:10.5129/001041518822704890 · Cited by 119 · see citing works
Social Media, Sentiment and Public Opinions: Evidence from #Brexit and #USElection
Gorodnichenko, Y., Pham, T., & Talavera, O. (2018) · National Bureau of Economic Research · doi:10.3386/w24631 · Cited by 115 · see citing works
Using big data analytics to study brand authenticity sentiments: The case of Starbucks on Twitter
Shirdastian, H., Laroche, M., & Richard, M. (2017) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2017.09.007 · Cited by 119 · see citing works
Disinformation, Misinformation, and Fake News in Social Media
Cao, J., Qi, P., Sheng, Q., Yang, T., Guo, J., & Li, J. (2020) · Lecture notes in social networks · doi:10.1007/978-3-030-42699-6 · Cited by 120 · see citing works
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 109 · see citing works
Twitter turing test: Identifying social machines
Alarifi, A., Alsaleh, M., & Al‐Salman, A. S. (2016) · Information Sciences · doi:10.1016/j.ins.2016.08.036 · Cited by 106 · see citing works
Aswani, R., Kar, A. K., & Ilavarasan, P. V. (2017) · Information Systems Frontiers · doi:10.1007/s10796-017-9805-8 · Cited by 102 · see citing works
Detecting Social Bots on Twitter: A Literature Review
Alothali, E., Zaki, N., Mohamed, E. A., & Alashwal, H. (2018) · 2018 International Conference on Innovations in Information Technology (IIT) · doi:10.1109/innovations.2018.8605995 · Cited by 102 · see citing works
Uyheng, J., & Carley, K. M. (2020) · Journal of Computational Social Science · doi:10.1007/s42001-020-00087-4 · Cited by 96 · see citing works
Twitter spam account detection based on clustering and classification methods
Adewole, K. S., Han, T., Wu, W., Song, H. H., & Sangaiah, A. K. (2018) · The Journal of Supercomputing · doi:10.1007/s11227-018-2641-x · Cited by 95 · see citing works
A novel framework for detecting social bots with deep neural networks and active learning
Wu, Y., Fang, Y., Shang, S., Jin, J., Wei, L., & Wang, H. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.106525 · Cited by 95 · see citing works
Self-citationDetection of malicious social bots: A survey and a refined taxonomy
Latah, M. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.113383 · Cited by 94 · see citing works
Early detection of cyberbullying on social media networks
López-Vizcaíno, M. F., Novoa, F. J., Carneiro, V., & Cacheda, F. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2021.01.006 · Cited by 100 · see citing works
An enhanced graph-based semi-supervised learning algorithm to detect fake users on Twitter
Muthu, B., Karthikeyan, N., Karthik, S., Varatharajan, R., Manogaran, G., & Sivaparthipan, C. B. (2019) · The Journal of Supercomputing · doi:10.1007/s11227-019-02948-w · Cited by 92 · see citing works
Fake news outbreak 2021: Can we stop the viral spread?
Khan, T., Michalas, A., & Akhunzada, A. (2021) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2021.103112 · Cited by 91 · see citing works
Bot, or not? Comparing three methods for detecting social bots in five political discourses
Martini, F., Samula, P., Keller, T. R., & Klinger, U. (2021) · Big Data & Society · doi:10.1177/20539517211033566 · Cited by 91 · see citing works
Securing Big Data Provenance for Auditors: The Big Data Provenance Black Box as Reliable Evidence
Appelbaum, D. A. (2016) · Journal of Emerging Technologies in Accounting · doi:10.2308/jeta-51473 · Cited by 87 · see citing works
Curiel, C. P., & Molpeceres, A. M. V. (2020) · Revista Latina de Comunicación Social · doi:10.4185/rlcs-2020-1469 · Cited by 85 · see citing works
The Measure of All Minds: Evaluating Natural and Artificial Intelligence
Hernández‐Orallo, J. (2017) · Cited by 88 · see citing works
Desai, A. N., Ruidera, D., Steinbrink, J. M., Granwehr, B. P., & Lee, D. H. (2022) · Clinical Infectious Diseases · doi:10.1093/cid/ciac109 · Cited by 89 · see citing works
A new sociology of humans and machines
Tsvetkova, M., Yasseri, T., Pescetelli, N., & Werner, T. (2024) · Nature Human Behaviour · doi:10.1038/s41562-024-02001-8 · Cited by 98 · see citing works
Supervised Machine Learning Bot Detection Techniques to Identify Social Twitter Bots
Efthimion, P. G., Payne, S., & Proferes, N. (2018) · SMU Scholar (Southern Methodist University) · Cited by 81 · see citing works
Algarni, A., Xu, Y., & Chan, T. (2017) · European Journal of Information Systems · doi:10.1057/s41303-017-0057-y · Cited by 82 · see citing works
Contrast Pattern-Based Classification for Bot Detection on Twitter
Loyola‐González, O., Monroy, R., Rodríguez‐Ruiz, J., López‐Cuevas, A., & Mata-Sánchez, J. I. (2019) · IEEE Access · doi:10.1109/access.2019.2904220 · Cited by 80 · see citing works
A hybrid approach for spam detection for Twitter
Malik, M., Iqbal, M. A., Aleem, M., & Islam, M. A. (2017) · International Bhurban Conference on Applied Sciences and Technology (IBCAST) · doi:10.1109/ibcast.2017.7868095 · Cited by 79 · see citing works
Detecting Fake News on Social Media
Shu, K., & Liu, H. (2019) · Synthesis lectures on data mining and knowledge discovery · doi:10.1007/978-3-031-01915-9 · Cited by 78 · see citing works
A Deep Learning Approach for Robust Detection of Bots in Twitter Using Transformers
Martin-Gutierrez, D., Hernández-Peñaloza, G., Belmonte-Hernández, A., Lozano-Díez, A., & Álvarez, F. (2021) · IEEE Access · doi:10.1109/access.2021.3068659 · Cited by 79 · see citing works
Preprocessing framework for Twitter bot detection
Kantepe, M., & Ganiz, M. C. (2017) · doi:10.1109/ubmk.2017.8093483 · Cited by 76 · see citing works
Vaporous 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 75 · see citing works
Bodaghi, A., & Oliveira, J. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.116110 · Cited by 75 · see citing works
Lingam, G., Rout, R. R., & Somayajulu, D. V. L. N. (2019) · Applied Intelligence · doi:10.1007/s10489-019-01488-3 · Cited by 73 · see citing works
Detecting Malicious Social Bots Based on Clickstream Sequences
Shi, P., Zhang, Z., & Choo, K.-K. R. (2019) · IEEE Access · doi:10.1109/access.2019.2901864 · Cited by 72 · see citing works
Alshaabi, T., Dewhurst, D. R., Minot, J. R., Arnold, M. V., Adams, J. L., Danforth, C. M., & Dodds, P. S. (2021) · EPJ Data Science · doi:10.1140/epjds/s13688-021-00271-0 · Cited by 76 · see citing works
A big data approach to examining social bots on Twitter
Liu, X. (2019) · Journal of Services Marketing · doi:10.1108/jsm-02-2018-0049 · Cited by 73 · see citing works
An exploratory study of Twitter messages about software applications
Guzmán, E., Alkadhi, R., & Seyff, N. (2017) · Requirements Engineering · doi:10.1007/s00766-017-0274-x · Cited by 69 · see citing works
Predicting 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 68 · see citing works
The dark side of social networking sites:Understanding phishing risks
Silić, M., & Bäck, A. (2016) · Computers in Human Behavior · doi:10.1016/j.chb.2016.02.050 · Cited by 68 · see citing works
Detection of Malicious Social Bots Using Learning Automata With URL Features in Twitter Network
Rout, R. R., Lingam, G., & Somayajulu, D. V. L. N. (2020) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2020.2992223 · Cited by 64 · see citing works
The anatomy of ‘fake news’: Studying false messages as digital objects
Khan, A., Brohman, M. K., & Addas, S. (2021) · Journal of Information Technology · doi:10.1177/02683962211037693 · Cited by 67 · see citing works
Complex Network and Source Inspired COVID-19 Fake News Classification on Twitter
Qureshi, K. A., Malick, R. A. S., Sabih, M., & Cherifi, H. (2021) · IEEE Access · doi:10.1109/access.2021.3119404 · Cited by 62 · see citing works
Martinez, L. S., Hughes, S., Buhi, E. R., & Tsou, M. (2018) · Journal of Health Communication · doi:10.1080/10810730.2018.1493057 · Cited by 60 · 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
A framework for detecting unfolding emergencies using humans as sensors
Avvenuti, M., Cimino, M. G. C. A., Cresci, S., Marchetti, A., & Tesconi, M. (2016) · SpringerPlus · doi:10.1186/s40064-016-1674-y · Cited by 62 · see citing works
Ansar, W., & Goswami, S. (2021) · International Journal of Information Management Data Insights · doi:10.1016/j.jjimei.2021.100052 · Cited by 65 · see citing works
Detecting computer-generated disinformation
Stiff, H., & Johansson, F. (2022) · International Journal of Data Science and Analytics · doi:10.1007/s41060-021-00299-5 · Cited by 79 · see citing works
Social media bot detection with deep learning methods: a systematic review
Hayawi, K., Saha, S., Masud, M. M., Mathew, S. S., & Kaosar, M. G. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-08352-z · Cited by 61 · see citing works
Detecting Non‐personal and Spam Users on Geo‐tagged Twitter Network
Guo, D., & Chen, C. (2014) · Transactions in GIS · doi:10.1111/tgis.12101 · Cited by 60 · see citing works
Minnich, A., Chavoshi, N., Koutra, D., & Mueen, A. (2017) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1145/3110025.3110163 · Cited by 61 · see citing works
Political Astroturfing on Twitter: How to Coordinate a Disinformation CampaignPreprint
Keller, F., Schoch, D., Stier, S., & Yang, J. (2019) · doi:10.31235/osf.io/a5gk6 · Cited by 60 · see citing works
TSD: Detecting Sybil Accounts in Twitter
Alsaleh, M., Alarifi, A., Al‐Salman, A. S., Alfayez, M., & Almuhaysin, A. (2014) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2014.81 · Cited by 58 · see citing works
Keller, F., Schoch, D., Stier, S., & Yang, J. (2017) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v11i1.14941 · Cited by 58 · see citing works
For Whom the Bell Trolls: Shifting Troll Behaviour in the Twitter Brexit Debate
Llewellyn, C., Cram, L., Hill, R. L., & Favero, A. (2019) · JCMS Journal of Common Market Studies · doi:10.1111/jcms.12882 · Cited by 56 · see citing works
Al-Zoubi, A. M., Alqatawna, J., Faris, H., & Hassonah, M. A. (2019) · Journal of Information Science · doi:10.1177/0165551519861599 · Cited by 56 · see citing works
Combating the evasion mechanisms of social bots
Ji, Y., He, Y., Jiang, X., Cao, J., & Li, Q. (2016) · Computers & Security · doi:10.1016/j.cose.2016.01.007 · Cited by 56 · see citing works
Стукал, Д., Sanovich, S., Tucker, J. A., & Bonneau, R. (2019) · SAGE Open · doi:10.1177/2158244019827715 · Cited by 55 · see citing works
- Authors
- Chu, Z., Gianvecchio, S., Wang, H., & Jajodia, S.
- Affiliations
- Twitter (United States) (US)
- Mitre (United States) (US)
- William & Mary (US)
- George Mason University (US)
- Venue
- IEEE Transactions on Dependable and Secure Computing
- Published
- 2012-11
- Volume
- 9
- Issue
- 6
- Pages
- 811-824
- ISSN
- 1545-5971 (ISSN-L)
- OpenAlex
- W2072715695
- Cited by
- 634
- References
- 46
- Citations by year
- 2026: 16 · 2025: 26 · 2024: 31 · 2023: 46 · 2022: 52 · 2021: 70 · 2020: 88 · 2019: 85 · 2018: 73 · 2017: 56 · 2016: 39 · 2015: 32 · 2014: 14 · 2013: 5 · 2012: 1
- FWCI
- 45.14
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Dependable and Secure Computing (publishedVersion)
- Funders
- National Science Foundation
- University of Oregon
- Multidisciplinary University Research Initiative
- Air Force Office of Scientific Research
- Army Research Office
- 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 › Information Systems › User Authentication and Security Systems
- Keywords
- Twitter bots, bot detection, human vs bot classification, social media automation, spam detection, entropy features, microblogging
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
