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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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“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]
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Self-citationNorms of valence, arousal, and dominance for 13,915 English lemmas
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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.”
MethodInfluential
Cited by
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Items 301–400 of 933
An Evolutionary Computation Approach for Twitter Bot Detection
Rovito, L., Bonin, L., Manzoni, L., & Lorenzo, A. D. (2022) · Applied Sciences · doi:10.3390/app12125915 · Cited by 18
Investigating the Validity of Botometer-Based Social Bot Studies
Gallwitz, F., & Kreil, M. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-18253-2_5 · Cited by 17
Marketing campaign targeting using bridge extraction in multiplex social network
Vikatos, P., Gryllos, P., & Makris, C. H. (2019) · Artificial Intelligence Review · doi:10.1007/s10462-018-9675-6 · Cited by 17
“Current studies Ferrara et al. (2016), Varol et al. (2017) declare the rise of social bots in the Twitter environment.”
BackgroundTechnology-Enabled Disinformation: Summary, Lessons, and RecommendationsPreprint
Akers, J. G., Bansal, G., Cadamuro, G., Chen, C., Chen, Q., Lin, L., Mulcaire, P., Nandakumar, R., Rockett, M., Simko, L., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1812.09383 · Cited by 39
Detection of Rumors and Their Sources in Social Networks: A Comprehensive Survey
Sattarov, O., & Choi, J. (2024) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2024.3522801 · Cited by 18
Bow-tie structures of twitter discursive communities
Mattei, M., Pratelli, M., Caldarelli, G., Petrocchi, M., & Saracco, F. (2022) · Scientific Reports · doi:10.1038/s41598-022-16603-7 · Cited by 24
A Temporal Topic Model for Noisy Mediums
Churchill, R., Singh, L., & Kirov, C. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-93037-4_4 · Cited by 16
Identification of credulous users on Twitter
Balestrucci, A., Nicola, R. D., Inverso, O., & Trubiani, C. (2019) · ACM Symposium on Applied Computing · doi:10.1145/3297280.3297486 · Cited by 16
“This is built upon an existing bot detector and a dataset of Twitter users [41], where each user is associated to a label indicatingwhether it is a genuine one (in otherwords, a human”
BackgroundMethodInfluentialMarzouki, Y., Barach, E., Srinivasan, V., Shaikh, S., & Feldman, L. B. (2020) · Heliyon · doi:10.1016/j.heliyon.2020.e04311 · Cited by 17
Helping Users Tackle Algorithmic Threats on Social Media: A Multimedia Research Agenda
von der Weth, C., Abdul, A., Fan, S., & Kankanhalli, M. (2020) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3394171.3414692 · Cited by 17
“he bots to capture large audiences, making it easier to spread fake news. Mimicking humanlike behavior may include varying sharing frequency and schedule, or periodically updating profile information [67, 195]. Apart from better “blending in”, sophisticated bots also coordinate attacks through the synchronization of w…”
BackgroundLa COVID-19 en el periodismo: un impacto ambivalente
de Castelló, U. J. I., & Casero-Ripollés, A. (2020) · Revista de la Asociación Española de Investigación de la Comunicación · doi:10.24137/raeic.7.14.1 · Cited by 16
Keijzer, M. A., & Mäs, M. (2020) · Online Social Networks and Media · doi:10.1016/j.osnem.2020.100106 · Cited by 20
“State-of-the-art bot detection algorithms claim to achieve impressive detection rates around 90 percent [e.g.2,20].”
BackgroundModeling interurban mentioning relationships in the U.S. Twitter network using geo-hashtags
Cvetojevic, S., & Hochmair, H. H. (2021) · Computers Environment and Urban Systems · doi:10.1016/j.compenvurbsys.2021.101621 · Cited by 16
Estimating the Bot Population on Twitter via Random Walk Based Sampling
Fukuda, M., Nakajima, K., & Shudo, K. (2022) · IEEE Access · doi:10.1109/access.2022.3149887 · Cited by 16
“The bot population (i.e., the percentage of bot users) on Twitter is one of the basic properties for understanding the impact of bots on individuals’ opinions and behaviors [9], [11].”
BackgroundResultInfluentialMulBot: Unsupervised Bot Detection Based on Multivariate Time Series
Mannocci, L., Cresci, S., Monreale, A., Vakali, A. I., & Tesconi, M. (2022) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata55660.2022.10020363 · Cited by 21
“It was estimated that between 9% and 17% of Twitter accounts are bots who contribute on average between 16% and 56% of tweets [3], [4].”
BackgroundAnalysis of Strategy and Spread of Russia-sponsored Content in the US in 2017Preprint
Spangher, A., Ranade, G. V., Nushi, B., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.10033 · Cited by 16
“Social bots on Twitter have played a role in influencing the spread of information in a network (Varol et al. 2017a; 2017b; Bessi and Ferrara 2016).”
BackgroundAn Empirical Study on Pre-trained Embeddings and Language Models for Bot Detection
García-Silva, A., Berrío, C., & Gómez-Pérez, J. M. (2019) · Workshop on Representation Learning for NLP (RepL4NLP) · doi:10.18653/v1/w19-4317 · Cited by 15
“In fact, as of 2017 it has been estimated that as 9% to 15% of twitter accounts are bots (Varol et al., 2017) which means that out of the 321 million active user accounts the number of automatic agents range from 28 to 48 million.”
BackgroundSchuchard, R., & Crooks, A. T. (2021) · PLoS ONE · doi:10.1371/journal.pone.0244309 · Cited by 14
“Therefore, most current social bot analysis research efforts rely primarily upon an open-source bot detection platform service such as Botometer [9,29] or DeBot [10], which, like most detection algorithms, currently only focus on Twitter due to its ease of data accessibility via its publicly available standard applica…”
BackgroundMethodInfluentialHossain, M. M., Awosaf, Z., Prottoy, M. S. H., et al. (2022) · Lecture notes in networks and systems · doi:10.1007/978-981-19-2445-3_51 · Cited by 15
Social media engagement and cryptocurrency performance
Qureshi, K., & Zaman, T. R. (2023) · PLoS ONE · doi:10.1371/journal.pone.0284501 · Cited by 15
Twitter Bot Detection Using Neural Networks and Linguistic Embeddings
Feng, W. Q., & Nguyen, U. T. (2023) · IEEE Open Journal of the Computer Society · doi:10.1109/ojcs.2023.3302286 · Cited by 18
“Existing Twitter bot detection models generally rely on features obtained from user information such as profile [1], [2], [3], [4], timestamps [1], [2], [4], friendship [1], [4], behavior [5], [6], and network connection [2], [7], [8].”
BackgroundMethodExposure to social bots amplifies perceptual biases and regulation propensity
Yan, H. Y., Yang, K.-C., Shanahan, J. E., & Menczer, F. (2023) · Scientific Reports · doi:10.1038/s41598-023-46630-x · Cited by 24
“%) of social media accounts are bots, which is substantially larger than the estimates provided by Twitter, Varol et al. 40 , and even Musk.”
BackgroundInfluentialSelf-citationAdversarial Socialbots Modeling Based on Structural Information Principles
Zeng, X., Peng, H. M., & Li, A. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i1.27793 · Cited by 18
Robust language-based mental health assessments in time and space through social media
Mangalik, S., Eichstaedt, J. C., Giorgi, S., Mun, J., Ahmed, F., Gill, G., Ganesan, A. V., Subrahmanya, S., Soni, N., Clouston, S., et al. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01100-0 · Cited by 22
“The exclusion of tweets with hyperlinks, retweet, and duplicates in particular emphasizes genuine thoughts expressed by humans rather than social bots which have been seen to generate far less novel content, focusing on retweeting and making posts with URLs 53,54 .”
BackgroundAkhtar, M. M., Masood, R., Ikram, M., & Kanhere, S. S. (2024) · ACM Asia Conference on Computer and Communications Security (ASIA CCS) · doi:10.1145/3634737.3644998 · Cited by 16
“Moving ahead, Varol et al. [139] uses various network features such as network density, clustering coefficient, retweet network, and in-strength and out-strength (weighted degree) distribution.”
BackgroundMethodThe Impact of Malicious Accounts on Political Tweet Sentiment
Heredia, B., Prusa, J. D., & Khoshgoftaar, T. M. (2018) · IEEE International Conference on Collaboration and Internet Computing (CIC) · doi:10.1109/cic.2018.00035 · Cited by 15
“60 results in similar bot distributions [17], [30].”
ResultKim, D., Graham, T., Wan, Z., & Rizoiu, M.-A. (2019) · QUT ePrints (Queensland University of Technology) · Cited by 14
It’s a Matter of Style: Detecting Social Bots through Writing Style Consistency
Cardaioli, M., Conti, M., Sorbo, A. D., Fabrizio, E., Laudanna, S., & Visaggio, C. A. (2021) · International Conference on Computer Communications and Networks (ICCCN) · doi:10.1109/icccn52240.2021.9522339 · Cited by 15
Characterizing Retweet Bots: The Case of Black Market Accounts
Elmas, T., Overdorf, R., & Aberer, K. (2022) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v16i1.19282 · Cited by 15
“One common feature found in prior bot detection studies [7, 35, 36, 37, 2] is account age.”
BackgroundMethodUnderlying socio-political processes behind the 2016 US election
Bryden, J., & Silverman, E. (2019) · PLoS ONE · doi:10.1371/journal.pone.0214854 · Cited by 15
Interaction-Based Behavioral Analysis of Twitter Social Network Accounts
İş, H., & Tuncer, T. (2019) · Applied Sciences · doi:10.3390/app9204448 · Cited by 17
Bots fired: examining social bot evidence in online mass shooting conversations
Schuchard, R., Crooks, A. T., Stefanidis, A., & Croitoru, A. (2019) · Humanities and Social Sciences Communications · doi:10.1057/s41599-019-0359-x · Cited by 14
“Turning our attention to social bots, Varol et al. (2017) estimated that social bots account for 9–15% of all Twitter user accounts.”
BackgroundMethodKušen, E., & Strembeck, M. (2020) · Journal of Computational Social Science · doi:10.1007/s42001-020-00092-7 · Cited by 17
“According to [65], as much as 15% of all Twitter accounts can be attributed to automated bot accounts.”
BackgroundDetecting Malicious Social Bots: Story of a Never-Ending Clash
Cresci, S. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-39627-5_7 · Cited by 16
The spread of misinformation by social botsPreprint
Shao, C., Ciampaglia, G. L., Varol, O., Flammini, A., & Menczer, F. (2017) · arXiv (Cornell University) · Cited by 18
Self-citationThe Battle for #Baltimore: Networked Counterpublics and the Contested Framing of Urban Unrest
Welles, B. F., & Jackson, S. J. (2019) · International journal of communication · Cited by 14
Improving information spread by spreading groups
Sela, A., Milo, O., Kagan, E., & Ben‐Gal, I. (2019) · Online Information Review · doi:10.1108/oir-08-2018-0245 · Cited by 15
A Study of Friend Abuse Perception in Facebook
Talukder, S. K., & Cărbunar, B. (2020) · ACM Transactions on Social Computing · doi:10.1145/3408040 · Cited by 17
“Past work has shown that adversaries, including bot-operated user accounts [71], 2 can establish friend relations with unsuspecting social network users and then expose them to vulnerabilities and abuse that include the collection and misuse of private 17:2 S. Talukder and B. Carbunar information…”
BackgroundDetection of automated behavior on Twitter through approximate entropy and sample entropy
Gilmary, R., Akila, V., & Vaiyapuri, G. (2021) · Personal and Ubiquitous Computing · doi:10.1007/s00779-021-01647-9 · Cited by 15
“These techniques identifies bots by examining the various attributes like user profiles [35], the content of tweets [51], location attributes [18], temporal attributes [8], sentiment attributes [1] or interaction graph between profiles and tweets [46].”
BackgroundNg, Q. X., Lee, D. Y. X., Yau, C. E., Lim, Y. L., Ng, C. X., & Liew, T. M. (2023) · Healthcare · doi:10.3390/healthcare11101485 · Cited by 14
“Fourth, we cannot rule out the presence of bot accounts on Twitter that may have a name similar or identical to that of an organisation, although research has shown that such bot accounts make up only a small proportion of users and tweets on the platform [34].”
BackgroundBots, Elections, and Controversies: Twitter Insights from Brazil's Polarised Elections
Pacheco, D. F. (2024) · International World Wide Web Conference (WWW) · doi:10.1145/3589334.3645651 · Cited by 17
“In 2017, Varol et al. [46] proposed a method for conducting a census of Twitter accounts and found that approximately 9% to 15% of accounts were likely to be social bots.”
MethodEllaky, Z., & Benabbou, F. (2024) · Scientific African · doi:10.1016/j.sciaf.2024.e02269 · Cited by 16
Early Detection and Prevention of Malicious User Behavior on Twitter Using Deep Learning Techniques
Sánchez, R., Zubiaga, A., & Almeida, A. (2024) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2024.3419171 · Cited by 18
“…and community formations, facilitating the Caverlee-2011 [38] 15483 14833 Cresci-2015 [17] 1950 1950 Cresci-2017 [39] 7049 2764 Varol-2017 [40] 733 1495 Gilani-2017 [41] 1090 1413 Cresci-stock-2018 [42] 7102 6174 Midterm-2018 [43] 42446 8092 Pronbots-2019 [43] 17882 - Celebrity-2019 [43]…”
Influence Estimation on Social Media Networks Using Causal Inference
Smith, S., Kao, E., Shah, D. C., Simek, O., & Rubin, D. (2018) · Symposium on Software Performance · doi:10.1109/ssp.2018.8450823 · Cited by 17
Babcock, M., Beskow, D. M., & Carley, K. M. (2019) · Journal of Data and Information Quality · doi:10.1145/3339468 · Cited by 13
“, one study suggests that 48 million Twitter accounts may be bots [24].”
BackgroundUsing User Behavior to Measure Privacy on Online Social Networks
Li, X., Xin, Y., Zhao, C., et al. (2020) · IEEE Access · doi:10.1109/access.2020.3000780 · Cited by 13
Misleading information in Spanish: a survey
Providel, E., & Mendoza, M. (2021) · Social Network Analysis and Mining · doi:10.1007/s13278-021-00746-y · Cited by 13
“The visibility of some rumors and fake news can be amplified by bots (Varol et al. 2017).”
BackgroundBoosting Fraud Detection in Mobile Payment with Prior Knowledge
Sun, Q., Tang, T., Chai, H., et al. (2021) · Applied Sciences · doi:10.3390/app11104347 · Cited by 13
González-Pizarro, F., Figueroa, A., López, C., & Aragon, C. (2022) · Computer Supported Cooperative Work (CSCW) · doi:10.1007/s10606-021-09422-3 · Cited by 16
“Botometer is free and has been widely used 5 (Varol et al., 2017; Yang et al., 2019).”
BackgroundMethodInfluentialMethod for Detecting Far-Right Extremist Communities on Social Media
Карпова, А. Ю., Savelev, A., Вильнин, А. Д., et al. (2022) · Social Sciences · doi:10.3390/socsci11050200 · Cited by 13
Zhang, M., Chen, Z., Qi, X., & Liu, J. (2022) · Sustainability · doi:10.3390/su14095566 · Cited by 15
Do Twitter users change their behavior after exposure to misinformation? An in-depth analysis
Wang, Y., Han, R. Y., Lehman, T. S., Lv, Q., & Mishra, S. (2022) · Social Network Analysis and Mining · doi:10.1007/s13278-022-00992-8 · Cited by 14
“As social bots’ general behavior has been well studied (Varol et al 2017; Shao et al 2018), we only focused on their replying related behavior towards misinformation.”
BackgroundKwon, K. H., Priniski, J. H., & Chadha, M. (2018) · Communication Methods and Measures · doi:10.1080/19312458.2018.1430755 · Cited by 14
“Studies estimate that 7% of Facebook accounts, and 9 – 15% of Twitter accounts are composed of bot-accounts (Varol, Ferrara, Davis, Menczer, & Flammini, 2017).”
BackgroundTracking and Characterizing the Competition of Fact Checking and Misinformation: Case Studies
Shao, C., Hui, P.-M., Cui, P., Jiang, X., & Peng, Y. (2018) · IEEE Access · doi:10.1109/access.2018.2881037 · Cited by 13
“classification by supervised learning, including a large set of features, such as network, temporal, language, and sentiment signals [18].”
BackgroundTrends in Detection and Characterization of Propaganda Bots
Williamson, W., III, & Scrofani, J. W. (2019) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2019.854 · Cited by 12
“In most of the cases we studied the random forest approach provides the best results for example, the Indiana University Team [14] tried each of the standard classifier routines in the scikit-learn library and found that RF was the most accurate algorithm for their features, yielding a 0.”
BackgroundMethodInfluentialDetection of Arabic Spam Tweets Using Word Embedding and Machine Learning
Al-Azani, S., & El-Alfy, E.-S. M. (2018) · International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT) · doi:10.1109/3ict.2018.8855747 · Cited by 17
“As mentioned in [1], 9% to 15% of Twitter accounts are estimated to be controlled by software (bots), emulating human behavior to generate content and establish interactions.”
BackgroundContagion Dynamics of Extremist Propaganda in Social NetworksPreprint
Ferrara, E. (2017) · SSRN Electronic Journal · doi:10.2139/ssrn.2982259 · Cited by 19
Self-citationExploring the construction and infiltration strategies of social bots in sina microblog
Wang, W., Chen, X., Jiang, S., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-76814-8 · Cited by 12
SybilHP: Sybil Detection in Directed Social Networks with Adaptive Homophily Prediction
Lu, H., Gong, D., Li, Z., Liu, F., & Liu, F. (2023) · Applied Sciences · doi:10.3390/app13095341 · Cited by 13
CrediBot: Applying Bot Detection for Credibility Analysis on Twitter
Aguilera, A., Quinteros, P., Dongo, I., & Cardinale, Y. (2023) · IEEE Access · doi:10.1109/access.2023.3320687 · Cited by 12
Exploring the climate change discourse on Chinese social media and the role of social bots
Ji, J., Hu, T., Chen, Z., & Zhu, M. (2023) · Asian Journal of Communication · doi:10.1080/01292986.2023.2269423 · Cited by 14
“Social bots also actively broaden their social networks by following and interacting with many other users, especially celebrities (Varol et al., 2017).”
BackgroundRodrigues, V. P., & Caetano, M. A. L. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e22670 · Cited by 12
“…scientific evidence suggests that the human brain ’ s structures that respond to primary rewards (i.e., necessary for the survival, such as food) and secondary rewards (i.e., derived from the primary rewards, such as money) are also directly related to how people process social rewards [19].”
BackgroundHybrid Modeling Approaches in Energy Internet: Bridging Cyber, Physical, and Social Realms
Chaturvedi, P., Thethi, H. P., Reddy, N. V. U., et al. (2024) · doi:10.1109/ic3se62002.2024.10592917 · Cited by 12
“A trading game for energy based on agents Using agent-based models, ABETS can recreate energy trading situations [14].”
MethodTowards the future of bot detection: A comprehensive taxonomical review and challenges on Twitter/X
Javed, D., Jhanjhi, N. Z., Khan, N. A., Ray, S. K., Mazroa, A. A., Ashfaq, F., & Das, S. R. (2024) · Computer Networks · doi:10.1016/j.comnet.2024.110808 · Cited by 15
Dissecting a social bot powered by generative AI: anatomy, new trends and challenges
López-Joya, S., Diaz-Garcia, J. A., Ruiz, M. D., & Martín-Bautista, M. J. (2025) · Social Network Analysis and Mining · doi:10.1007/s13278-025-01410-5 · Cited by 13
“This influence extends beyond politics; in 2017, it was estimated that 15% of Twitter accounts were bots (Varol et al. 2017), while in 2019, 11% of Facebook Page 2 of 24 accounts were bots (Zago et al. 2019).”
Message Distortion in Information Cascades
Ribeiro, M. H., Gligorić, K., & West, R. (2019) · International World Wide Web Conference (WWW) · doi:10.1145/3308558.3313531 · Cited by 22
“Our experimental setting abstracts away from the complex social media landscape with heterogeneous agents including bots and trolls [16, 55], in order to understand the fundamental patterns that govern information distortion.”
BackgroundSocial Media Multidimensional Analysis for Intelligent Health Surveillance
Aramburu, M. J., Berlanga, R., & Lanza-Cruz, I. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17072289 · Cited by 12
Using Artificial Intelligence and Social Media for Disaster Response and Management: An Overview
Ofli, F., Imran, M., & Alam, F. (2020) · Disaster research and management series on the global South · doi:10.1007/978-981-15-4291-6_5 · Cited by 12
Evolution of the plandemic communication network among serial participants on Twitter
Xu, Y., Sun, Y., Hagen, L., Patel, M., & Falling, M. (2021) · New Media & Society · doi:10.1177/14614448211050928 · Cited by 12
“Developers of Botometer (Varol et al., 2017) have shown that it can detect human accounts with higher accuracy (90”
BackgroundMethodInfluentialZeng, Z., Li, T., Sun, S., Sun, J., & Yin, J. (2021) · Data Technologies and Applications · doi:10.1108/dta-07-2021-0196 · Cited by 12
Novel semantic and statistic features-based author profiling approach
Ouni, S., Fkih, F., & Omri, M. N. (2022) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-022-04198-w · Cited by 12
“…able to determine several users’ aspects such as gender, age, language, etc. Recently, the interest in the field of bot and gender profiling has been increasing (Varol et al. 2017; Rangel et al. 2018) as several users tend to spread threats, hate messages, and false news (Giachanou et al. 2020).”
BackgroundInformation access equality on generative models of complex networks
Wang, X., Varol, O., & Eliassi‐Rad, T. (2022) · Applied Network Science · doi:10.1007/s41109-022-00494-8 · Cited by 12
Self-citationPolitical polarization on Twitter during the COVID-19 pandemic: a case study in Brazil
Brum, P. P. V., Teixeira, M. C., Vimieiro, R., Araújo, E. F. D. M., Meira, W., & Pappa, G. L. (2022) · Social Network Analysis and Mining · doi:10.1007/s13278-022-00949-x · Cited by 15
“The cyborgs are users controlled by a human and a computer alternately (Chu et al. 2010; Varol et al. 2017).”
BackgroundThe Impact of Governance Bots on Sense of Virtual Community
Smith, C. E., Alam, I., Tan, C., Keegan, B., & Blanchard, A. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3555563 · Cited by 12
“The emergence of relational communities online began with early discussion fora such as USEnet in the 80s or America Online (AOL) chatgroups in the 90s, and now continues more rapidly and ubiquitously as social media platforms like Reddit, Facebook, Twitter, etc. continue to proliferate new online spaces where users c…”
BackgroundInfluentialPerna, D., & Tagarelli, A. (2018) · ACM Conference on Hypertext and Social Media (HT) · doi:10.1145/3209542.3209563 · Cited by 12
“We focus our attention on three of the most relevant and recent methods for bot classification, namely BotOrNot [17, 44],DeBot [13], and BotWalk [34].”
BackgroundMethodInfluentialDeveloping a Twitter bot that can join a discussion using state-of-the-art architectures
Çetinkaya, Y. M., Toroslu, İ. H., & Davulcu, H. (2020) · Social Network Analysis and Mining · doi:10.1007/s13278-020-00665-4 · Cited by 14
“Automated botnets now exist to exploit echo chambers and further divide and polarize communities (Varol et al. 2017).”
BackgroundBot Detection on Social Networks Using Persistent Homology
Nguyen, M., Aktas, M. E., & Akbaş, E. (2020) · Mathematical and Computational Applications · doi:10.3390/mca25030058 · Cited by 12
“The malicious activity of bots has led to the necessity of bot detection techniques [26,27].”
BackgroundDetecting Engagement Bots on Social Influencer Marketing
Kim, S., & Han, J. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-60975-7_10 · Cited by 17
Perils and Challenges of Social Media and Election Manipulation Analysis: The 2018 US MidtermsPreprint
Deb, A., Luceri, L., Badawy, A., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.00043 · Cited by 12
“the system were first published in [15, 53] and further revised in [59].”
BackgroundMethodSelf-citationLi, Q., Liu, Q., Liu, S., Di, X., Chen, S., & Zhang, H. (2023) · Communication and the Public · doi:10.1177/20570473231166157 · Cited by 12
“Previous research on social bots has mainly focused on the behavioral characteristics (Varol et al., 2017), communication effects (Varol & Uluturk, 2020), and how to form a social network under a specific topic to drive public opinion (Cheng et al., 2020), while there are few studies on the growth…”
BackgroundMechanisms Driving Online Vaccine Debate During the COVID-19 Pandemic
Seckin, O. C., Atalay, A., Otenen, E., Duygu, U., & Varol, O. (2024) · Social Media + Society · doi:10.1177/20563051241229657 · Cited by 19
“Botometer defines bots as “social media accounts controlled in part by software that can post content and interact with other accounts programmatically and possibly automatically” (Varol et al., 2017).”
BackgroundSelf-citationChang, Y.-Y., Wang, W., & Peng, W.-C. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i1.27752 · Cited by 11
Detecting Social Media Manipulation in Low-Resource Languages
Haider, S., Luceri, L., Deb, A., Badawy, A., Peng, N., & Ferrara, E. (2023) · International World Wide Web Conference (WWW) · doi:10.1145/3543873.3587615 · Cited by 13
Self-citationDetecting Social Media Bots with Variational AutoEncoder and k-Nearest Neighbor
Wang, X., Zhang, Q., Zheng, K., et al. (2021) · Applied Sciences · doi:10.3390/app11125482 · Cited by 11
“Varol et al. [5] pointed out that 9–15% of active Twitter accounts are bots.”
BackgroundTopic Analysis of Social Media Posts during the COVID-19 Pandemic: Evidence from Tweets in Turkish
Bătrâncea, I., Balcı, M. A., Batrancea, L. M., Akgüller, Ö., Tulai, H. I., Rus, M.-I., Masca, E. S., & Morar, I. D. (2023) · Journal of the Knowledge Economy · doi:10.1007/s13132-023-01565-6 · Cited by 11
“It has been suggested that between 9% and 15% of Twitter accounts in 2017 were bots (Varol et al., 2017).”
Fake News Detection Using Time Series and User Features Classification
Previti, M., Rodríguez-Fernández, V., Camacho, D., Carchiolo, V., & Malgeri, M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-43722-0_22 · Cited by 12
Döring, N. M. (2019) · Springer eBooks · doi:10.1007/978-3-658-18016-4_8 · Cited by 12
O’Brien, G. E., Ganjigunta, R., & Dhillon, P. S. (2024) · Journal of Medical Internet Research · doi:10.2196/56651 · Cited by 13
“However, one estimate from 2017 put the percentage between 9% and 15% [61], and another using Twitter data from 2022 estimated between 8% and 20%, depending on how inactive and deleted accounts were handled in the calculation [62].”
Social bots spoil activist sentiment without eroding engagement
Li, L., Vásárhelyi, O., & Vedres, B. (2024) · Scientific Reports · doi:10.1038/s41598-024-74032-0 · Cited by 16
“Bot interaction has the strongest negative impact on the sentiment of bot-exposed users with neutral opinion, indicating that bots might target those users whose opinion can be changed 15,56 .”
Lingam, G., & Das, S. K. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113019 · Cited by 11
Exploring the sentiment of entrepreneurs on Twitter
Waters, J. P., Nicolaou, N., Stefanidis, D., Efstathiades, H., Pallis, G., & Dikaiakos, M. D. (2021) · PLoS ONE · doi:10.1371/journal.pone.0254337 · Cited by 13
“Experimental results have shown that Botometer achieves 95% accuracy [82].”
BackgroundKoch, K., Dippel, A., & Schümann, M. (2021) · Online Social Networks and Media · doi:10.1016/j.osnem.2021.100151 · Cited by 10
A New Joint Approach with Temporal and Profile Information for Social Bot Detection
Yang, Z., Chen, X., Wang, H., Wang, W., Miao, Z., & Jiang, T. (2022) · Security and Communication Networks · doi:10.1155/2022/9119388 · Cited by 11
“[23, 48], the length of nickname is used to detect social bots, so the length of username is also adopted as a feature in this paper.”
BackgroundMethodOpinion manipulation on Farsi Twitter
Farzam, A., Moradi, P., Mohammadi, S., Padar, Z., & Siegel, A. A. (2023) · Scientific Reports · doi:10.1038/s41598-022-26921-5 · Cited by 15
“To classify users according to our typology, we use Botometer [16, 17, 18], a supervised machine-learning algorithm, to measure the Complete Automation Probability (CAP) for the users in our Twitter data.”
MethodSocial Bots’ Role in the COVID-19 Pandemic Discussion on Twitter
Zhang, Y., Song, W., Shao, J., Abbas, M., Zhang, J., Koura, Y. H., & Su, Y. (2023) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph20043284 · Cited by 10
“Studies have shown that social bots account for 9–15% of active Twitter accounts [24].”
BackgroundTweet Trajectory and AMPS-based Contextual Cues can Help Users Identify Misinformation
Zade, H., Woodruff, M., Johnson, E., Stanley, M., Zhou, Z., Huynh, M. T., Acheson, A. E., Hsieh, G., & Starbird, K. (2023) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3579536 · Cited by 9
“Though efforts to detect amplification by bot accounts on these platforms may be useful [85], they do not address the perhaps more prominent role—as we see in these cases—of real human Twitter accounts in disseminating misinformation.”
BackgroundMulti-input BiLSTM deep learning model for social bot detection
Messai, A., Hamida, Z. F., Drif, A., & Giordano, S. (2023) · International Conference on Advances in Electronics, Control and Communication Systems (ICAECCS) · doi:10.1109/icaeccs56710.2023.10104646 · Cited by 10
“Davis et al. [9] proposed BotOrNot, also referred to as Botometer, that was further improved through training on a novel dataset by [10], the model employs a rich set of over a thousand features, including user profiles, friendship networks, temporal content, and sentiment-based features.”
BackgroundJain, S., Jain, M., & Balasubramani, R. (2019) · IJARCCE · doi:10.17148/ijarcce.2019.8543 · Cited by 10
How Much I Can Rely on You: Measuring Trustworthiness of a Twitter User
Das, R., Karmakar, G., & Kamruzzaman, J. (2019) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2019.2929782 · Cited by 10
“Moreover, between 9 and 15 percent of active Twitter users are bots [35] whose activities are controlled by computer programs.”
BackgroundIdentifying Twitter Bots Using a Convolutional Neural Network.
Färber, M., Qurdina, A., & Ahmedi, L. (2019) · CLEF (Working Notes) · Cited by 10
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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