A decade of social bot detection
Open accessAbstract
Bots increasingly tamper with political elections and economic discussions. Tracing trends in detection strategies and key suggestions on how to win the fight.
AI snapshot
Written from the paper’s full text.
TL;DR
This survey reviews the first decade (2010–2020) of research on social bot detection, documenting a paradigm shift from supervised, individual-account detectors to unsupervised, group-based detectors targeting coordinated behavior, and argues that adversarial machine learning is the emerging next direction. It concludes that detection must move away from binary labels toward multifaceted measures of inauthentic coordination.
Key findings
- Bot prevalence is substantial: ~15% of all active Twitter accounts in 2017 and 11% of all Facebook accounts in 2019; in high-stakes contexts it rises sharply, with 71% of Twitter users mentioning trending US stocks likely being bots.
- Bot evolution degrades detection: only 5% of newer bots are removed from platforms versus 60% of older ones, and crowdsourcing participants identified newer bots only 24% of the time versus 91% for older bots.
- A longitudinal classification of 236 bot detectors published since 2010 shows group-based detectors first surpassed individual-account detectors in 2018; unsupervised approaches rose from 2013 and plateaued around 2017, coinciding with the rise of adversarial approaches (first papers in 2018–19).
- The paper reports no consensus on bot impact: some studies find bots pivotal in spreading disinformation, polarization and hateful speech, while competing results claim bots play no significant role.
Methods
- Longitudinal survey and manual classification of more than 230 papers proposing bot detection techniques, categorized along two orthogonal dimensions: (A) individual-account vs. group-based targets, and (B) high-level approach (heuristics, crowdsourcing, supervised ML, unsupervised ML, adversarial).
- The paper also synthesizes prior empirical results on bot prevalence, bot evolution, and human annotation accuracy, and proposes a conceptual bi-dimensional generalizability space (bot type × time) and a GAN-based adversarial detection framework.
- No new primary data collection or experiment is reported; the work is a review/survey.
Limitations
- The paper states that the exact number of new bot detectors per type can vary if a different set of papers is analyzed, though it argues the overall trends are clear and reliable.
- The paper states that adversarial bot detection is still in its infancy, lagging behind traditional approaches, and that challenges remain in generating diverse, realistic adversarial examples and in evaluating their novelty and diversity.
- Evident from the text: the survey's classification relies on manual labeling by the author, and the paper notes the field lacks standard benchmarks, reference datasets, and agreed definitions of social bots, which limits comparability of the reviewed detectors.
Model-written summary. Check it against the paper before relying on it.
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Claims the paper makes
0 of 54 cited sources checked · 0 supported · 0 flagged
- Findingown data§ ACMReference Format: · p. 3
Since 2014, the number of publications on the characterization, detection and impact estimation of social bots has skyrocketed.
“Since 2014, the number of publications on the topic skyrocketed.”
Quote in the paperSubstantial claim - Findingcitation§ THE SOCIAL BOT PANDEMIC · p. 3
The majority of existing works on social bot detection focused on detecting malicious bots rather than benign bots.
“Regarding the detection of benign or malicious social bots, the majority of existing works focused on detecting the latter.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [30] [30] Stefan Stieglitz, Florian Brachten, Björn Ross, and Anna-Katharina Jung. 2017. Do social bots dream of electric sheep? A categorisation of social media bot accounts. In The 17th Australasian Conference on Information Systems (ACIS'17) . -
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- Findingown data§ ACMReference Format: · p. 2
The scientific literature has documented political manipulation by social bots in 39 countries.
“World view of 39 countries for which scientific literature documented political manipulation by social bots.”
Quote in the paperNot a substantial claim - Findingcitation§ THE SOCIAL BOT PANDEMIC · p. 3
Scholars still lack a widespread consensus on the impact of social bots.
“In fact, scholars still lack a widespread consensus on the impact of social bots, with some studies reporting on their pivotal role for increasing disinformation's spread, polarization, and hateful speech [27, 29], and competing results claiming that bots do not play a significant role in these processes [32].”
Quote in the paperNot a substantial claim0 of 3 sources checked- cites [27] [27] Chengcheng Shao, Giovanni Luca Ciampaglia, Onur Varol, Kai-Cheng Yang, Alessandro Flammini, and Filippo Menczer. 2018. The spread of low-credibility content by social bots. Nature communications 9, 1 (2018), 4787. -
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- cites [29] [29] Massimo Stella, Emilio Ferrara, and Manlio De Domenico. 2018. Bots increase exposure to negative and inflammatory content in online social systems. Proceedings of the National Academy of Sciences 115, 49 (2018), 12435-12440. - source
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- cites [32] [32] Soroush Vosoughi, Deb Roy, and Sinan Aral. 2018. The spread of true and false news online. Science 359, 6380 (2018), 1146-1151. - source
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- Findingnone given§ THE SOCIAL BOT PANDEMIC · p. 3
Bots did not always have a real impact in political discussions about the countries highlighted in Figure 1.
“Despite taking part in political discussions about all countries highlighted in figure, bots did not always have a real impact.”
Quote in the paperNot a substantial claim
- Findingown data§ THE RISE OF GROUP APPROACHES · p. 8
The longitudinal categorization covers 236 bot detectors published since 2010.
“Longitudinal categorization of 236 bot detectors published since 2010.”
Quote in the paperSubstantial claim - Findingown data§ THE RISE OF GROUP APPROACHES · p. 9
Bot detection approaches based on heuristics and crowdsourcing received very little attention.
“From panel B we note that bot detection approaches based on heuristics and crowdsourcing received very little attention.”
Quote in the paperNot a substantial claim - Findingown data§ THE RISE OF GROUP APPROACHES · p. 9
In 2018, the number of newly proposed group-based detectors surpassed for the first time that of detectors based on the analysis of individual accounts.
“In fact, in 2018 the number of newly proposed group-based detectors surpassed for the first time that of detectors based on the analysis of individual accounts.”
Quote in the paperNot a substantial claim - Findingown data§ THE RISE OF GROUP APPROACHES · p. 8
Both panels of Figure 5 document the rise of a new approach to bot detection characterized by group-analyses and many unsupervised detectors.
“Both panels clearly document the rise of a new approach to bot detection, characterized by group-analyses and many unsupervised detectors.”
Quote in the paperSubstantial claim - Findingcitation§ THE RISE OF GROUP APPROACHES · p. 9
Several group detectors implement graph-based approaches and aim at spotting suspicious account connectivity patterns.
“For instance, several group detectors implement graph-based approaches and aim at spotting suspicious account connectivity patterns [20, 24].”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [20] [20] Meng Jiang, Peng Cui, Alex Beutel, Christos Faloutsos, and Shiqiang Yang. 2016. Catching Synchronized Behaviors in Large Networks: A Graph Mining Approach. ACM Transactions on Knowledge Discovery from Data (TKDD) 10, 4 (2016). - source
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- cites [24] [24] Leonardo Nizzoli, Serena Tardelli, Marco Avvenuti, Stefano Cresci, Maurizio Tesconi, and Emilio Ferrara. 2020. Charting the landscape of online cryptocurrency manipulation. IEEE Access (2020). https://doi.org/10.1109/ACCESS.2020.3003370 - source
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- Findingreasoning§ THE ISSUE OF BOT EVOLUTION · p. 7
Newer bots are more similar to legitimate human-operated accounts than to other older bots.
“In fact, newer bots are more similar to legitimate human-operated accounts than to other older bots .”
Quote in the paperNot a substantial claim - Findingreasoning§ THE RISE OF GROUP APPROACHES · p. 7
Since 2012-13, several different teams independently proposed new systems that target groups of accounts as a whole rather than individual accounts.
“Since 2012-13, several different teams independently proposed new systems that, despite being based on different techniques and implementations, shared the same concepts and philosophy. As schematized in Figure 3 (panel B ), the primary characteristic of these new systems, is that of targeting groups of accounts as a whole, rather than individual accounts.”
Quote in the paperSubstantial claim - Findingown data§ THE RISE OF GROUP APPROACHES · p. 8
The plateau reached by unsupervised approaches since 2017 occurred in conjunction with the recent rise of adversarial ones.
“Interestingly, the plateau reached by unsupervised approaches since 2017 occurred in conjunction with the recent rise of the adversarial ones.”
Quote in the paperNot a substantial claim - Findingown data§ THE RISE OF GROUP APPROACHES · p. 9
Since 2015 there has been a steadily increasing number of bot detectors published every year.
“Indeed, since 2015 there has been a steadily increasing number of bot detectors published every year.”
Quote in the paperNot a substantial claim - Findingreasoning§ A GLIMPSE INTO THE FUTURE OF DECEPTION DETECTION · p. 11
The first papers published in 2018-19 initiated the development of an adversarial approach to bot detection.
“This intuition resulted in the first papers published in 2018-19 that initiated the development of an adversarial approach to bot detection, as shown in panel B of Figure 5.”
Quote in the paperNot a substantial claim - Findingown data§ OPEN CHALLENGES AND THE WAY AHEAD · p. 14
For more than a decade, each of the menaces posed by sophisticated social bots, cyborgs, trolls, and collusive humans was fought separately.
“Our analysis revealed that for more than a decade we fought each of the menaces posed by sophisticated social bots, cyborgs, trolls, and collusive humans, separately.”
Quote in the paperSubstantial claim - Findingreasoning§ THE RISE OF GROUP APPROACHES · p. 10
Some combinations of approaches - above all, text-based detectors that perform unsupervised, group analyses - are almost unexplored and underrepresented in the landscape of existing bot detectors.
“As a consequence, some combinations of approaches - above all, text-based detectors that perform unsupervised, group analyses - are almost unexplored and definitely underrepresented in the landscape of existing bot detectors.”
Quote in the paperNot a substantial claim - Findingown data§ OPEN CHALLENGES AND THE WAY AHEAD · p. 14
The emergence of group-based approaches occurred several years before 'coordinated inauthentic behavior' was acknowledged as the main threat by the general public and social platforms.
“Our in-depth analysis revealed the emergence of group-based approaches several years before 'coordinated inauthentic behavior' was acknowledged as the main threat to our online social ecosystems by the general public, and by the social platforms themselves.”
Quote in the paperNot a substantial claim - Findingnone given§ OPEN CHALLENGES AND THE WAY AHEAD · p. 14
A new rising trend of adversarial approaches has emerged.
“Finally, we highlighted the new rising trend of adversarial approaches.”
Quote in the paperSubstantial claim - Findingown data§ OPEN CHALLENGES AND THE WAY AHEAD · p. 14
Early days of social bot detection were characterized by simple supervised detectors analyzing accounts individually.
“Early days were characterized by simple supervised detectors analyzing accounts individually .”
Quote in the paperSubstantial claim - Findingreasoning§ A GLIMPSE INTO THE FUTURE OF DECEPTION DETECTION · p. 10
Both the individual and the group-based approaches to social bot detection follow a reactive schema.
“Firstly, we observe that both the individual and the group-based approaches to social bot detection follow a reactive schema.”
Quote in the paperSubstantial claim - Interpretationreasoning§ ACMReference Format: · p. 2
The list of papers documenting political manipulation by social bots allows mapping the worldwide spread of the social bot pandemic.
“Although the list of papers is illustrative and not exhaustive, it nonetheless allows to map the worldwide spread of the social bot pandemic.”
Quote in the paperSubstantial claim- The authors state this as an assumption: The list of papers documenting political manipulation by social bots in Figure 1 is illustrative and not exhaustive.
- Interpretationcitation§ THE SOCIAL BOT PANDEMIC · p. 3
When strong political or economic interests are at stake, the presence of bots dramatically increases.
“Even more worryingly, when strong political or economic interests are at stake, the presence of bots dramatically increases.”
Quote in the paperNot a substantial claim - Interpretationnone given§ THE ISSUE OF BOT EVOLUTION · p. 7
An increased hybridization between automated and human-driven behaviors is among the reasons for the human-like appearance of many bots.
“Among the reasons for the human-like appearance of many bots is an increased hybridization between automated and human-driven behaviors.”
Quote in the paperNot a substantial claim - Premisecitation§ THE DAWN OF SOCIAL BOT DETECTION · p. 4
The first work that specifically addressed the detection of automated accounts in online social networks dates back to January 2010.
“The first work that specifically addressed the detection of automated accounts in online social networks dates back to January 2010 [37].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [37] [37] Sarita Yardi, Daniel Romero, Grant Schoenebeck, et al. 2010. Detecting spam in a Twitter network. First Monday 15, 1 (2010). -
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- Interpretationreasoning§ THE RISE OF GROUP APPROACHES · p. 9
Group-based detectors are particularly effective at identifying evolving, coordinated, and synchronized accounts.
“As a consequence of this paradigm-shift, group-based detectors are particularly effective at identifying evolving, coordinated, and synchronized accounts.”
Quote in the paperNot a substantial claim - Premisecitation§ THE DAWN OF SOCIAL BOT DETECTION · p. 5
Botometer's generality and ease of deployment are counterbalanced by reduced bot detection accuracy.
“The generality and ease of deployment of this detector are however counterbalanced by a reduced bot detection accuracy [5, 17].”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [5] [5] Stefano Cresci, Roberto Di Pietro, Marinella Petrocchi, Angelo Spognardi, and Maurizio Tesconi. 2017. The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In The 26th International World Wide Web Conference Companion (WWW'17 Companion) . IW3C2. - source
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- cites [17] [17] Christian Grimme, Dennis Assenmacher, and Lena Adam. 2018. Changing perspectives: Is it sufficient to detect social bots?. In The 10th International Conference on Social Computing and Social Media (SCSM'18) . - source
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- Interpretationreasoning§ THE DAWN OF SOCIAL BOT DETECTION · p. 5
Systems that solely focus on network characteristics, textual content, or profile information are typically easier to game.
“Instead, other systems solely focus on network characteristics, textual content of shared messages, or profile information. These systems are typically easier to game, since they only analyze a single facet of the complex behavior of bots.”
Quote in the paperNot a substantial claim - Interpretationreasoning§ THE RISE OF GROUP APPROACHES · p. 7
Devising techniques for spotting suspiciously coordinated and synchronized behaviors is likely to yield better results than analyzing individual accounts.
“Devising techniques for spotting suspiciously coordinated and synchronized behaviors is thus likely to yield better results than analyzing individual accounts.”
Quote in the paperSubstantial claim - Interpretationreasoning§ THE ISSUE OF BOT EVOLUTION · p. 7
Current sophisticated bots are hardly distinguishable from legitimate accounts if analyzed one at a time.
“The previous anecdotal and quantitative results tell us that current sophisticated bots are hardly distinguishable from legitimate accounts if analyzed one at a time, as supervised classifiers and crowdsourcing participants did.”
Quote in the paperSubstantial claim - Premisecitation§ THE ISSUE OF BOT EVOLUTION · p. 6
Noteworthy works published by Chao Yang et al. between 2011 and 2013 provided the first evidence and theoretical foundations to study social bot evolution.
“Noteworthy works published by Chao Yang et al. between 2011 and 2013 provided the first evidence and the theoretical foundations to study social bot evolution [34].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [34] [34] Chao Yang, Robert Harkreader, and Guofei Gu. 2013. Empirical evaluation and new design for fighting evolving twitter spammers. IEEE Transactions on Information Forensics and Security 8, 8 (2013), 1280-1293. - source
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- Interpretationcitation§ THE SOCIAL BOT PANDEMIC · p. 3
The ubiquity of social bots is partly fueled by the availability of open-source code.
“The ubiquity of social bots is also partly fueled by the availability of open-source code, for which Bence Kollanyi reported an exponential growth that led in 2016 to more than 4,000 GitHub repositories containing code for deploying Twitter bots [22].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [22] [22] Bence Kollanyi. 2016. Where Do Bots Come From? An Analysis of Bot Codes Shared on GitHub. International Journal of Communication 10 (2016), 20. -
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- Premisecitation§ THE ISSUE OF BOT EVOLUTION · p. 7
Yang's classifier for detecting evolving bots was no longer successful at spotting the third wave of malicious accounts.
“Unfortunately, Yang's classifier for detecting evolving bots was no longer successful at spotting the third wave of malicious accounts [6].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [6] [6] Stefano Cresci, Roberto Di Pietro, Marinella Petrocchi, Angelo Spognardi, and Maurizio Tesconi. 2017. Social fingerprinting: detection of spambot groups through DNA-inspired behavioral modeling. IEEE Transactions on Dependable and Secure Computing 15, 4 (2017), 561-576. -
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- Premisecitation§ THE RISE OF GROUP APPROACHES · p. 7
Botnets leave behind more traces of their automation and coordination than those left behind by sophisticated single bots.
“As such, botnets leave behind more traces of their automation and coordination than those left behind by sophisticated single bots [5].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [5] [5] Stefano Cresci, Roberto Di Pietro, Marinella Petrocchi, Angelo Spognardi, and Maurizio Tesconi. 2017. The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In The 26th International World Wide Web Conference Companion (WWW'17 Companion) . IW3C2. - source
Not checked in the free run.
- Interpretationcitation§ THE RISE OF GROUP APPROACHES · p. 7
Many group detectors are based on unsupervised or semi-supervised approaches to overcome the generalization deficiencies of supervised detectors that are limited by the availability of exhaustive and reliable training datasets.
“Finally, many group detectors are also based on unsupervised or semi-supervised approaches. Here the idea is to overcome the generalization deficiencies of supervised detectors that are severely limited by the availability of exhaustive and reliable training datasets [11].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [11] [11] Juan Echeverrìa, Emiliano De Cristofaro, Nicolas Kourtellis, Ilias Leontiadis, Gianluca Stringhini, and Shi Zhou. 2018. LOBO: Evaluation of generalization deficiencies in Twitter bot classifiers. In The 34th Annual Computer Security Applications Conference (ACSAC'18) . ACM, 137-146. -
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- Premisecitation§ THE RISE OF GROUP APPROACHES · p. 7
Bots act in coordination with other bots, forming botnets to amplify their effects.
“The rationale for this design choice is that bots act in coordination with other bots, forming botnets to amplify their effects [40].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [40] [40] Jinxue Zhang, Rui Zhang, Yanchao Zhang, and Guanhua Yan. 2016. The rise of social botnets: Attacks and countermeasures. IEEE Transactions on Dependable and Secure Computing 15, 6 (2016), 1068-1082.
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- Premisecitation§ THE RISE OF GROUP APPROACHES · p. 7
In 2018, Facebook and Twitter acknowledged the importance of focusing on coordinated inauthentic behaviors.
“In 2018 - roughly 5 years after the emergence of the group approach to bot detection also Facebook 9 and Twitter 10 acknowledged the importance of focusing on 'coordinated inauthentic behaviors' .”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [9] [9] Stefano Cresci, Marinella Petrocchi, Angelo Spognardi, and Stefano Tognazzi. 2019. Better Safe Than Sorry: An Adversarial Approach to Improve Social Bot Detection. In The 11th International Conference on Web Science (WebSci'19) . ACM. - source
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- cites [10] [10] Giovanni Da San Martino, Stefano Cresci, Alberto Barrón-Cedeño, Seunghak Yu, Roberto Di Pietro, and Preslav Nakov. 2020. A survey on computational propaganda detection. In The 29th International Joint Conference on Artificial Intelligence (IJCAI'20) . -
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- Premisecitation§ THE ISSUE OF BOT EVOLUTION · p. 7
A third wave of bots spread through online social networks from 2016 onwards.
“Time went by and new studies acknowledged the rise of a third wave of bots that spread through online social networks from 2016 onwards [5, 13], as shown in panel C of Figure 4.”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [5] [5] Stefano Cresci, Roberto Di Pietro, Marinella Petrocchi, Angelo Spognardi, and Maurizio Tesconi. 2017. The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In The 26th International World Wide Web Conference Companion (WWW'17 Companion) . IW3C2. - source
Not checked in the free run.
- cites [13] [13] Emilio Ferrara, Onur Varol, Clayton Davis, Filippo Menczer, and Alessandro Flammini. 2016. The rise of social bots. Commun. ACM 59, 7 (2016), 96-104. - source
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- Interpretationreasoning§ THE ISSUE OF BOT EVOLUTION · p. 7
The naïve assumption of early supervised bot detection approaches that bots are clearly separable from legitimate accounts is no longer valid.
“Ultimately, the findings about the evolution of online automation and deception tell us that the naïve assumption of early, supervised bot detection approaches - according to which bots are clearly separable from legitimate accounts - is no longer valid.”
Quote in the paperSubstantial claim - Interpretationnone given§ OPEN CHALLENGES AND THE WAY AHEAD · p. 14
Inauthentic coordination is oblivious to the different types of bad actors.
“Moreover, it is oblivious to the different types of bad actors.”
Quote in the paperSubstantial claim - Premisecitation§ A GLIMPSE INTO THE FUTURE OF DECEPTION DETECTION · p. 10
When the stationarity and neutrality assumptions are violated, algorithms yield unreliable predictions that result in dramatically decreased performances.
“When the stationarity and neutrality assumptions are violated, algorithms yield unreliable predictions that result in dramatically decreased performances [15].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [15] [15] Ian Goodfellow, Patrick McDaniel, and Nicolas Papernot. 2018. Making machine learning robust against adversarial inputs. Commun. ACM 61, 7 (2018). - source
Not checked in the free run.
- Interpretationreasoning§ A GLIMPSE INTO THE FUTURE OF DECEPTION DETECTION · p. 11
The very same algorithms relied upon for a decade, for which excellent detection results were reported in studies, are actually seeing their chances to detect bots in-the-wild severely limited.
“As a consequence, the very same algorithms that we have been relying upon for a decade, and for which we reported excellent detection results in our studies, are actually seeing their chances to detect bots in-the-wild severely limited.”
Quote in the paperSubstantial claim - Interpretationreasoning§ A GLIMPSE INTO THE FUTURE OF DECEPTION DETECTION · p. 10
Bad actors such as bots, cyborgs and trolls benefit from a large time span during which they are essentially free to tamper with online environments.
“Bad actors such as bots, cyborgs and trolls thus benefit from a large time span - the time needed to design, develop, and deploy a new effective detector - during which they are essentially free to tamper with our online environments.”
Quote in the paperSubstantial claim - Interpretationreasoning§ THE RISE OF GROUP APPROACHES · p. 9
A large cluster of accounts with highly similar behaviors might indicate the presence of a botnet, even in the absence of explicit connections between the accounts.
“Consequently, a large cluster of accounts with highly similar behaviors might indicate the presence of a botnet, even in the absence of explicit connections between the accounts.”
Quote in the paperSubstantial claim - Interpretationreasoning§ A GLIMPSE INTO THE FUTURE OF DECEPTION DETECTION · p. 10
The stationarity assumption is violated by the mechanism of bot evolution that results in accounts exhibiting different behaviors and characteristics over time.
“The stationarity assumption is violated by the mechanism of bot evolution that results in accounts exhibiting different behaviors and characteristics over time.”
Quote in the paperSubstantial claim - Premisecitation§ A GLIMPSE INTO THE FUTURE OF DECEPTION DETECTION · p. 11
In adversarial bot detection, scholars experiment with meaningful adversarial examples with which they extensively test the capabilities of current bot detectors.
“In the so-called adversarial bot detection , scholars experiment with meaningful adversarial examples with which they extensively test the capabilities of current bot detectors [9].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [9] [9] Stefano Cresci, Marinella Petrocchi, Angelo Spognardi, and Stefano Tognazzi. 2019. Better Safe Than Sorry: An Adversarial Approach to Improve Social Bot Detection. In The 11th International Conference on Web Science (WebSci'19) . ACM. - source
Not checked in the free run.
- Interpretationreasoning§ A GLIMPSE INTO THE FUTURE OF DECEPTION DETECTION · p. 10
The driving factor for the development of new and better detectors has always been bot mischiefs.
“Hence, the driving factor for the development of new and better detectors have always been bot mischiefs.”
Quote in the paperSubstantial claim - Premisecitation§ THE RISE OF GROUP APPROACHES · p. 9
Coordinated and synchronized behaviors appear as near-fully connected communities in graphs, dense blocks in adjacency matrices, or peculiar patterns in spectral subspaces.
“Coordinated and synchronized behaviors appear as near-fully connected communities in graphs, dense blocks in adjacency matrices, or peculiar patterns in spectral subspaces [21].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [21] [21] Meng Jiang, Peng Cui, Alex Beutel, Christos Faloutsos, and Shiqiang Yang. 2016. Inferring lockstep behavior from connectivity pattern in large graphs. Knowledge and Information Systems 48, 2 (2016), 399-428. - source
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- Interpretationreasoning§ A GLIMPSE INTO THE FUTURE OF DECEPTION DETECTION · p. 10
The lag between observations and countermeasures possibly explains why the influence of bots and other bad actors on online discussions did not seem to decrease despite increasing detection techniques.
“This lag between observations and countermeasures possibly explains the current situation with our online social ecosystems: Despite the increasing number of existing detection techniques, the influence of bots and other bad actors on our online discussions did not seem to decrease.”
Quote in the paperSubstantial claim - Premisecitation§ THE RISE OF GROUP APPROACHES · p. 9
Human behaviors are intrinsically more heterogeneous than automated ones.
“The rationale behind this approach is based on evidence suggesting that human behaviors are intrinsically more heterogeneous than automated ones [7].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [7] [7] Stefano Cresci, Roberto Di Pietro, Marinella Petrocchi, Angelo Spognardi, and Maurizio Tesconi. 2020. Emergent properties, models, and laws of behavioral similarities within groups of Twitter users. Computer Communications 150 (2020), 47-61. - source
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- Interpretationreasoning§ A GLIMPSE INTO THE FUTURE OF DECEPTION DETECTION · p. 10
Scholars and OSN administrators are constantly one step behind malicious account developers.
“In other words, scholars and OSN administrators are constantly one step behind of malicious account developers .”
Quote in the paperSubstantial claim - Interpretationreasoning§ A GLIMPSE INTO THE FUTURE OF DECEPTION DETECTION · p. 10
Improvements in the detection of bad actors typically occur only some time after having collected evidence of new mischiefs.
“A major implication of this approach is that improvements in the detection of bad actors typically occur only some time after having collected evidence of new mischiefs.”
Quote in the paperSubstantial claim - Premisecitation§ A GLIMPSE INTO THE FUTURE OF DECEPTION DETECTION · p. 10
The vast majority of machine learning algorithms are designed for operating within environments that are stationary and neutral, if not even benign.
“The vast majority of machine learning algorithms are designed for operating within environments that are stationary and neutral, if not even benign.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [15] [15] Ian Goodfellow, Patrick McDaniel, and Nicolas Papernot. 2018. Making machine learning robust against adversarial inputs. Commun. ACM 61, 7 (2018). - source
Not checked in the free run.
- Interpretationreasoning§ THE RISE OF GROUP APPROACHES · p. 10
The overall approach to the task of bot detection can have serious repercussions on detection performance.
“As the switch from individual to group detectors demonstrates, the overall approach to the task of bot detection can have serious repercussions on detection performance.”
Quote in the paperSubstantial claim
- Classified as probably not a substantial claim. 49
- May combine two claims. 33
Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.
Findingnone given§ THE RISE OF GROUP APPROACHES · p. 9All techniques based on the analysis of the textual content of posted messages are supervised detectors that analyze individual accounts.
“Contrarily, all techniques based on the analysis of the textual content of posted messages, such as those works that exclusively employ natural language processing techniques, are supervised detectors that analyze individual accounts.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingcitation§ THE RISE OF GROUP APPROACHES · p. 9The number of new supervised detectors has been constantly increasing since 2012, despite their severe generalization issues.
“Instead, the number of new supervised detectors has been constantly increasing since 2012, despite their severe generalization issues [11].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [11] [11] Juan Echeverrìa, Emiliano De Cristofaro, Nicolas Kourtellis, Ilias Leontiadis, Gianluca Stringhini, and Shi Zhou. 2018. LOBO: Evaluation of generalization deficiencies in Twitter bot classifiers. In The 34th Annual Computer Security Applications Conference (ACSAC'18) . ACM, 137-146. -
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May combine two claims.
Findingnone given§ THE DAWN OF SOCIAL BOT DETECTION · p. 5A real ground truth dataset for training supervised bot detectors is lacking in most cases, and labels are simply given by human operators that manually analyze the data.
“In most cases, a real ground truth is lacking and the labels are simply given by human operators that manually analyze the data.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown data§ THE RISE OF GROUP APPROACHES · p. 9The adoption of unsupervised machine learning started in 2013 with the rise of group approaches and now appears to be stationary.
“The adoption of unsupervised machine learning started in 2013 with the rise of group approaches, and now appears to be stationary.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingown data§ THE SOCIAL BOT PANDEMIC · p. 4Nowadays, new studies on the characterization, detection and impact estimation of bots are published at an impressive rate.
“Nowadays, new studies on the characterization, detection and impact estimation of bots are published at an impressive rate, as shown in Figure 2.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingnone given§ THE DAWN OF SOCIAL BOT DETECTION · p. 4In the early days, the vast majority of attempts at bot detection were based on supervised machine learning and on the analysis of individual accounts.
“In the early days, the vast majority of attempts at bot detection featured two distinctive characteristics: (i) they were based on supervised machine learning, and (ii) on the analysis of individual accounts.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Findingnone given§ A GLIMPSE INTO THE FUTURE OF DECEPTION DETECTION · p. 10A shift took place from individual to group detectors in an effort to contrast social bot evolution.
“So far, we highlighted that a shift took place from individual to group detectors in an effort to contrast social bot evolution.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim. May combine two claims.
Findingreasoning§ OPEN CHALLENGES AND THE WAY AHEAD · p. 14Among the most pressing challenges along this line of research is the problem of scalability of group-based detectors and the intrinsic fuzziness of 'inauthentic coordination'.
“Among the most pressing challenges along this line of research is the problem of scalability of group-based detectors and the intrinsic fuzziness of 'inauthentic coordinaton'.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S4 (based).
Findingnone given§ OPEN CHALLENGES AND THE WAY AHEAD · p. 14Computational means to discriminate between authentic and inauthentic coordination are yet to be proposed and evaluated.
“Similarly, computational means to discriminate between authentic and inauthentic coordination are yet to be proposed and evaluated.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingcitation§ OPEN CHALLENGES AND THE WAY AHEAD · p. 14The scalable and generalizable detection of coordination is still a largely open challenge, with only few contributions proposed so far.
“In fact, the scalable and generalizable detection of coordination is still a largely open challenge, with only few contributions proposed so far [12, 25].”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [12] [12] Mohd Fazil and Muhammad Abulaish. 2020. A socialbots analysis-driven graph-based approach for identifying coordinated campaigns in Twitter. Journal of Intelligent & Fuzzy Systems 38 (2020), 2961-2977. Issue 3. - source
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- cites [25] [25] Diogo Pacheco, Pik-Mai Hui, Christopher Torres-Lugo, Bao Tran Truong, Alessandro Flammini, and Filippo Menczer. 2020. Uncovering Coordinated Networks on Social Media. arXiv preprint arXiv:2001.05658 (2020). -
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Findingreasoning§ THE RISE OF GROUP APPROACHES · p. 10Scientific communities tend to favor and stick to a specific approach to bot detection.
“At the same time, some scientific communities tend to favor and stick to a specific approach.”
Quote in the paperSubstantial claimMay combine two claims.
Findingnone given§ OPEN CHALLENGES AND THE WAY AHEAD · p. 14Unsupervised detectors emerged in 2012-13 and shifted the target to groups of misbehaving accounts.
“Unsupervised detectors emerged in 2012-13 and shifted the target to groups of misbehaving accounts.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S4 (based).
Findingreasoning§ OPEN CHALLENGES AND THE WAY AHEAD · p. 13These challenges currently stand as largely unsolved.
“These challenges currently stand as largely unsolved, and call for the highest effort of our scientific community.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Findingnone given§ OPEN CHALLENGES AND THE WAY AHEAD · p. 13Challenges for creating reference datasets include the limited availability of data itself, missing or ambiguous ground truth, and the obsolescence of existing datasets that hardly cope with the rapid evolution of malicious accounts.
“Here, challenges include the limited availability of data itself, missing or ambiguous ground truth and the obsolescence of existing datasets that hardly cope with the rapid evolution of malicious accounts.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingcitation§ A GLIMPSE INTO THE FUTURE OF DECEPTION DETECTION · p. 12A GAN trained in [33] for creating adversarial examples of social bots improved the training of downstream detectors.
“A notable example of this kind is represented by the GAN trained in [33] for creating adversarial examples of social bots that improved the training of downstream detectors.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [33] [33] Bin Wu, Le Liu, Yanqing Yang, Kangfeng Zheng, and Xiujuan Wang. 2020. Using Improved Conditional Generative Adversarial Networks to Detect Social Bots on Twitter. IEEE Access 8 (2020), 36664-36680. - source
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Findingreasoning§ THE RISE OF GROUP APPROACHES · p. 10Works published within the natural language processing community focus on textual content, resulting in a multitude of supervised classifiers that analyze accounts individually and yield binary labels.
“For instance, works published within the natural language processing community, quite naturally focus on textual content, thus resulting in a multitude of supervised classifiers that analyze accounts individually and that yield binary labels.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingcitation§ A GLIMPSE INTO THE FUTURE OF DECEPTION DETECTION · p. 12Initial efforts towards adversarial bot detection were driven by the creativity of some researchers and only covered few cases with limited applicability.
“The previous analysis highlights that initial efforts towards adversarial bot detection were driven by the creativity of some researchers and only covered few cases with limited applicability [9, 17].”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [9] [9] Stefano Cresci, Marinella Petrocchi, Angelo Spognardi, and Stefano Tognazzi. 2019. Better Safe Than Sorry: An Adversarial Approach to Improve Social Bot Detection. In The 11th International Conference on Web Science (WebSci'19) . ACM. - source
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- cites [17] [17] Christian Grimme, Dennis Assenmacher, and Lena Adam. 2018. Changing perspectives: Is it sufficient to detect social bots?. In The 10th International Conference on Social Computing and Social Media (SCSM'18) . - source
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Findingreasoning§ A GLIMPSE INTO THE FUTURE OF DECEPTION DETECTION · p. 12Adversarial bot detection research is still in its infancy and is still lagging behind more traditional approaches.
“Despite the high hopes placed on adversarial approaches for detecting deception and automation, this research direction is still in its infancy and, probably due to its recency, is still lagging behind more traditional approaches.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Findingcitation§ A GLIMPSE INTO THE FUTURE OF DECEPTION DETECTION · p. 12This paradigm has never been applied to the task of social bot detection, but it was tested with promising results for related tasks, such as fake news generation/detection.
“This paradigm has never been applied to the task of social bot detection, but it was tested with promising results for related tasks, such as that of fake news generation/detection [39].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [39] [39] Rowan Zellers, Ari Holtzman, Hannah Rashkin, Yonatan Bisk, Ali Farhadi, Franziska Roesner, and Yejin Choi. 2019. Defending against neural fake news. In The 33rd Conference on Neural Information Processing Systems (NeurIPS'19) . 9051-9062. -
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Findingnone given§ OPEN CHALLENGES AND THE WAY AHEAD · p. 13One aspect that is often overlooked when evaluating bot detectors is their generalizability - that is, their capacity of achieving good detection results also for types of bots that have not been originally considered.
“One aspect that is often overlooked when evaluating bot detectors is their generalizability - that is, their capacity of achieving good detection results also for types of bots that have not been originally considered.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Findingnone given§ THE RISE OF GROUP APPROACHES · p. 10The complex networks community favors graph-based approaches.
“In contrast, the complex networks community favors graph-based approaches.”
Quote in the paperNot a substantial claimMay combine two claims.
Premisecitation§ ACMReference Format: · p. 1A minority of social bots played a central role in spreading divisive messages and disinformation, possibly contributing to Trump's victory in the 2016 US Presidential elections.
“Today, we know that a minority of social bots - automated social media accounts mimicking humans - played a central role in spreading divisive messages and disinformation, possibly contributing to Trump's victory [16, 19].”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [16] [16] Yuriy Gorodnichenko, Tho Pham, and Oleksandr Talavera. 2018. Social Media, Sentiment and Public Opinions: Evidence from #Brexit and #USElection . Working Paper 24631. National Bureau of Economic Research. -
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- cites [19] [19] Nir Grinberg, Kenneth Joseph, Lisa Friedland, Briony Swire-Thompson, and David Lazer. 2019. Fake news on Twitter during the 2016 US presidential election. Science 363, 6425 (2019), 374-378. - source
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Premisecitation§ THE SOCIAL BOT PANDEMIC · p. 3The average presence of bots was estimated to be around 11% of all Facebook accounts in 2019.
“and 11% of all Facebook accounts in 2019 [38]”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [38] [38] Mattia Zago, Pantaleone Nespoli, Dimitrios Papamartzivanos, Manuel Gil Perez, Felix Gomez Marmol, Georgios Kambourakis, and Gregorio Martinez Perez. 2019. Screening Out Social Bots Interference: Are There Any Silver Bullets? IEEE Communications Magazine 57, 8 (2019), 98-104. - source
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Premisecitation§ THE SOCIAL BOT PANDEMIC · p. 3The average presence of bots was estimated to be around 15% of all active Twitter accounts in 2017.
“The average presence of bots was estimated to be in the region of 15% of all active Twitter accounts in 2017 [31]”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [31] [31] Onur Varol, Emilio Ferrara, Clayton A Davis, Filippo Menczer, and Alessandro Flammini. 2017. Online human-bot interactions: Detection, estimation, and characterization. In The 11th International Conference on Web and Social Media (ICWSM'17) . AAAI. -
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Interpretationreasoning§ ACMReference Format: · p. 3From 2021 there will be more than one new paper published per day on social bots.
“We forecast that from 2021 there will be more than 1 new paper published per day on social bots, which poses a heavy burden on those trying to keep pace with the evolution of this thriving field.”
Quote in the paperNot a substantial claim- The authors state this as an assumption: The skyrocketing publication trend on social bots will continue.
Probably not a substantial claim: may not pass S3 (load-bearing).
Premisereasoning§ THE SOCIAL BOT PANDEMIC · p. 3The field still lacks a precise and well-agreed definition of what a social bot is.
“Yet, we still lack a precise and well-agreed definition of what a social bot is.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Premisecitation§ THE SOCIAL BOT PANDEMIC · p. 3Social bots are actively used for both beneficial and nefarious purposes.
“Social bots are actively used for both beneficial and nefarious purposes [13].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [13] [13] Emilio Ferrara, Onur Varol, Clayton Davis, Filippo Menczer, and Alessandro Flammini. 2016. The rise of social bots. Commun. ACM 59, 7 (2016), 96-104. - source
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Premisecitation§ THE SOCIAL BOT PANDEMIC · p. 3Bots played a role in strategic information operations orchestrated ahead of numerous worldwide events.
“Other studies specifically focused on political activity, concluding that bots played a role in strategic information operations orchestrated ahead of numerous worldwide events, as shown in Figure 1.”
Quote in the paperNot a substantial claimMay combine two claims.
Premisecitation§ THE SOCIAL BOT PANDEMIC · p. 3Bots are present in online cryptocurrency discussions and in the infodemics about the COVID-19 pandemic.
“Similar results were obtained about the presence of bots in online cryptocurrency discussions [24] and as part of the 'infodemics' about the COVID-19 pandemic [14].”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [24] [24] Leonardo Nizzoli, Serena Tardelli, Marco Avvenuti, Stefano Cresci, Maurizio Tesconi, and Emilio Ferrara. 2020. Charting the landscape of online cryptocurrency manipulation. IEEE Access (2020). https://doi.org/10.1109/ACCESS.2020.3003370 - source
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- cites [14] [14] Riccardo Gallotti, Francesco Valle, Nicola Castaldo, Pierluigi Sacco, and Manlio De Domenico. 2020. Assessing the risks of 'infodemics' in response to COVID-19 epidemics. arXiv preprint arXiv:2004.03997 (2020). - source
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Premisecitation§ THE SOCIAL BOT PANDEMIC · p. 3A 2019 study reported that 71% of Twitter users mentioning trending US stocks are likely to be bots.
“A 2019 study reported that 71% of Twitter users mentioning trending US stocks, are likely to be bots [8].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [8] [8] Stefano Cresci, Fabrizio Lillo, Daniele Regoli, Serena Tardelli, and Maurizio Tesconi. 2019. Cashtag piggybacking: Uncovering spam and bot activity in stock microblogs on Twitter. ACM Transactions on the Web (TWEB) 13, 2 (2019), 11. - source
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Interpretationreasoning§ THE ISSUE OF BOT EVOLUTION · p. 7Cyborgs exist and operate halfway between traditional concepts of bots and humans, resulting in weakened distinctions and overlapping behaviors between the two.
“These cyborgs exist and operate halfway between the traditional concepts of bots and humans, resulting in weakened distinctions and overlapping behaviors between the two.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Premisecitation§ THE SOCIAL BOT PANDEMIC · p. 3By 2018, scholars found more than 40,000 public bot repositories.
“In fact, by 2018, scholars found more than 40,000 public bot repositories [1].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [1] [1] Dennis Assenmacher, Lena Adam, Lena Frischlich, Heike Trautmann, and Christian Grimme. 2019. Openbots. arXiv preprint arXiv:1902.06691 (2019). -
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Premisecitation§ THE SOCIAL BOT PANDEMIC · p. 3By 2016, there were more than 4,000 GitHub repositories containing code for deploying Twitter bots.
“Bence Kollanyi reported an exponential growth that led in 2016 to more than 4,000 GitHub repositories containing code for deploying Twitter bots [22].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [22] [22] Bence Kollanyi. 2016. Where Do Bots Come From? An Analysis of Bot Codes Shared on GitHub. International Journal of Communication 10 (2016), 20. -
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Interpretationreasoning§ THE SOCIAL BOT PANDEMIC · p. 3Social bots are among the weapons of choice for deceiving and manipulating crowds.
“The looming picture is one where social bots are among the weapons of choice for deceiving and manipulating crowds.”
Quote in the paperNot a substantial claimMay combine two claims.
Premisecitation§ THE ISSUE OF BOT EVOLUTION · p. 7Hundreds of tech-savvy social media users were able to tell apart newer bots from legitimate users only 24% of the times, but could spot older bots 91% of the times.
“Moreover, hundreds of tech-savvy social media users that participated in a crowdsourcing experiment were able to tell apart newer bots from legitimate users only 24% of the times. The same users were instead able of spotting older bots 91% of the times [5].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [5] [5] Stefano Cresci, Roberto Di Pietro, Marinella Petrocchi, Angelo Spognardi, and Maurizio Tesconi. 2017. The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In The 26th International World Wide Web Conference Companion (WWW'17 Companion) . IW3C2. - source
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Interpretationnone given§ THE RISE OF GROUP APPROACHES · p. 9The plateau hit by unsupervised approaches co-occurred with the rise of adversarial ones, which might take their place in the coming years.
“Interestingly, the plateau hit by unsupervised approaches co-occurred with the rise of adversarial ones, which might take their place in the coming years.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Premisecitation§ THE DAWN OF SOCIAL BOT DETECTION · p. 4Cresci et al. developed supervised machine learning classifiers for detecting fake followers.
“For example, Cresci et al. developed a set of supervised machine learning classifiers for detecting so-called fake followers, a type of automated accounts commonly used to artificially boost the popularity of the public characters that buy them [4].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [4] [4] Stefano Cresci, Roberto Di Pietro, Marinella Petrocchi, Angelo Spognardi, and Maurizio Tesconi. 2015. Fame for sale: Efficient detection of fake Twitter followers. Decision Support Systems 80 (2015), 56-71. - source
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Premisecitation§ THE DAWN OF SOCIAL BOT DETECTION · p. 4Fake followers can be bought for as low as 12$ per 1,000 followers in the surface Web.
“Fake followers can be bought for as low as 12$ per 1,000 followers in the surface Web.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [5] [5] Stefano Cresci, Roberto Di Pietro, Marinella Petrocchi, Angelo Spognardi, and Maurizio Tesconi. 2017. The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In The 26th International World Wide Web Conference Companion (WWW'17 Companion) . IW3C2. - source
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Interpretationcitation§ THE ISSUE OF BOT EVOLUTION · p. 7Malicious accounts can leverage deepfake texts, profile pictures, and videos, which is worrying and worthy of increased attention.
“Indeed, the possibility for malicious accounts to leverage deepfake texts, profile pictures, and videos is worrying, and worthy of increased attention [10].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [10] [10] Giovanni Da San Martino, Stefano Cresci, Alberto Barrón-Cedeño, Seunghak Yu, Roberto Di Pietro, and Preslav Nakov. 2020. A survey on computational propaganda detection. In The 29th International Joint Conference on Artificial Intelligence (IJCAI'20) . -
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Premisecitation§ THE DAWN OF SOCIAL BOT DETECTION · p. 5Botometer evaluates possible bots using more than 1,200 features of an account.
“By leveraging more than 1,200 features of an account, Botometer evaluates possible bots based on their profile characteristics, social network structure, the content they produce, their sentiment expressions, and the timings of their actions [35].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [35] [35] Kai-Cheng Yang, Onur Varol, Clayton A Davis, Emilio Ferrara, Alessandro Flammini, and Filippo Menczer. 2019. Arming the public with artificial intelligence to counter social bots. Human Behavior and Emerging Technologies 1, 1 (2019), 48-61. - source
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Premisecitation§ THE ISSUE OF BOT EVOLUTION · p. 6The social bots studied by Chao Yang et al. appeared as more popular and credible, given the relatively large number of their social connections, and were no longer spamming the same messages over and over again.
“Conversely, the social bots studied by Chao Yang et al. appeared as more popular and credible, given the relatively large number of their social connections. In addition, they were no longer spamming the same messages over and over again.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [34] [34] Chao Yang, Robert Harkreader, and Guofei Gu. 2013. Empirical evaluation and new design for fighting evolving twitter spammers. IEEE Transactions on Information Forensics and Security 8, 8 (2013), 1280-1293. - source
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Interpretationreasoning§ THE DAWN OF SOCIAL BOT DETECTION · p. 6Early bot detection approaches typically output binary classifications, which cannot account for malicious accounts that feature a mixture of automated and human-driven behaviors.
“Furthermore, these approaches typically output binary classifications. In many cases however, malicious accounts feature a mixture of automated and human-driven behaviors that cannot be accounted for with simple binary labels.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Interpretationreasoning§ THE RISE OF GROUP APPROACHES · p. 7By analyzing large groups of accounts, detectors also have access to more data for fueling powerful AI algorithms.
“In addition, by analyzing large groups of accounts, detectors also have access to more data for fueling powerful - yet data-hungry - AI algorithms.”
Quote in the paperNot a substantial claimMay combine two claims.
Interpretationreasoning§ THE ISSUE OF BOT EVOLUTION · p. 6Initial success at social bot detection forced bot developers to put in place sophisticated countermeasures, leading to newer bots that are harder to detect.
“Initial success at social bot detection forced bot developers to put in place sophisticated countermeasures. Because of this, newer bots often feature advanced characteristics that make them way harder to detect with respect to older ones.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based). May combine two claims.
Interpretationreasoning§ THE ISSUE OF BOT EVOLUTION · p. 7Dichotomous classifications such as human vs bot, fake vs real, coordinated vs not coordinated might represent oversimplifications, unable to grasp the complexity of these phenomena and unlikely to yield accurate and actionable results.
“Within this global picture, dichotomous classifications - such as human vs bot, fake vs real, coordinated vs not coordinated - might represent oversimplifications, unable to grasp the complexity of these phenomena and unlikely to yield accurate and actionable results.”
Quote in the paperNot a substantial claimMay combine two claims.
Premisecitation§ THE ISSUE OF BOT EVOLUTION · p. 6The first wave of social bots that populated OSNs until around 2011 was made of rather simplistic bots with few social connections and posted messages and clear signs of automation.
“The first wave of social bots that populated OSNs until around 2011 was made of rather simplistic bots. Accounts with very low reputation due to few social connections and posted messages, and featuring clear signs of automation as shown in panel A of Figure 4.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [34] [34] Chao Yang, Robert Harkreader, and Guofei Gu. 2013. Empirical evaluation and new design for fighting evolving twitter spammers. IEEE Transactions on Information Forensics and Security 8, 8 (2013), 1280-1293. - source
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Study design and conductown data§ THE RISE OF GROUP APPROACHES · p. 9The paper surveyed more than 230 papers that proposed a bot detection technique and manually classified each detector along two orthogonal dimensions.
“We surveyed more than 230 papers that proposed a bot detection technique and we manually classified each detector along two orthogonal dimensions.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Interpretationreasoning§ THE ISSUE OF BOT EVOLUTION · p. 7The social bot pandemic gradually became much more difficult to stop.
“The social bot pandemic gradually became much more difficult to stop.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Premisecitation§ THE SOCIAL BOT PANDEMIC · p. 3Facebook, 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.”
Quote in the paperNot a substantial claim0 of 3 sources checked- cites [2] [2] Nikan Chavoshi, Hossein Hamooni, and Abdullah Mueen. 2016. DeBot: Twitter bot detection via warped correlation. In The 16th International Conference on Data Mining (ICDM'16) . IEEE, 817-822. - source
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- cites [3] [3] Zi Chu, Steven Gianvecchio, Haining Wang, and Sushil Jajodia. 2012. Detecting automation of Twitter accounts: Are you a human, bot, or cyborg? IEEE Transactions on Dependable and Secure Computing 9, 6 (2012), 811-824. - source
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- cites [4] [4] Stefano Cresci, Roberto Di Pietro, Marinella Petrocchi, Angelo Spognardi, and Maurizio Tesconi. 2015. Fame for sale: Efficient detection of fake Twitter followers. Decision Support Systems 80 (2015), 56-71. - source
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Premisecitation§ THE ISSUE OF BOT EVOLUTION · p. 7Only 5% of newer bots are removed from social platforms, whereas older ones are removed 60% of the times.
“Results showed that only 5% of newer bots are removed from social platforms, whereas older ones are removed 60% of the times [5].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [5] [5] Stefano Cresci, Roberto Di Pietro, Marinella Petrocchi, Angelo Spognardi, and Maurizio Tesconi. 2017. The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In The 26th International World Wide Web Conference Companion (WWW'17 Companion) . IW3C2. - source
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May combine two claims.
Premisereasoning§ THE RISE OF GROUP APPROACHES · p. 7The existence of botnets does not necessarily imply that accounts are explicitly connected in the social network, but rather that they are maneuvered by a single entity and that they share common goals.
“The existence of botnets does not necessarily imply that accounts are explicitly connected in the social network, but rather that they are maneuvered by a single entity and that time they share common goals.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).
Interpretationreasoning§ A GLIMPSE INTO THE FUTURE OF DECEPTION DETECTION · p. 12The adversarial framework sketched in Figure 6 is general enough to be applied to a wide set of deception detection tasks, comprising the detection of social bots, cyborgs, trolls and mis/disinformation.
“The adversarial framework sketched in Figure 6 is general enough to be applied to a wide set of deception detection tasks, comprising the detection of social bots, cyborgs, trolls and mis/disinformation.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).
Interpretationreasoning§ A GLIMPSE INTO THE FUTURE OF DECEPTION DETECTION · p. 12Efforts at adversarial detection can only be successful if the scientific community decides to rise to the many open challenges.
“As such, efforts at adversarial detection can only be successful if the scientific community decides to rise to the many open challenges.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Premisecitation§ A GLIMPSE INTO THE FUTURE OF DECEPTION DETECTION · p. 12Generative adversarial networks (GANs) are a powerful machine learning framework where two competing deep learning networks are jointly trained in a game-theoretic setting.
“Generative adversarial networks (GANs) are a powerful machine learning framework where two competing deep learning networks are jointly trained in a game-theoretic setting [15].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [15] [15] Ian Goodfellow, Patrick McDaniel, and Nicolas Papernot. 2018. Making machine learning robust against adversarial inputs. Commun. ACM 61, 7 (2018). - source
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Interpretationreasoning§ A GLIMPSE INTO THE FUTURE OF DECEPTION DETECTION · p. 11The adversarial paradigm accounts for adversaries by design, thus providing higher guarantees for deception detection, which violates the stationarity and neutrality assumptions.
“In addition, this paradigm accounts for adversaries by design , thus providing higher guarantees for deception detection, which violates the stationarity and neutrality assumptions.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).
Interpretationreasoning§ OPEN CHALLENGES AND THE WAY AHEAD · p. 14Inauthentic coordination is an important piece of the deception puzzle, since it is exploited by bad actors for obtaining visibility and impact.
“Inauthentic coordination is an important piece of the deception puzzle, since it is exploited by bad actors for obtaining visibility and impact.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).
Interpretationnone given§ A GLIMPSE INTO THE FUTURE OF DECEPTION DETECTION · p. 11All tasks related to the detection of online deception, manipulation and automation are intrinsically adversarial.
“All tasks related to the detection of online deception, manipulation and automation are intrinsically adversarial .”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Interpretationreasoning§ A GLIMPSE INTO THE FUTURE OF DECEPTION DETECTION · p. 11Finding good adversarial examples can help scholars understand the weaknesses of existing bot detection systems.
“Finding good adversarial examples can help scholars understand the weaknesses of existing bot detection systems.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Premisecitation§ A GLIMPSE INTO THE FUTURE OF DECEPTION DETECTION · p. 11Adversarial machine learning is a paradigm specifically devised for application in scenarios presenting adversaries motivated in fooling learned models.
“Adversarial machine learning is a paradigm specifically devised for application in those scenarios presenting adversaries motivated in fooling learned models [15].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [15] [15] Ian Goodfellow, Patrick McDaniel, and Nicolas Papernot. 2018. Making machine learning robust against adversarial inputs. Commun. ACM 61, 7 (2018). - source
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Interpretationreasoning§ A GLIMPSE INTO THE FUTURE OF DECEPTION DETECTION · p. 12In contrast with existing adversarial approaches for bot detection, the GAN-based framework is grounded on an established and successful machine learning framework, rather than on ad-hoc solutions lacking broad applicability.
“Furthermore, in contrast with existing adversarial approaches for bot detection, it is grounded on an established and successful machine learning framework, rather than on ad-hoc solutions lacking broad applicability.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable).
Interpretationreasoning§ OPEN CHALLENGES AND THE WAY AHEAD · p. 13Combining the two evaluation dimensions (generalization across bot types and across bot versions) would allow a much more reliable assessment of the detection capabilities of present and future techniques, thus avoiding overestimates of detection performance.
“Combining these two evaluation dimensions, thus extensively exploring the generalizability space, would allow a much more reliable assessment of the detection capabilities of present and future techniques, thus avoiding overestimates of detection performance.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing). May combine two claims.
Interpretationreasoning§ A GLIMPSE INTO THE FUTURE OF DECEPTION DETECTION · p. 11Bot hunters need not wait anymore for new bot mischiefs in order to adapt their techniques, but instead they can proactively test them, in an effort that could quickly make them more robust.
“As a result, bot hunters need not wait anymore for new bot mischiefs in order to adapt their techniques, but instead they can proactively (instead of reactively) test them, in an effort that could quickly make them more robust.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).
Interpretationreasoning§ A GLIMPSE INTO THE FUTURE OF DECEPTION DETECTION · p. 11Early detection of vulnerabilities can contribute to the development of more robust detection systems.
“Early detection of vulnerabilities can in turn contribute to the development of more robust detection systems.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S4 (based). May combine two claims.
Interpretationreasoning§ OPEN CHALLENGES AND THE WAY AHEAD · p. 14Thanks to the rise of AI-enabled deception techniques such as deepfakes, the most sophisticated malicious actors are bound to become indistinguishable from one another, and likely also from legitimate accounts.
“Now, thanks to the rise of AI-enabled deception techniques such as deepfakes , the most sophisticated of these malicious actors are bound to become indistinguishable from one another, and likely also from legitimate accounts.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Interpretationown data§ OPEN CHALLENGES AND THE WAY AHEAD · p. 14The same analysis that anticipated worldwide interest in inauthentic coordination now suggests that adversarial approaches might give an edge in the fight against online deception.
“Interestingly, the same analysis that anticipated worldwide interest in inauthentic coordination, is now suggesting that adversarial approaches might give us an edge in the long-lasting fight against online deception.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).
Interpretationown data§ OPEN CHALLENGES AND THE WAY AHEAD · p. 12The exponentially growing body of work on social bot detection suggests that much effort is bound to be devoted in the coming years to the fight of this crucial issue.
“The exponentially growing body of work on social bot detection shown in Figure 2, somehow reassures us that much effort is bound to be devoted in the coming years to the fight of this crucial issue.”
Quote in the paperNot a substantial claimMay combine two claims.
Interpretationreasoning§ A GLIMPSE INTO THE FUTURE OF DECEPTION DETECTION · p. 10The task of social bot detection is neither stationary nor neutral.
“Notably, the task of social bot detection is neither stationary nor neutral .”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).
Interpretationnone given§ OPEN CHALLENGES AND THE WAY AHEAD · p. 13The foreseen increase in publications inevitably implies that more bot detectors will be proposed.
“Secondly, the foreseen increase in publications inevitably implies that more bot detectors will be proposed.”
Quote in the paperNot a substantial claim
The authors state this as an assumption.
The list of papers documenting political manipulation by social bots in Figure 1 is illustrative and not exhaustive.
“Although the list of papers is illustrative and not exhaustive, it nonetheless allows to map the worldwide spread of the social bot pandemic.”
The authors state this as an assumption.
The skyrocketing publication trend on social bots will continue.
“Should this skyrocketing trend continue, by 2021 there will be more than one new paper published per day”
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?
Automated accounts (bots) have been widely used on Twitter to efficiently spread disinformation.
“To efficiently spread disinformation, automated accounts, known as bots, have been widely used on Twitter [10,17].”
From Unsupervised detection of coordinated fake-follower campaigns on social media (2024) · cites this paper as [10]
THE SOCIAL BOT PANDEMIC (p. 3) describes substantial bot presence on Twitter and their use in strategic information operations, including studies reporting increased disinformation spread. It also says bots did not always have a real impact and that studies disagree about their effect, so the claim’s unqualified implication that they efficiently spread disinformation overstates the source. (LLM, conf=0.84)
Experimental — accuracy not yet measuredThese kinds of sophisticated deception can only be detected through observations at the group level.
“These kinds of sophisticated deception can only be detected through observations at the group level [16].”
From A language framework for modeling social media account behavior (2023) · cites this paper as [16]
The Rise of Group Approaches (p. 7) says analyzing coordinated groups is likely to detect sophisticated bots better than analyzing individual accounts. Figure 3’s caption (p. 4) likewise says large coordinated groups are more likely to be detected than sophisticated individual bots. Neither passage says group-level observation is the only way to detect such deception, so the claim’s exclusivity is unsupported. (LLM, conf=0.87)
Experimental — accuracy not yet measuredAs some bots evolve to mimic human profiles and behaviors better, their activities can only be detected during orchestrated operations.
“As some bots evolve to mimic human profiles and behaviors better, their activities can only be detected during orchestrated operations (Cresci, 2020).”
From Anatomy of an AI-powered malicious social botnet (2024) · cites this paper as Cresci, 2020
The Issue of Bot Evolution (p. 7) says sophisticated bots are hard to distinguish from legitimate accounts when analyzed individually. The Rise of Group Approaches (p. 7) says coordinated behavior leaves more detectable traces, but describes group analysis as likely to work better—not as the only way to detect these bots. The claim’s “can only be detected” overstates the source. (LLM, conf=0.88)
Experimental — accuracy not yet measuredA group of inauthentic accounts can be coordinated by a single entity, whether human-controlled or automated.
“A group of inauthentic accounts can be coordinated by a single entity, whether their behavior are human-controlled or automated.”
From A language framework for modeling social media account behavior (2023) · cites this paper as [16]
Automated accounts exhibit distinctive temporal, linguistic, and network signatures.
“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 [15]
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“A desirable scenario for the near future would involve the possibility to evaluate any new bot detector against many different types of social bots, thus moving along the y axis of Figure 7, following the promising approaches recently developed in [11, 36].”
BackgroundMaking machine learning robust against adversarial inputs
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“Generative adversarial networks (GANs) are a powerful machine learning framework where two competing deep learning networks are jointly trained in a game-theoretic setting [15].”
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BackgroundDeBot: Twitter Bot Detection via Warped Correlation
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MethodDisinformation’s spread: bots, trolls and all of us
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“Kate Starbird recently discussed a related issue in an inspiring piece on Nature [28].”
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RTbust: Exploiting Temporal Patterns for Botnet Detection on Twitter
Mazza, M., Cresci, S., Avvenuti, M., Quattrociocchi, W., & Tesconi, M. (2019) · Proceedings of the 10th ACM Conference on Web Science · doi:10.1145/3292522.3326015 · Cited by 163 · cited 2× · see citing works
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MethodSelf-citationCharting the Landscape of Online Cryptocurrency Manipulation
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“For instance, several group detectors implement graph-based approaches and aim at spotting suspicious account connectivity patterns [20, 24].”
BackgroundResultSelf-citationProfiling Online Social Behaviors for Compromised Account Detection
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Explainable Artificial Intelligence in CyberSecurity: A Survey
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“Since they are 1276 automated, they work considerably more quickly than actual 1277 individuals [182].”
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“Recently, a study by Carnegie Mellon University researchers has shown that among 200 million tweets discussing coronavirus, 82% of the influential re-tweeters were bots (Virginia 2020).”
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“Recent methods use representation learning based on social and interaction networks [60] or employ deep learning techniques.”
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“[3]’s investigation shows that bots in the past used to be simple and easily identified, possessing too little profile and friend information to be genuine.”
BackgroundA new sociology of humans and machines
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BackgroundRoSGAS : Adaptive Social Bot Detection with Reinforced Self-supervised GNN Architecture Search
Yang, Y., Yang, R., Li, Y., Cui, K., Yang, Z. Q., Wang, Y., Xu, J., & Xie, H. (2022) · ACM Transactions on the Web · doi:10.1145/3572403 · Cited by 67 · see citing works
“Social bots have been long studied but not yet well resolved due to fast bot evolution [7].”
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“…from simpler, low-intelligent bots who may follow some basic rules to conduct actions such as re-peatedly mentioning certain accounts (Ratkiewicz et al., 2011) to highly sophisticated ones who adopt advanced deep neural networks to generate more credible content and profile images (Cresci, 2020).”
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
“Moreover, the human-like characteristic of these bots and a continuous evolution [7, 8] in their strategy make it very difficult to distinguish them from legitimate users and thus invoke the need of more sophisticated and dynamic countermeasures.”
BackgroundDeep Learning Based Social Bot Detection on Twitter
Arın, E., & Kutlu, M. (2023) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2023.3254429 · Cited by 65 · see citing works
“However, it still remains unsolved [15] because there are many extremely challenging problems, which can be summarized as follows.”
BackgroundPathak, R., Spezzano, F., & Pera, M. S. (2023) · ACM Transactions on the Web · doi:10.1145/3616088 · Cited by 64 · see citing works
“Recent research endeavors have addressed bot detection and features that characterize users, content, and context as means to identify misinformation in social networks and countermeasure the irst four factors that cause the misinformation to spread in social networks [10, 97].”
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Trujillo, A., & Cresci, S. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3555639 · Cited by 57 · see citing works
“2 Moderating Online Platforms Themany ailments that currently affect online social platforms —including those described above, as well as the spread ofmis- and disinformation [22, 62]; of propaganda and conspiracy theories [18, 48]; the rise of hateful, abusive, and coordinated inauthentic behavior [27, 47]; and the m…”
BackgroundSelf-citationBotMoE: Twitter Bot Detection with Community-Aware Mixtures of Modal-Specific Experts
Liu, Y., Tan, Z., Wang, H., Feng, S., Zheng, Q., & Luo, M. (2023) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3539618.3591646 · Cited by 61 · see citing works
“Existing automatic Twitter bot detection models could be characterized by their information sources (feature-based, text-based, and graph-based [17]), reflecting the arms race between bot operators and bot detection research [6]: Early Twitter bots are mostly simple spam bots posting repetitive and malicious content […”
BackgroundMethodInfluentialChen, K., Duan, Z., & Yang, S. (2021) · Politics and the Life Sciences · doi:10.1017/pls.2021.19 · Cited by 51 · see citing works
“Bot accounts are prevalent and evolving (Cresci, 2020).”
BackgroundThe Spread of Propaganda by Coordinated Communities on Social Media
Hristakieva, K., Cresci, S., Martino, G. D. S., et al. (2022) · ACM Web Science Conference (WebSci) · doi:10.1145/3501247.3531543 · Cited by 49 · see citing works
Self-citationAI-Based Techniques for Ad Click Fraud Detection and Prevention: Review and Research Directions
Alzahrani, R., & Aljabri, M. (2022) · Journal of Sensor and Actuator Networks · doi:10.3390/jsan12010004 · Cited by 41 · see citing works
“Some calculations, such as the variance of the IP clicks factor, were also done to assess IP patterns properly because it is suspicious that a single user, perhaps a click bot, performs a constant number of clicks from many IP addresses.”
The future of online or web-based research. Have you been BOTTED?
Liem, D. G. (2025) · Appetite · doi:10.1016/j.appet.2025.108058 · Cited by 47 · see citing works
Investigating the difference between trolls, social bots, and humans on Twitter
Mazza, M., Avvenuti, M., Cresci, S., & Tesconi, M. (2022) · Computer Communications · doi:10.1016/j.comcom.2022.09.022 · Cited by 36 · see citing works
Self-citationBots in Social and Interaction Networks
Mendoza, M., Tesconi, M., & Cresci, S. (2020) · ACM Transactions on Information Systems · doi:10.1145/3419369 · Cited by 36 · see citing works
“Social botnets can more effectively exert their influence on the users of a social network by acting in a synchronized and coordinated fashion [10, 13, 16].”
BackgroundSelf-citationAmaizu, G. C., Njoku, J. N., Lee, J., & Kim, D. (2024) · ICT Express · doi:10.1016/j.icte.2024.02.010 · Cited by 33 · see citing works
Detection of fake news campaigns using graph convolutional networks
Michail, D., Kanakaris, N., & Varlamis, I. (2022) · International Journal of Information Management Data Insights · doi:10.1016/j.jjimei.2022.100104 · Cited by 34 · see citing works
Detecting inorganic financial campaigns on Twitter
Tardelli, S., Avvenuti, M., Tesconi, M., & Cresci, S. (2021) · Information Systems · doi:10.1016/j.is.2021.101769 · Cited by 31 · see citing works
Self-citationDuped by Bots: Why Some are Better than Others at Detecting Fake Social Media Personas
Kenny, R., Fischhoff, B., Davis, A. L., Carley, K. M., & Canfield, C. (2022) · Human Factors The Journal of the Human Factors and Ergonomics Society · doi:10.1177/00187208211072642 · Cited by 31 · see citing works
“Their impact is ampli fi ed by bad actors ’ ability to scale deployment at little cost, while retaining anonymity and avoiding accountability (Cresci, 2020; Veale & Cook, 2018).”
BackgroundHays, C., Schutzman, Z., Raghavan, M., Walk, E., & Zimmer, P. (2023) · International World Wide Web Conference (WWW) · doi:10.1145/3543507.3583214 · Cited by 29 · see citing works
“1A previous version of this paper speculated that the data might have been from a project related to Cresci et al. [18]’s work on the social sensing of earthquakes.”
BackgroundInfluentialYang, Y., Yang, R., Peng, H. M., et al. (2023) · ACM Web Conference (WWW) · doi:10.1145/3543507.3583500 · Cited by 29 · see citing works
“Millions of bots, typically controlled by automated programs or platform APIs [1], attempt to sneak into genuine users as a disguise to pursue malicious goals such as actively engaging in election interference [11, 17], misinformation dissemination [8], and privacy attacks [37].”
BackgroundSocial Engineering Attacks: Recent Advances and Challenges
Mashtalyar, N., Ntaganzwa, U. N., Santos, T., Hakak, S., & Ray, S. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-77392-2_27 · Cited by 29 · see citing works
The language and targets of online trolling: A psycholinguistic approach for social cybersecurity
Uyheng, J., Moffitt, J. D., & Carley, K. M. (2022) · Information Processing & Management · doi:10.1016/j.ipm.2022.103012 · Cited by 26 · see citing works
Von der Mensch-Maschine-Interaktion zur kommunikativen KI
Hepp, A., Loosen, W., Dreyer, S., Jarke, J., Kannengießer, S., Katzenbach, C., Malaka, R., Pfadenhauer, M., Puschmann, C., & Schulz, W. (2022) · Publizistik · doi:10.1007/s11616-022-00758-4 · Cited by 32 · see citing works
Benchmarking Crisis in Social Media Analytics: A Solution for the Data-Sharing Problem
Assenmacher, D., Weber, D., Preuß, M., Valdez, A. S. C., Bradshaw, A., Roß, B., Cresci, S., Trautmann, H., Neumann, F., & Grimme, C. (2021) · Social Science Computer Review · doi:10.1177/08944393211012268 · Cited by 27 · see citing works
“…agents that are designed to support disinformation, manipulation, or propaganda campaigns and otherwise simulate human behavior (Al-Rawi, 2019; Cresci, 2020; Grimme, Preuss, et al., 2017) and disinformation campaigns addresses both computational detection methods and the social/political…”
BackgroundSelf-citationBot Datasets on Twitter: Analysis and Challenges
Samper-Escalante, L. D., Loyola‐González, O., Monroy, R., et al. (2021) · Applied Sciences · doi:10.3390/app11094105 · Cited by 24 · see citing works
“These services are a tempting target for malicious people to spread fake news, influence public voting, create false opinions, or harass users [12].”
On the efficacy of old features for the detection of new bots
Nicola, R. D., Petrocchi, M., & Pratelli, M. (2021) · Information Processing & Management · doi:10.1016/j.ipm.2021.102685 · Cited by 24 · see citing works
“In fact, in the first years of bots hunting ( 2010-2013/14), researchers mainly focused on supervised machine learning and on the analysis of the single account: ‘classifiers were separately applied to each account of the group’, to which they assigned a bot or not label [15].”
Alothali, E., Salih, M., Hayawi, K., et al. (2022) · Applied Sciences · doi:10.3390/app12168117 · Cited by 23 · see citing works
Profiling users and bots in Twitter through social media analysis
Pastor-Galindo, J., Mármol, F. G., & Pérez, G. M. (2022) · Information Sciences · doi:10.1016/j.ins.2022.09.046 · Cited by 23 · see citing works
SEGCN: a subgraph encoding based graph convolutional network model for social bot detection
Liu, F., Li, Z., Yang, C., Gong, D., Lu, H., & Liu, F. (2024) · Scientific Reports · doi:10.1038/s41598-024-54809-z · Cited by 25 · see citing works
“Bot operators are easily aware of the property features used by the bot detection model and they tend to evade detection by avoiding these features 10,18 .”
BackgroundLiu, Z. Q., Yao, Z., Li, F., & Luo, B. (2024) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3658644.3670392 · Cited by 63 · see citing works
“The detection of AI-backed bots, scams, and misinformation has been extensively studied in the literature [17, 66, 90, 107], while the rise of ChatGPT introduces both new opportunities [21, 29, 38, 39, 42, 103] and challenges [14, 18, 32, 33, 36, 71, 87].”
A language framework for modeling social media account behavior
Nwala, A. C., Flammini, A., & Menczer, F. (2023) · EPJ Data Science · doi:10.1140/epjds/s13688-023-00410-9 · Cited by 21 · see citing works
“The arms race has spawned a series of more complex detection methods [6, 16, 17].”
BackgroundInfluentialHHG-Bot: A Hyperheterogeneous Graph-Based Twitter Bot Detection Model
Wang, T., Wang, Z., Li, H., Xia, C., & Zhao, C. J. (2025) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2025.3543419 · Cited by 27 · see citing works
Social Botomics: A Systematic Ensemble ML Approach for Explainable and Multi-Class Bot Detection
Dimitriadis, I., Georgiou, K., & Vakali, A. I. (2021) · Applied Sciences · doi:10.3390/app11219857 · Cited by 20 · see citing works
“Recent research has shown that in order to avoid being suspended, social bot accounts can potentially learn to adapt in human behaviors; they show evidence of evolution [22] and linger in social media for prolonged periods of time, further expanding their functions.”
BackgroundInfluentialSocial media, migration and the platformization of moral panic: Evidence from Canada
Walsh, J., & Hill, D. (2022) · Convergence. The International Journal of Research into New Media Technologies · doi:10.1177/13548565221137002 · Cited by 19 · see citing works
“As a caveat, given the complexities of pattern recognition and online behaviour, bot detection is a challenging, imperfect task (Cresci, 2020).”
BackgroundUnsupervised detection of coordinated fake-follower campaigns on social media
Zouzou, Y., & Varol, O. (2024) · EPJ Data Science · doi:10.1140/epjds/s13688-024-00499-6 · Cited by 19 · see citing works
“Cresci thoroughly reviews bot detection approaches for the last decade [2].”
BackgroundMethodLee, S., Shin, D., Kwon, K. H., Han, S. P., & Lee, S. K. (2024) · MIS Quarterly · doi:10.25300/misq/2023/17195 · Cited by 19 · see citing works
Should we agree to disagree about Twitter’s bot problem?
Varol, O. (2023) · Online Social Networks and Media · doi:10.1016/j.osnem.2023.100263 · Cited by 20 · see citing works
Detection and impact estimation of social bots in the Chilean Twitter network
Mendoza, M., Providel, E., dos Santos, M. L. B., & Valenzuela, S. (2024) · Scientific Reports · doi:10.1038/s41598-024-57227-3 · Cited by 19 · see citing works
Multifaceted online coordinated behavior in the 2020 US presidential election
Tardelli, S., Nizzoli, L., Avvenuti, M., Cresci, S., & Tesconi, M. (2024) · EPJ Data Science · doi:10.1140/epjds/s13688-024-00467-0 · Cited by 25 · see citing works
“For example, social botnets exploit the synchronization allowed by the use of automation to effectively push narratives or support certain public characters [43].”
BackgroundSelf-citationSupervised learning models for social bot detection: Literature review and benchmark
Nguyen, H.-D., Nguyen, Q. D., Nguyen, C.-D. T., To, P. T., Nguyen, D. H., Nguyen-Gia, H., Tran, L. H., Tran, A. Q., Dang-Hieu, A., Nguyen‐Duc, A., et al. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122217 · Cited by 18 · see citing works
New Automation for Social Bots: From Trivial Behavior to AI-Powered Communication
Grimme, C., Pohl, J., Cresci, S., Lüling, R., & Preuss, M. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-18253-2_6 · Cited by 19 · see citing works
Self-citationCGNN: A Compatibility-Aware Graph Neural Network for Social Media Bot Detection
Huang, H., Tian, H., Zheng, X., Zhang, X., Zeng, D. D., & Wang, F. (2024) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2024.3396413 · Cited by 20 · see citing works
“Social bot detection methods are typically categorized into feature-based, graph-based, and hybrid methods based on their information sources: user features and social graphs [17], [18].”
BackgroundMethodInfluentialA Comparative Study of Bot Detection Techniques With an Application in Twitter Covid-19 Discourse
Antenore, M., Camacho, J. M., & Panizzi, E. (2022) · Social Science Computer Review · doi:10.1177/08944393211073733 · Cited by 17 · see citing works
“Supervised methods, though they might present generalization issues, are extensively adopted for bot detection (Cresci, 2020).”
InfluentialAn 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 · see citing works
“For these reasons, new approaches to the problem are considered to be of high relevance [5], since they may help to define a method that can generalize among different types of bot accounts while effectively detecting most of them.”
MethodYan, H. Y., Morrow, G., Yang, K.-C., & Wihbey, J. (2025) · Harvard Kennedy School Misinformation Review · doi:10.37016/mr-2020-171 · Cited by 18 · see citing works
“As demonstrated in the robocall incident, AI has already been exploited for political ends (Cresci, 2020; Menczer et al., 2023).”
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 18 · see citing works
BotCL: a social bot detection model based on graph contrastive learning
Yan, L., Li, Z., Gong, D., et al. (2024) · Knowledge and Information Systems · doi:10.1007/s10115-024-02116-4 · Cited by 16 · see citing works
Taiwan’s Public Discourse About Disinformation: The Role of Journalism, Academia, and Politics
Rauchfleisch, A., Tseng, T.-H., Kao, J.-J., et al. (2022) · Journalism Practice · doi:10.1080/17512786.2022.2110928 · Cited by 16 · see citing works
“On the other hand, computer scientists have mainly contributed with new methods that should help to automatically identify “ fake news ” (Shu et al. 2017) or so-called bots that automatically spread disinformation on social media platforms (Cresci 2020).”
BackgroundMulBot: 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 19 · see citing works
“So, developers of bot detectors moved from supervised learning towards unsupervised models that analyze groups of accounts as a whole rather than individual accounts, also solving the labeling issue [5].”
BackgroundInfluentialSelf-citationPohl, J., Seiler, M. V., Assenmacher, D., et al. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4066543 · Cited by 16 · see citing works
LGB: Language Model and Graph Neural Network-Driven Social Bot Detection
Zhou, M. L., Zhang, D., Wang, Y., et al. (2025) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2025.3573748 · Cited by 17 · see citing works
“However, such methods are vulnerable to feature forgery attacks [21].”
InfluentialInternet-Based Mental Health Survey Research: Navigating Internet Bots on Reddit
Mournet, A. M., & Kleiman, E. M. (2023) · Cyberpsychology Behavior and Social Networking · doi:10.1089/cyber.2022.0173 · Cited by 15 · see citing works
“9 For example, platforms like Qual-trics have implemented security measures within their platforms to identify suspicious responses.”
Detecting Social Bot on the Fly using Contrastive Learning
Zhou, M. L., Zhang, D., Wang, Y., Geng, Y., & Tang, J. (2023) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3583780.3615468 · Cited by 15 · see citing works
“However, such methods may suffer performance degradation when encountering well-designed fake features [7].”
BackgroundInfluentialMulti-modal Social Bot Detection: Learning Homophilic and Heterophilic Connections Adaptively
Li, S., Qiao, B., Li, K., Lu, Q., Lin, M., & Zhou, W. J. (2023) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3581783.3612569 · Cited by 19 · see citing works
“However, these methods only focus on the sequence features of tweets, but ignore the topological structure information[7].”
MethodExposure 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 19 · see citing works
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 · see citing works
Investigating coordinated account creation using burst detection and network analysis
Bellutta, D., & Carley, K. M. (2023) · Journal of Big Data · doi:10.1186/s40537-023-00695-7 · Cited by 15 · see citing works
“Though many automated social media accounts are innocuous or even beneficial, social bots have also seen extensive use in online influence operations [10, 53].”
BackgroundInfluentialA comprehensive review of current trends, challenges, and opportunities in text data privacy
Shahriar, S., Dara, R. A., & Akalu, R. (2025) · Computers & Security · doi:10.1016/j.cose.2025.104358 · Cited by 17 · see citing works
Robust Recommender System: A Survey and Future Directions
Zhang, K., Cao, Q., Sun, F., Wu, Y., Tao, S., Shen, H., & Cheng, X. (2025) · ACM Computing Surveys · doi:10.1145/3757057 · Cited by 40 · see citing works
Adversarial Machine Learning for Protecting Against Online Manipulation
Cresci, S., Petrocchi, M., Spognardi, A., et al. (2021) · IEEE Internet Computing · doi:10.1109/mic.2021.3130380 · Cited by 14 · see citing works
“These techniques, however, were still based on traditional approaches to the task of social bot detection, such as those based on general purpose, supervised machine learning algorithms [8].”
BackgroundMethodInfluentialSelf-citationAccou2vec: A Social Bot Detection Model Based on Community Walk
Liu, F., Yang, C., Li, Z., Gong, D., & Liu, F. (2023) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2023.3347040 · Cited by 14 · see citing works
“However, this kind of method needs manual feature extraction, which can be costly [6].”
Exploring the effect of streamed social media data variations on social network analysis
Weber, D., Nasim, M., Mitchell, L., & Falzon, L. (2021) · Social Network Analysis and Mining · doi:10.1007/s13278-021-00770-y · Cited by 13 · see citing works
“The question, however, of whether to remove content from social bots (bots that actively pretend to be human) depends on the research question at hand; because humans are easily fooled by social bots (Cresci et al. 2017; Nasim et al. 2018; Cresci 2020), their contribution to discussions may still be valid (unlike, e.”
BackgroundDas, B., & TSB, S. (2023) · Online Social Networks and Media · doi:10.1016/j.osnem.2023.100247 · Cited by 18 · see citing works
Huang, D., Song, J., & Zhang, X. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.102956 · Cited by 14 · see citing works
PEGNN: Peripheral-Enhanced graph neural network for social bot detection
Guyan, Q., Liu, Y., Liu, J., et al. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.127294 · Cited by 13 · see citing works
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 15 · see citing works
“In recent years, social bot accounts have become increasingly sophisticated, making distinguishing them from legitimate users difficult, thanks to advancements in deepfakes and other deceiving algorithms [18].”
BackgroundInfluentialMisleading 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 · see citing works
“Finally, in bot detection tasks, recent methods use representation learning based on social and interaction networks (Cresci 2020; Mendoza et al. 2020).”
BackgroundMethodLe, T., Tran-Thanh, L., & Lee, D. (2022) · International World Wide Web Conference (WWW) · doi:10.1145/3485447.3512215 · Cited by 14 · see citing works
“Moreover, we can also incorporate these adversarial behaviors to advance the development of novel bot detection models in a proactive manner [4].”
BackgroundInfluentialZhan, X., Zhang, K., Ge, L., Huang, J., Zhang, Z., Wei, L., Sun, G., Liu, C., & Zhang, Z. (2022) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2022.3217419 · Cited by 13 · see citing works
“There are also papers working on how to detect the misinformation and social bots related to COVID-19 [19], [20], [21], [22], [23].”
BackgroundDeep Neural Network for Detecting Fake Profiles in Social Networks
Amankeldin, D., Kurmangaziyeva, L., Майлыбаева, А. Д., Glazyrina, N., Zhumadillayeva, А., & Ташматовна, К. Н. (2023) · Computer Systems Science and Engineering · doi:10.32604/csse.2023.039503 · Cited by 13 · see citing works
“Cresci employed machine learning and honeypots to identify spammers on MySpace and Twitter social networks [21].”
MethodAssembling a multi-platform ensemble social bot detector with applications to US 2020 elections
Ng, L. H. X., & Carley, K. M. (2024) · Social Network Analysis and Mining · doi:10.1007/s13278-024-01211-2 · Cited by 13 · see citing works
“Because of the coordination features, where the group of bots are motivated by a single intent, they leave behind more automation than single bots, which allows detection through bot detection mechanisms [42].”
Counterspeakers’ Perspectives: Unveiling Barriers and AI Needs in the Fight against Online Hate
Mun, J., Buerger, C., Liang, J. T., Garland, J., & Sap, M. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642025 · Cited by 17 · see citing works
“AI bot detection using neural methods have been explored to help social media platforms in filtering bots [32, 146], but with increasing capabilities of generative AI, detecting non-human agents is becoming harder [45].”
BackgroundMethodDissecting 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 · see citing works
“They assumed that legitimate individual accounts could be distinguished from bot accounts based on their (Cresci 2020) characteristics.”
InfluentialEvolution 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 · see citing works
“Some scholars call for a paradigm shift in bot detection to focus on identifying suspicious network coordination instead of detecting bot accounts, and to use unsupervised learning instead of supervised methods (Cresci, 2020; Grimme et al., 2018).”
BackgroundLi, Q., Liu, Q., Liu, S., Di, X., Chen, S., & Zhang, H. (2023) · Communication and the Public · doi:10.1177/20570473231166157 · Cited by 12 · see citing works
“Generative adversarial networks are novel machine learning techniques that improve bot recognition models by incorporating adversarial training of generators and discriminators (Cresci, 2020).”
BackgroundEnhancing misinformation countermeasures: a multimodal approach to twitter bot detection
Arranz-Escudero, O., Quijano-Sánchez, L., & Liberatore, F. (2025) · Social Network Analysis and Mining · doi:10.1007/s13278-025-01435-w · Cited by 11 · see citing works
“However, advanced bots mimicking real user text pose a challenge to these methods (Cresci 2020).”
Cyber democracy in the digital age: Characterizing hate networks in the 2022 US midterm elections
Rozo, A. Z., Campo-Archbold, A., López, D. D., et al. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102459 · Cited by 10 · see citing works
Dispelling the Fake: Social Bot Detection Based on Edge Confidence Evaluation
Qiao, B., Zhou, W. J., Li, K., Li, S., & Hu, S. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3396192 · Cited by 12 · see citing works
“User profile-based methods typically analyze differences in value-type features (e.g., the number of followers) and Boolean-type features (e.g., verification status) to detect bots [14].”
InfluentialMapping state-sponsored information operations with multi-view modularity clustering
Uyheng, J., Cruickshank, I. J., & Carley, K. M. (2022) · EPJ Data Science · doi:10.1140/epjds/s13688-022-00338-6 · Cited by 10 · see citing works
“…existing models – especially those founded on supervised machine learning algorithms – may generate rich and accurate predictions on precise dimensions of an information operation, they are not designed to account for the multifaceted and novel nature of influence campaigns as a whole [12, 17, 18].”
BackgroundDetection of Inappropriate Tweets Linked to Fake Accounts on Twitter
Alsubaei, F. S. (2023) · Applied Sciences · doi:10.3390/app13053013 · Cited by 9 · see citing works
Evolution of Malicious Social Bot Detection: From Individual Profiling to Group Analysis and Beyond
Liu, Y., Xu, S., Li, Y., & Yu, S. (2025) · Journal of Social Computing · doi:10.23919/jsc.2025.0017 · Cited by 9 · see citing works
BotSCL: Heterophily-Aware Social Bot Detection with Supervised Contrastive Learning
Wu, Q., Yang, Y., He, B., et al. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-78183-4_4 · Cited by 9 · see citing works
- Authors
- Cresci, S.
- Affiliations
- National Research Council (IT)
- Corresponding author
- Cresci, Stefano
- ORCID
- Cresci, Stefano
- Venue
- Communications of the ACM
- Published
- 2020-09-23
- Volume
- 63
- Issue
- 10
- Pages
- 72-83
- ISSN
- 0001-0782 (ISSN-L)
- 1557-7317
- OpenAlex
- W3041367927
- Cited by
- 323
- References
- 30
- Citations by year
- 2026: 52 · 2025: 56 · 2024: 54 · 2023: 64 · 2022: 49 · 2021: 40 · 2020: 8
- FWCI
- 38.08
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Communications of the ACM (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- CINECA IRIS Institutial research information system (University of Pisa) (submittedVersion)
- arXiv (Cornell University)
- 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 bot detection, bots, political disinformation, election interference, misinformation, detection methods
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.
Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-09.
Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-09.
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 ›
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