The DOI matches its Crossref record; 48 of the 50 references it cites were found in a registry or an index; none of the 17 sources its claims cite is checked for support yet.
The Paradigm-Shift of Social Spambots: Evidence, Theories, and Tools for the Arms Race
Open accessAbstract
Recent studies in social media spam and automation provide anecdotal argumentation of the rise of a new generation of spambots, so-called social spambots. Here, for the first time, we extensively study this novel phenomenon on Twitter and we provide quantitative evidence that a paradigm-shift exists in spambot design. First, we measure current Twitter's capabilities of detecting the new social spambots. Later, we assess the human performance in discriminating between genuine accounts, social spambots, and traditional spambots. Then, we benchmark several state-of-the-art techniques proposed by the academic literature. Results show that neither Twitter, nor humans, nor cutting-edge applications are currently capable of accurately detecting the new social spambots. Our results call for new approaches capable of turning the tide in the fight against this raising phenomenon. We conclude by reviewing the latest literature on spambots detection and we highlight an emerging common research trend based on the analysis of collective behaviors. Insights derived from both our extensive experimental campaign and survey shed light on the most promising directions of research and lay the foundations for the arms race against the novel social spambots. Finally, to foster research on this novel phenomenon, we make publicly available to the scientific community all the datasets used in this study.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
The paper provides quantitative evidence that a new generation of Twitter “social spambots” mimics genuine users closely enough to evade Twitter, human annotators, and several state-of-the-art detection tools. It argues for a paradigm shift toward group-level/collective-behavior detection and releases annotated datasets.
Key findings
- Twitter’s own suspensions barely affect social spambots: survival rates were 95.2%, 96.1%, and 99.6% for social spambots #1–#3, close to genuine accounts at 96.5%; by contrast, traditional spambots #2 were 1.0% alive / 99.0% suspended, and fake followers were 25.4% alive / 73.5% suspended.
- Human annotators failed on social spambots: accuracy 0.2355 with 1,065 false negatives, versus 0.9136 on traditional spambots and 0.9201 on genuine accounts; Fleiss’ kappa was 0.186 for social spambots, 0.007 for traditional spambots, and 0.410 for genuine accounts.
- Established detection tools also failed: BotOrNot? achieved F-Measure 0.288 and MCC 0.174 on test set #1; Yang et al. achieved F-Measure 0.261 and MCC 0.043; Miller et al. performed worst.
- Group-level approaches performed much better: Ahmed et al. modified with fastgreedy community detection reached MCC 0.886 on test set #1 and 0.847 on test set #2; Cresci et al.’s digital DNA reached MCC 0.952 and 0.867, respectively.
Methods
- The study used Twitter datasets: 3,474 genuine accounts / 8,377,522 tweets; social spambots #1 (991 accounts / 1,610,176 tweets), #2 (3,457 / 428,542), #3 (464 / 1,418,626); traditional spambots #1–#4; fake followers (3,351 / 196,027); and two mixed test sets of 1,982 and 928 accounts.
- Twitter monitoring used API error codes 63 (suspended) and 50 (deleted) to measure account survival. Crowdsourcing used CrowdFlower with 4,428 accounts, at least 3 contributors per account, 247 trusted contributors from 42 countries, 13,284 answers, majority voting, and a 70% test-question accuracy threshold.
- The authors reproduced and benchmarked BotOrNot?, Yang et al.’s supervised classifier, Miller et al.’s stream clustering, Ahmed et al.’s graph clustering (modified with fastgreedy), and Cresci et al.’s digital DNA, evaluating precision, recall, specificity, accuracy, F-Measure, and MCC on the test sets.
Limitations
- Stated: BotOrNot?’s authors note its performance against evolved spambots may be worse than originally reported; Viswanath et al.’s technique cannot directly spot individual tampering accounts; and Table 7 is explicitly not complete but illustrative of an emerging trend.
- Stated/evident: 142 accounts (3.2%) were deleted, suspended, or protected during the crowdsourcing campaign, leaving 4,286 instead of 4,428; contributors were tech-savvy Twitter users and filtered by >70% test accuracy, so human-performance results may not generalize to all users.
- Evident: the datasets come from specific Twitter campaigns and years and rely on manual annotation; the social-spambot ground truth used group comparison, and the paper does not establish performance on other platforms or future spambot generations.
Model-written summary. Check it against the paper before relying on it.
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Verification
Claims the paper makes
39 Own result · 21 Own interpretation · 16 Not checked yet · 10 No basis given · 5 Own method
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Surprisingly, we measured a slightly higher agreement for the classification of social spambots than for traditional ones, with κ = 0 . 186.”
plain- Asserted: passes — The authors put it forward as true.
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- Rests on an assumption: The level of agreement measured by Fleiss' kappa can be interpreted as a proxy for the difficulty of a task.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As shown, social spambots feature very small differences with respect to genuine accounts ( ∼ ± 3%).”
plain- Asserted: passes — The authors put it forward as true.
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- Rests on an assumption: Twitter's API error codes 63 and 50 accurately indicate that an account has been suspended by Twitter or deleted by its original owner, respectively.
- Rests on an assumption: The genuine accounts that answered the crowdsensing question are human-operated.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As shown in Table 2, genuine accounts feature a very high survival rate (96.5%).”
plain- Asserted: passes — The authors put it forward as true.
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- Rests on an assumption: Twitter's API error codes 63 and 50 accurately indicate that an account has been suspended by Twitter or deleted by its original owner, respectively.
- Rests on an assumption: The genuine accounts that answered the crowdsensing question are human-operated.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Surprisingly, we found such automated accounts to be similar to genuine ones in every way.”
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Some of these differences are not even statistically significant, according to a chi-square test.”
plain- Asserted: passes — The authors put it forward as true.
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- Rests on an assumption: Twitter's API error codes 63 and 50 accurately indicate that an account has been suspended by Twitter or deleted by its original owner, respectively.
- Rests on an assumption: The genuine accounts that answered the crowdsensing question are human-operated.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The deceitful activity was carried out by spamming URLs pointing to the advertised products.”
plaindirect- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Instead, they showed very little agreement while classifying traditional spambots, as represented by κ = 0 . 007.”
plain- Asserted: passes — The authors put it forward as true.
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- Rests on an assumption: The level of agreement measured by Fleiss' kappa can be interpreted as a proxy for the difficulty of a task.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“So far, traditional spambots #1 have largely managed to evade suspension, despite dating back to 2009.”
plain- Asserted: passes — The authors put it forward as true.
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- Rests on an assumption: Twitter's API error codes 63 and 50 accurately indicate that an account has been suspended by Twitter or deleted by its original owner, respectively.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Further, we uncovered a third group of social bots, social spambots #3 , which advertise products on sale on Amazon.com .”
plain- Asserted: passes — The authors put it forward as true.
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In addition, among the no longer available accounts, the vast majority have been deleted by the original owner, rather than suspended by Twitter.”
plain- Asserted: passes — The authors put it forward as true.
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- Rests on an assumption: Twitter's API error codes 63 and 50 accurately indicate that an account has been suspended by Twitter or deleted by its original owner, respectively.
- Rests on an assumption: The genuine accounts that answered the crowdsensing question are human-operated.
- Probably not a substantial claim. 13
- It may state two separate claims as one. 21
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Overall, we collected 13,284 answers given by 247 trusted contributors from 42 different countries.”
plain- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Out of the 247 participating contributors, 60 of them ( ∼ 24%) evaluated our campaign, leading to a convincing aggregated score of 3.7/5, as shown in detail in Table 5.”
boosteddirect- Asserted: passes — The authors put it forward as true.
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Instead, traditional spambots #2 and fake followers show massive differences: ∼ ± 96% and ∼ ± 72%, respectively.”
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
- Rests on an assumption: Twitter's API error codes 63 and 50 accurately indicate that an account has been suspended by Twitter or deleted by its original owner, respectively.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“So far, we have demonstrated that neither Twitter nor human operators are currently capable of identifying novel social spambots.”
boosted- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Amazingly, we also found tens of human accounts who tried to engage in conversation with some of the spambots.”
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“Those accounts also represented credible sources of information since they all had thousands of followers and friends, the majority of which were genuine users 2 .”
plaindirect- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“All the 3 groups of social spambots feature very high survival rates, respectively 95.2%, 96.1%, and 99.6%.”
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
- Rests on an assumption: Twitter's API error codes 63 and 50 accurately indicate that an account has been suspended by Twitter or deleted by its original owner, respectively.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As reported in Table 6, overall, the human annotators obtained an accuracy of less than 0.24 on the social spambots, with more than 1,000 False Negatives (FN), meaning that contributors classified more than 1,000 accounts as genuine, when they actually belonged to the dataset of the last generation of spambots.”
plain- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The vast majority of tweets were harmless messages, occasionally interspersed by tweets mentioning a specific genuine (human) account and suggesting him to buy the VIP version of the app from a Web store.”
plain- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Indeed, only 8.6% of the bots have been suspended, while 88.9% of them are still alive.”
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
- Rests on an assumption: Twitter's API error codes 63 and 50 accurately indicate that an account has been suspended by Twitter or deleted by its original owner, respectively.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Whereas the genuine accounts feature distributions that almost uniformly span across the possible range of values, social spambots have anomalous distributions.”
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“As shown in Table 7, the established works benchmarked in Section 4 largely failed to detect the new wave of social spambots, one notable exception being the unsupervised approach proposed in [2].”
What the sources say
- Not checked yetcites [2] F. Ahmed and M. Abulaish. A generic statistical approach for spam detection in online social networks. Computer Communications, 36(10):1120--1129, 2013. source
plain- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As reported in Table 7, our modified implementation proved effective in detecting social spambots, with an MCC = 0.886 for test set #1 and MCC = 0.847 for test set #2 .”
plaindirect- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This interesting result shows that, overall, the human contributors were able to correctly detect traditional spambots, as shown by the 0.91 accuracy, but also that contributors rarely agreed on the class.”
plain- Asserted: passes — The authors put it forward as true.
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- Rests on an assumption: The level of agreement measured by Fleiss' kappa can be interpreted as a proxy for the difficulty of a task.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“We only recruited contributors who were tech-savvy and Twitter users themselves, in order to be reasonably sure about their knowledge of Twitter and its dynamics.”
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim.
In the paper
“This seems to suggest that Twitter's spambot detection mechanisms are still unable to accurately identify such accounts, while recent solutions proposed by Academia have succeeded in this task [44].”
What the sources say
- Not checked yetcites [44] C. Yang, R. Harkreader, and G. Gu. Empirical evaluation and new design for fighting evolving Twitter spammers. IEEE Trans. Inform. Forens. Sec., 8(8):1280--1293, 2013. source
hedgeddirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
- Rests on an assumption: Twitter's API error codes 63 and 50 accurately indicate that an account has been suspended by Twitter or deleted by its original owner, respectively.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Results show that neither Twitter, nor humans, nor cutting-edge applications are currently capable of accurately detecting the new social spambots.”
plain- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These results imply that humans generally failed in classifying social spambots (accuracy = 0 . 2355) and, furthermore, that they also were more in agreement on this mistake than they were when (correctly) classifying traditional spambots.”
plaincorrelational- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
- Rests on an assumption: The level of agreement measured by Fleiss' kappa can be interpreted as a proxy for the difficulty of a task.
In the paper
“BotOrNot? is a publicly-available service 7 to evaluate the similarity of a Twitter account with the known characteristics of social spambots [15].”
What the sources say
- Not checked yetcites [15] C. A. Davis, O. Varol, E. Ferrara, A. Flammini, and F. Menczer. BotOrNot: A system to evaluate social bots. In WWW companion. ACM, 2016. source
plain- Asserted: passes — The authors put it forward as true.
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What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Our internal manual annotation has been carried out by comparing every account to all the others, in order to highlight possible similarities and common behaviors.”
plain- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Thus, the political candidate was able to reach many more accounts in addition to his direct followers and managed to alter Twitter engagement metrics during the electoral campaign.”
plaindirectSingle case- Asserted: passes — The authors put it forward as true.
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In the paper
“More worryingly, they also suggest that humans might not be able to detect social spambots in the wild, and to distinguish them from genuine accounts.”
hedged- Asserted: passes — The authors put it forward as true.
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None of the sources cited for this claim have been checked yet.
In the paper
“Recent studies in social media spam and automation provide anecdotal argumentation of the rise of a new generation of spambots, so-called social spambots .”
- Also stated in: § 1. INTRODUCTION · p. 1 · hedged
“Recent investigations anecdotally highlight how new waves of social spambots are rising [16, 48].”
What the sources say
- Not checked yetcites [16] E. Ferrara, O. Varol, C. Davis, F. Menczer, and A. Flammini. The rise of social bots. Commun. ACM, 59(7):96--104, 2016. source
- Not checked yetcites [48] J. Zhang, R. Zhang, Y. Zhang, and G. Yan. The rise of social botnets: Attacks and countermeasures. arXiv preprint arXiv:1603.02714, 2016.
plain- Asserted: passes — The authors put it forward as true.
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- Also stated in: § 1. INTRODUCTION · p. 1 · hedged
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Furthermore, we required each account to be classified by at least 3 different contributors, with the final class decided by majority voting.”
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“Low values of both Precision and Recall mean incomplete and unreliable spambot detection.”
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“However, the technique cannot directly spot the tampering accounts, and, thus, detection and removal of the single accounts must be performed using a separate methodology.”
What the sources say
- Not checked yetcites [40] B. Viswanath, M. A. Bashir, M. B. Zafar, S. Bouget, S. Guha, K. P. Gummadi, A. Kate, and A. Mislove. Strength in numbers: Robust tamper detection in crowd computations. In COSN. ACM, 2015. source
plain- Asserted: passes — The authors put it forward as true.
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In addition, a further property - and advantage - of this recent research wave is that it proposes ad-hoc detection algorithms, rather than adopting generic and off-the-shelf machine learning algorithms.”
plain- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We can observe a paradigm-shift for research and development of spambot detection systems, which may exploit the new concepts to achieve better resilience and robustness and to withstand the next evolution of social media spambots.”
hedgedconditional- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The promising outcome of these novel approaches clearly indicates that this is a favorable research avenue.”
boosted- Asserted: passes — The authors put it forward as true.
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Despite being based on different key concepts, all these systems propose novel algorithmic solutions and investigate groups of accounts as a whole, marking a significant difference with the previous literature.”
plain- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We demonstrated the need for novel analytic tools capable of turning the tide in the arms race against such sophisticated spambots.”
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
The paper relies on this without support.
Twitter's API error codes 63 and 50 accurately indicate that an account has been suspended by Twitter or deleted by its original owner, respectively.
“API queries to a suspended account result in Twitter responding with the error code 63. API queries to accounts that have been deleted by their original owner result in Twitter responding with the error code 50.”
The paper relies on this without support.
The internal manual verification correctly certifies the automated nature of the accounts.
“All the accounts collected in this process have then undergone an internal manual verification phase to certify their automated nature.”
The paper relies on this without support.
The genuine accounts that answered the crowdsensing question are human-operated.
“All the replies to our questions were manually verified and all the 3,474 accounts that answered were certified as humans.”
The paper relies on this without support.
The level of agreement measured by Fleiss' kappa can be interpreted as a proxy for the difficulty of a task.
“The level of agreement can be also interpreted as a proxy for the difficulty of a task.”
The paper relies on this without support.
Social spambots have a common goal, such as increasing someone's reputation score.
“since they do have a common goal (e.g., increasing someone's reputation score).”
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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“these models can be evaded by malicious actors if they change their behaviors over time [40, 92]”
From Six Million (Suspected) Fake Stars on GitHub: A Growing Spiral of Popularity Contests, Spams, and Malware (2026) · cites this paper as [40]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“we examined the behavioral similarities between accounts during annotation as described in the literature [12] and noticed significant similarities in the content-sharing behavior of anomalous accounts”
From Unsupervised detection of coordinated fake-follower campaigns on social media (2024) · cites this paper as [12]
The cited source says what the claim attributes to it.
In the citing paper
“malicious actors continuously evolve their behaviors to evade platform detection [40, 58]”
From Six Million (Suspected) Fake Stars on GitHub: A Growing Spiral of Popularity Contests, Spams, and Malware (2026) · cites this paper as [40]
In the citing paper
“Fake followers can be bought for as low as 12$ per 1,000 followers in the surface Web.”
From A decade of social bot detection (2020) · cites this paper as [5]
In the citing paper
“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.”
From A decade of social bot detection (2020) · cites this paper as [5]
In the citing paper
“As such, botnets leave behind more traces of their automation and coordination than those left behind by sophisticated single bots [5].”
From A decade of social bot detection (2020) · cites this paper as [5]
In the citing paper
“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].”
From A decade of social bot detection (2020) · cites this paper as [5]
In the citing paper
“The generality and ease of deployment of this detector are however counterbalanced by a reduced bot detection accuracy [5, 17].”
From A decade of social bot detection (2020) · cites this paper as [5]
In the citing paper
“Results showed that only 5% of newer bots are removed from social platforms, whereas older ones are removed 60% of the times [5].”
From A decade of social bot detection (2020) · cites this paper as [5]
In the citing paper
“They also report that some bot classes, like social spambots, are hard to distinguish from human accounts.”
From Detection of Novel Social Bots by Ensembles of Specialized Classifiers (2020) · cites this paper as [11]
In the citing paper
“Cresci et al. [11] highlight that it is hard for human annotators to assign one label to one account to describe its bot class.”
From Detection of Novel Social Bots by Ensembles of Specialized Classifiers (2020) · cites this paper as [11]
In the citing paper
“The performance decay of current detection systems in the wild was reported by Cresci et al. [11], who showed that Twitter, human annotators, and state-of-the-art bot detection systems failed at discriminating between some new social spambots and genuine accounts.”
From Detection of Novel Social Bots by Ensembles of Specialized Classifiers (2020) · cites this paper as [11]
In the citing paper
“By promoting online activity, bots can interact with humans and influence their standing against specific topics such as political issues [10, 7, 12, 13].”
From Influence of augmented humans in online interactions during voting events (2019) · cites this paper as [7]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
“| social spambots #2 | 3,457 | 3,322 (96.1%) | 1 (0.1%) | 134 (3.8%) |”
Reported 0.1 · recomputed 0.028927
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
“| social spambots #2 | 3,457 | 3,322 (96.1%) | 1 (0.1%) | 134 (3.8%) |”
Reported 3.8 · recomputed 3.876193
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Keywords · p. 2
“| social spambots #1 | retweeters of an Italian political candidate | 991 | 1,610,176 | 2012 | 3.1, 3.2 |”
§ 2. DATASETS · p. 3
“| social spambots #1 | 994 | 946 (95.2%) | 2 (0.2%) | 46 (4.6%) |”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
“| traditional spambots #1 | - 7.6% *** | - 0.8% | +8.7% *** |”
Reported 8.7 · recomputed 8.5
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
“| genuine accounts | 3,474 | 3,353 (96.5%) | 115 (3.3%) | 6 (0.1%) |”
Reported 0.1 · recomputed 0.172712
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Items 201–300 of 382
A Method of Machine Learning for Social Bot Detection Combined with Sentiment Analysis
Long, G., Lin, D., Lei, J., Zhi-yong, G., Hu, Y., & Xia, L. (2022) · International Conference on Machine Learning and Natural Language Processing (MLNLP) · doi:10.1145/3578741.3578790 · Cited by 8
CALEB: A Conditional Adversarial Learning Framework to enhance bot detection
Dimitriadis, I., Dialektakis, G., & Vakali, A. I. (2023) · Data & Knowledge Engineering · doi:10.1016/j.datak.2023.102245 · Cited by 15
Characterizing the language-production dynamics of social media users
Stine, Z. K., & Agarwal, N. (2019) · Social Network Analysis and Mining · doi:10.1007/s13278-019-0605-7 · Cited by 7
“Though four subsets of the traditional bot class are analyzed in Cresci et al. (2017), the data we received only had user information for one, a set of spam accounts used as a training set in Yang et al.”
InfluentialDetecting Malicious Social Robots with Generative Adversarial Networks
Wu, B., Liu, L., Dai, Z., Wang, X., & Zheng, K. (2019) · KSII Transactions on Internet and Information Systems · doi:10.3837/tiis.2019.11.018 · Cited by 7
“The experiment used 1971 normal human accounts and 462 social robot accounts as original samples, of which 891 normal users were from the data set used in [4], and the 1080 normal users and all the social robot users were from the data set used in [28].”
MethodOn Using Node Indices and Their Correlations for Fake Account Detection
Asghari, S., Chehreghani, M. H., & Chehreghani, M. H. (2022) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata55660.2022.10020627 · Cited by 7
“Likewise, Cresci-2017 includes user profile details, tweet metadata, and tweets.”
MethodDetection of Fake Instagram Accounts via Machine Learning Techniques
Chelas, S., Routis, G., & Roussaki, I. G. (2024) · Computers · doi:10.3390/computers13110296 · Cited by 6
Terumalasetti, S., & Reeja, S. R. (2024) · IEEE Access · doi:10.1109/access.2024.3521951 · Cited by 7
Twitter Bot Detection using Machine Learning Models
Tyagi, T., Sharma, P., Bansal, R., Anjali, A., Jain, K., Bansal, P., & Malik, K. (2023) · International Conference on Cloud Computing, Data Science & Engineering (Confluence) · doi:10.1109/confluence56041.2023.10048796 · Cited by 6
“Dataset was adopted from the research [13].”
Political Bot Bias in the Perception of Online Discourse
Schweitzer, S., Dobson, K. S. H., & Waytz, A. G. (2023) · Social Psychological and Personality Science · doi:10.1177/19485506231156020 · Cited by 8
“Whether Musk’s assertions are accurate, research clearly shows that social media bots are notoriously difficult for people to detect (Cresci et al., 2017; Pew Research Center, 2018).”
BackgroundManipify: An Automated Framework for Detecting Manipulators in Twitter Trends
Kausar, S., Tahir, B., & Mehmood, M. A. (2023) · Journal of Social Computing · doi:10.23919/jsc.2023.0001 · Cited by 6
Johnson, D., Carenini, G., & Murray, G. (2020) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3377325.3377513 · Cited by 6
“[9], which contains both tweets from genuine accounts, as well as from accounts identified as bots in [45].”
BackgroundHwang, T. (2017) · Cambridge University Press eBooks · doi:10.1017/9781108890960.012 · Cited by 12
Online Social Networks Misuse, Cyber Crimes, and Counter Mechanisms
Rao, S., Verma, A. K., & Bhatia, T. (2020) · Advances in social networking and online communities book series · doi:10.4018/978-1-7998-4718-2.ch010 · Cited by 7
Fahmy, S. G., Abdelgaber, K. M., Karam, O. H., & Elzanfaly, D. S. (2023) · Informatics · doi:10.3390/informatics10010027 · Cited by 6
Bot Detection: Will Focusing on Recall Cause Overall Performance Deterioration?
Nazer, T. H., Davis, M., Karami, M., et al. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-21741-9_5 · Cited by 6
Theoretical Bases of Methods of Counteraction to Modern Forms of Information Warfare
Bakirov, A. S., & Suleimenov, I. E. (2025) · Computers · doi:10.3390/computers14100410 · Cited by 17
Madahali, L., & Tian, J. (2023) · IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) · doi:10.1109/uemcon59035.2023.10316169 · Cited by 5
“Regarding the malicious bots, we use Cresci 2017 data [13] as malicious bots.”
BackgroundMethodExperimental Evaluation: Can Humans Recognise Social Media Bots?
Kolomeets, M., Tushkanova, O. N., Desnitsky, V. A., Vitkova, L., & Chechulin, A. A. (2024) · Big Data and Cognitive Computing · doi:10.3390/bdcc8030024 · Cited by 6
Analysis of the Impact of Poisoned Data within Twitter Classification Models
Price, K. R., Priisalu, J., & Nõmm, S. (2019) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2019.12.170 · Cited by 5
Deep Temporal Analysis of Twitter Bots
Rajendran, G., Ram, A., Vijayan, V., & Poornachandran, P. (2020) · Communications in computer and information science · doi:10.1007/978-981-15-4301-2_4 · Cited by 5
From Online Behaviours to Images: A Novel Approach to Social Bot Detection
Paolo, E. D., Petrocchi, M., & Spognardi, A. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-35995-8_42 · Cited by 6
Self-citationAre Deep Learning-Generated Social Media Profiles Indistinguishable from Real Profiles?Preprint
Rossi, S., Kwon, Y., Auglend, O. H., Mukkamala, R. R., Rossi, M., & Thatcher, J. B. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2209.07214 · Cited by 5
“We identified two main approaches for experiments in social media, which are either conducting experiments directly within the social media platform (Cresci et al., 2017; Freitas et al., 2015; Shafahi et al., 2016) or alternatively by using a survey or other simulated environment such as a…”
Meng, X., Wang, X., & Peng, T. (2026) · Communication Methods and Measures · doi:10.1080/19312458.2026.2613829 · Cited by 7
“…coordination signals (Khaund et al., 2022), expert annotation or crowdsourcing (Chu et al., 2012; Feng et al., 2021), purchased fake accounts (Cresci et al., 2017b), platform moderation records (Kantepe & Ganiz, 2017), anomalous traffic patterns (Echeverria & Zhou, 2017), and honeypot…”
KE-MLLM: A Knowledge-Enhanced Multi-Sensor Learning Framework for Explainable Fake Review Detection
Chen, J., Liu, J., Liang, Y., & Zhou, M. (2026) · Applied Sciences · doi:10.3390/app16062909 · Cited by 8
Swinging in the States: Does disinformation on Twitter mirror the US presidential election system?
Pratelli, M., Petrocchi, M., Saracco, F., & Nicola, R. D. (2023) · International World Wide Web Conference (WWW) · doi:10.1145/3543873.3587638 · Cited by 4
“Bots’ evolutionary ability, namely the tendency they have acquired over the years to evade detection techniques, is well known [17, 23].”
BackgroundSelf-citationDeep neural networks approach with transfer learning to detect fake accounts social media on Twitter
Lubis, A. R., Prayudani, S., Hamzah, M. L., et al. (2024) · Indonesian Journal of Electrical Engineering and Computer Science · doi:10.11591/ijeecs.v33.i1.pp269-277 · Cited by 4
“This study will use the dataset contained in the study [27].”
Li, K., Guo, B., Zhang, Q., Yuan, J., & Yu, Z. (2019) · International World Wide Web Conference (WWW) · doi:10.1145/3308560.3316704 · Cited by 4
South African bot behaviour post the July 2018 Twitter account cull
Featherstone, C. (2019) · International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD) · doi:10.1109/icabcd.2019.8851039 · Cited by 4
“An unfortunate consequence of investigating the behaviour of bots is that we assist them in further disguising the behaviour [13].”
BackgroundToward Limiting Social Botnet Effectiveness while Detection Is Performed: A Probabilistic Approach
Spradling, M., Allison, M., Tsogbadrakh, T., & Strong, J. (2019) · International Conference on Computational Science and Computational Intelligence (CSCI) · doi:10.1109/csci49370.2019.00259 · Cited by 4
“This increased activity level has fueled what Cresci et. al dub an “arms race” to counter the next generation of social botnets [10], [11].”
BackgroundHow Many Features Do We Need to Identify Bots on Twitter?
Tabassum, F., Mubarak, S., Liu, L., & Du, J. T. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-28035-1_22 · Cited by 6
Machine Learning-Based Detection and Categorization of Malicious Accounts on Social Media
Bhattacharyya, A., & Kulkarni, A. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-61281-7_23 · Cited by 4
Pietro, R. D., Raponi, S., Caprolu, M., & Cresci, S. (2020) · Advances in information security · doi:10.1007/978-3-030-60618-3_2 · Cited by 4
Self-citationRidge count thresholding to uncover coordinated networks during onset of the Covid-19 pandemic
Kirn, S. L., & Hinders, M. K. (2022) · Social Network Analysis and Mining · doi:10.1007/s13278-022-00873-0 · Cited by 5
Understanding Information Propagation in Online Social Networks: A Systematic Mapping Study
Jerez-Villota, E., Jurado, F., & Moreno–Llorena, J. (2025) · IEEE Access · doi:10.1109/access.2025.3558768 · Cited by 5
Jin, R., & Liao, Y. (2025) · Applied Sciences · doi:10.3390/app15116230 · Cited by 3
“Malicious social bots have become a growing concern due to their ability to deceive and manipulate social networks [1–3].”
BotBR: Social Bot Detection with Balanced Feature Fusion and Reliability-Enhanced Graph Learning
Lin, Q., & Zhou, J. (2025) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3726302.3729908 · Cited by 3
Transdisciplinary AI Observatory—Retrospective Analyses and Future-Oriented Contradistinctions
Aliman, N.-M., Kester, L. J. H. M., & Yampolskiy, R. V. (2021) · Philosophies · doi:10.3390/philosophies6010006 · Cited by 21
“nabling some bot-free social media spaces could be crafted. However, it is conceivable that at a certain point, AI-based bot detection [162] might become futile. Also, social bots already fool people [131,163] and many assume that humans will become unable to discern them in the long-term. Nevertheless, it could be wo…”
BackgroundSocial Bot Detection Based on Window Strategy
Qiao, B., Li, K., Zhou, W. J., et al. (2023) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme55011.2023.00376 · Cited by 3
Beyond Words: Stylometric Analysis for Detecting AI Manipulation on Social Media
Ullah, U., Laudanna, S., Vinod, P., Sorbo, A. D., Visaggio, C. A., & Canfora, G. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-70879-4_11 · Cited by 3
Li, Y., Sun, X., Yang, R., Sun, X., Chen, S., Wang, S., Bhuiyan, M. Z. A., Zomaya, A. Y., & Xu, J. (2024) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-024-02392-0 · Cited by 4
“The third generation of bots since 2016, with a potent mixture of human operations and automated bot behaviors, managed to disguise themselves and survived platform-level detectors by using traditional classifiers or classic GNN models [25, 26].”
Integrating higher-order relations for enhanced twitter bot detection
Reiche, S., Cohen, S., Simonov, K., & Friedrich, T. (2024) · Social Network Analysis and Mining · doi:10.1007/s13278-024-01372-0 · Cited by 3
Tweet Credibility Ranker: A Credibility Features’ Fusion Model
Qureshi, K. A., & Malick, R. A. S. (2025) · Cognitive Computation · doi:10.1007/s12559-025-10413-5 · Cited by 3
Sybil Account Detection in Online Social Networks Using Statistical Feature Selection Techniques
Chauhan, A., & Sood, M. (2020) · Communications in computer and information science · doi:10.1007/978-981-15-4451-4_48 · Cited by 3
Emotions and Interests of Evolving Twitter Bots
Fazzolari, M., Pratelli, M., Martinelli, F., & Petrocchi, M. (2020) · IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS) · doi:10.1109/eais48028.2020.9122703 · Cited by 3
“…by filtering the streaming API for verified Twitter accounts. c) Social #1: A group of social bots 4 that was presented for the first time in [20], after being spotted on Twitter during Talnts is a mobile phone application for getting in touch with and hiring artists interested in writing,…”
BackgroundSelf-citationPrioritizing Original News on Facebook
Ni, X., Bu, S., Adams, L., & Markov, I. L. (2021) · Proceedings of the 30th ACM International Conference on Information & Knowledge Management · doi:10.1145/3459637.3481943 · Cited by 3
“Context indicators in the other group require analysis of external sources, such as author reputation and/or recognition by peers in terms of the PageRank algorithm, as in [3, 4].”
BackgroundJain, A., Gairola, R., Jain, S., & Arora, A. (2018) · Advances in business information systems and analytics book series · doi:10.4018/978-1-5225-5097-6.ch004 · Cited by 3
Malicious Account Detection on Indonesian Twitter Account
Alhaura, L., & Budi, I. D. (2020) · doi:10.1109/ic2ie50715.2020.9274682 · Cited by 3
He, H., Yang, H., Burckhardt, P., Kapravelos, A., Vasilescu, B., & Kästner, C. (2026) · Proceedings of the 2026 IEEE/ACM 48th International Conference on Software Engineering · doi:10.1145/3744916.3764531 · Cited by 28
“In general, the battle against fraudulent activities in social media is a never-ending arms race [39, 96].”
InfluentialMitigating group polarization through positive and neutral comment bots
Liu, M., Wu, Y. C., & Li, W. (2025) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2025.130580 · Cited by 2
Abisha, B., Kumari, S., V, R, W. B. N., Sutherlin Subitha, G., Christopher, C. S., & Wasim Haidar, S. K. (2025) · International Conference for Artificial Intelligence, Applications, Innovation and Ethics (AI2E) · doi:10.1109/ai2e64943.2025.10982966 · Cited by 3
Demystifying Misconceptions in Social Bots Research
Cresci, S., Yang, K.-C., Spognardi, A., Pietro, R. D., Menczer, F., & Petrocchi, M. (2025) · Social Science Computer Review · doi:10.1177/08944393251376707 · Cited by 30
“The first accounts of the difficulties supervised detectors face in detecting sophisticated bots date back to 2013 (Yang et al., 2013) and intensified in subsequent years (Cresci et al., 2017; Grimme et al., 2018; Cresci et al., 2019b).”
Self-citationProactive Bot Detection Based on Structural Information Principles
Zeng, X., Peng, H., & Li, A. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3646644 · Cited by 4
“However,thesedetectorsarevulnerable toadversarialmanipulations,asbotscanmodifythemanually designedfeaturestoevadedetectionbyexploitingpredictable featureselectionpatterns[18],[19].”
Kats, D., & Sharif, M. (2022) · International Conference on Human-Agent Interaction (HAI) · doi:10.1145/3527188.3561928 · Cited by 3
“Similarly, Cresci et al. asked study participants to label accounts which they later classified by a vote [16].”
BackgroundPerformance Comparison of Deep Learning Algorithms in Twitter Bot Detection
Al-ibadi, S. E. N., & Çevik, M. (2022) · College of Science International Conference of Recent Trends in Information Technology (CSCTIT) · doi:10.1109/csctit56299.2022.10145657 · Cited by 2
“This dataset was described by Cresci and collaborators[22] which includes a brand-new generation of social bots.”
MethodThe Dynamics of Political Narratives During the Russian Invasion of Ukraine
Biswas, A., Niven, T., & Lin, Y. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43129-6_4 · Cited by 2
Polychronis, C., & Kogan, M. (2023) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3610033 · Cited by 3
Beyond Following: Augmenting Bot Detection with the Integration of Behavioral Patterns
Reiche, S., Cohen, S., Simonov, K., & Friedrich, T. (2024) · Studies in computational intelligence · doi:10.1007/978-3-031-53468-3_21 · Cited by 2
Social bot detection on Twitter: robustness evaluation and improvement
Liu, A.-A., Xie, Y., Wang, L., Jin, G., Guo, J., & Li, J. (2024) · Multimedia Systems · doi:10.1007/s00530-024-01364-2 · Cited by 2
“Their impact on the dissemination of information in political, economic, and cultural realms cannot be underestimated and they may even pose a significant threat to national security [6, 7].”
Mmds: multimodal benchmark dataset for suspicious profile detection on twitter social network
Choudhary, M., Patil, S., Chouhan, S. S., & Pilli, E. S. (2024) · Social Network Analysis and Mining · doi:10.1007/s13278-024-01296-9 · Cited by 2
“…community, or product), cloned profiles (accounts reusing the profile and other information of a genuine account, with a malicious intention) (Cresci et al. 2017), abusive or bullying accounts (accounts engaging in foul language, online bullying, and harassment) (Choud-hary et al. 2021), and…”
Sallah, A., Alaoui, E. A. A., Hessane, A., Agoujil, S., & Nayyar, A. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-10350-8 · Cited by 3
The Face of Deception: The Impact of AI-Generated Photos on Malicious Social Bots
Kolomeets, M., Wu, H., Shi, L., & van Moorsel, A. (2024) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2024.3461328 · Cited by 3
“Nevertheless, this existing AI detection solution might be adequate to dissuade bot owners from extensively adopting AI, at least until a more advanced AI generator emerges in the ongoing arms race [47] between producing and combating disinformation.”
GRUbBD-SM: Gated Recurrent Unit Based Bot Detection on Social Media
Shah, A., Varshney, S., & Mehrotra, M. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-75164-6_3 · Cited by 2
Adversarial botometer: adversarial analysis for social bot detection
Najari, S., Rafiei, D., Salehi, M., & Farahbakhsh, R. (2024) · Social Network Analysis and Mining · doi:10.1007/s13278-024-01387-7 · Cited by 2
“To gain insight into the behavior of our models and identify areas where performance degradation occurs and finally propose solutions, we carried adversarial training.”
Social Media Influencers and Politics
Makse, H. A., & Zava, M. (2024) · Understanding complex systems · doi:10.1007/978-3-031-78058-5_2 · Cited by 3
Social Media Bot Policies: Evaluating Passive and Active Enforcement
Radivojevic, K., McAleer, C., Conley, C., Kennedy, C., & Brenner, P. R. (2024) · International Conference on Social Networks Analysis, Management and Security (SNAMS) · doi:10.1109/snams64316.2024.10883777 · Cited by 2
G-CutMix: A CutMix-based graph data augmentation method for bot detection in social networks
Li, Y., Shi, S., Guo, X., Zhou, C., & Hu, Q. (2025) · PLoS ONE · doi:10.1371/journal.pone.0331978 · Cited by 3
Using Connected Accounts to Enhance Information Spread in Social Networks
Sela, A., Milo-Cohen, O., Kagan, E., Zwilling, M., & Ben‐Gal, I. (2019) · Studies in computational intelligence · doi:10.1007/978-3-030-36687-2_38 · Cited by 2
Impostor GAN: Toward Modeling Social Media User Impersonation with Generative Adversarial Networks
Nafees, M., Pan, S., Chen, Z., et al. (2021) · Communications in computer and information science · doi:10.1007/978-3-030-91779-1_11 · Cited by 2
“These bots are very difficult to detect for both algorithms and humans [9].”
BackgroundСтукал, Д., & Филиппов, И. Б. (2022) · Monitoring obŝestvennogo mneniâ: èkonomičeskie i socialʹnye peremeny · doi:10.14515/monitoring.2022.1.2022 · Cited by 1
Data Crawling Approaches for User Discussion Analysis on Web 2.0 Platforms
Nepiyushchikh, D., & Blekanov, I. S. (2022) · Lecture notes in control and information sciences - proceedings · doi:10.1007/978-3-030-87966-2_91 · Cited by 2
A variational-autoencoder approach to solve the hidden profile task in hybrid human-machine teams
Pescetelli, N., Reichert, P., & Rutherford, A. (2022) · PLoS ONE · doi:10.1371/journal.pone.0272168 · Cited by 4
Twitter Bots’ Detection with Benford’s Law and Machine Learning
Bhosale, S., & Troia, F. D. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-24049-2_3 · Cited by 2
Detection of Malicious Social Bots with the Aid of Learning Automata on Twitter
Vashisht, S., Gupta, S. K., Fegade, A., Dhondiyal, S. A., Kumar, R., & Revathy, G. (2022) · International Conference on Contemporary Computing and Informatics (IC3I) · doi:10.1109/ic3i56241.2022.10073346 · Cited by 2
Classifying Real and Bot Users Based on their News Spread for Combating Misinformation
Bui, T., Pham, A., Kulkarni, W., Yao, Y., & Potika, K. (2023) · IEEE International Symposium on Service-Oriented System Engineering (SOSE) · doi:10.1109/sose58276.2023.00038 · Cited by 2
Swetha, C. V., Shaji, S., & Sundaram, B. M. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-97-1961-7_17 · Cited by 3
Aboud, A., Rokbani, N., Mirjalili, S., Hussain, A., Chabchoub, H., & Alimi, A. M. (2022) · doi:10.36227/techrxiv.19461080 · Cited by 2
Towards an Adversary-Aware ML-Based Detector of Spam on Twitter Hashtags
Imam, N. H., & Vassilakis, V. G. (2023) · Lecture notes in networks and systems · doi:10.1007/978-981-99-3243-6_32 · Cited by 1
Towards understanding bogus traffic service in online social networks
He, P., Zhang, X., Lin, C., Wang, T., & Ji, S. (2024) · Frontiers of Information Technology & Electronic Engineering · doi:10.1631/fitee.2300068 · Cited by 1
“Unfortunately, fraud accounts are becoming increasingly advanced and hard to identify (Freitas et al., 2015; Cresci et al., 2017, 2019; Weeras-inghe et al., 2020).”
García-Ull, F. J., Broseta-Dupré, B., & Lamirán-Palomares, J. M. (2025) · Frontiers in Communication · doi:10.3389/fcomm.2025.1545634 · Cited by 1
Bot Detection and Credibility Score Analysis Using Random Forest and Network Analysis
Alhumaid, K. F., Akour, I. A., & Salloum, S. A. (2025) · Studies in computational intelligence · doi:10.1007/978-3-031-89175-5_13 · Cited by 1
Advanced text-based transformer architecture for malicious social bots detection
Ellaky, Z., & Benabbou, F. (2025) · Mathematical Modeling and Computing · doi:10.23939/mmc2025.03.972 · Cited by 1
Saucier, C. J., Wack, M., Linvill, D. L., Okoronkwo, A. H., Tatineni, G., & Sezgin, A. (2025) · Computers in Human Behavior · doi:10.1016/j.chb.2025.108881 · Cited by 2
Simulating Advanced Social Botnets: A Framework for Behavior Realism and Coordinated Stealth
Jin, R., & Liao, Y. (2025) · Information · doi:10.3390/info17010027 · Cited by 1
Detection of Twitter Bots using DNA-based Entropy Technique
Gilmary, R., Akila, V., Praveen, M. B., Prasath, H. R., & Vaiyapuri, G. (2022) · International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT) · doi:10.1109/iceeict53079.2022.9768516 · Cited by 1
Twitter Bot Detection Using One-Class Classifier and Topic Analysis
Rajkumar, A., Rakesh, C., Kalaivani, M. M., & Arun, G. Y. B. (2022) · Lecture notes in networks and systems · doi:10.1007/978-981-19-1012-8_56 · Cited by 1
Detection of Social Bots in Twitter Network
Duddu, M. C., & Bhavani, S. D. (2023) · Algorithms for intelligent systems · doi:10.1007/978-981-99-1435-7_53 · Cited by 1
Semantic Information Mining and Fusion Method for Bot Detection
Liang, L., Wang, X., & Liu, G. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-44201-8_23 · Cited by 1
The Spread of Fake News by Social Bots: Perspectives on Social Bot Regulation
Soares, H. (2024) · Data science, machine intelligence, and law · doi:10.1007/978-3-031-47946-5_11 · Cited by 1
Machine Learning Classifiers for Social Media Bots Detection on Twitter using Explainable AI
Gupta, S., & Sinha, A. (2023) · International Conference on Informatics (ICI) · doi:10.1109/ici60088.2023.10421550 · Cited by 1
“In order to ensure the legitimacy and authenticity of online interactions as well as to defend people and organizations from potential harm, it is crucial to identify these bots [11].”
BackgroundBiometrics in online media: an anti-crisis paradigm shift
Shilina, S. (2023) · RUDN Journal of Studies in Literature and Journalism · doi:10.22363/2312-9220-2023-28-4-741-748 · Cited by 1
Cybersecurity in social networks: An ensemble model for Twitter bot detection
Darem, A. A., Alhashmi, A. A., Alanazi, M. H., Alanezi, A. F., Said, Y., Darem, L. A., & Hussain, M. M. (2024) · International Journal of ADVANCED AND APPLIED SCIENCES · doi:10.21833/ijaas.2024.11.014 · Cited by 1
Machine Learning for Detecting Fake Accounts in OSNs: A Review
Sallah, A., Sahlaoui, H., Errousso, H., Alaoui, E. A. A., & Agoujil, S. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-031-88304-0_32 · Cited by 1
Bots Recognition in Social Networks Using the Random Forest Algorithm
Khachatrian, M. G., & Klyucharev, P. G. (2019) · Mechanical Engineering and Computer Science · doi:10.24108/0419.0001473 · Cited by 1
A Study of Blending Ensembles for Detecting Bots on Twitter
Kumar, S., Bansal, R., & Mehta, R. (2021) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-981-15-9651-3_3 · Cited by 1
Bachelor of Engineering, C. S. F. L. I., Kadam, N., Patidar, H., & Bachelor of Engineering, C. S. A. E. D. (2020) · International Journal of Recent Technology and Engineering (IJRTE) · doi:10.35940/ijrte.f8417.038620 · Cited by 3
“Fake news or rumor is an another key issue in social media Sebastian Tschiatschek et al [21] were works to recognize fake news over social media.”
BackgroundMachine Learning models for Customer Relationship Analysis to Improve Satisfaction Rate in Banking
Jia, N., Bagam, L., Fabijanczyk, P., & Ceesay, E. N. (2022) · IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS) · doi:10.1109/iemtronics55184.2022.9795855 · Cited by 1
“Text analysis for investment and sentiment analysis of online comments can be use for improving user satisfaction in online banking [5], [6], [7], [8], [9], [10] .”
MethodJain, A., Gairola, R., Jain, S., & Arora, A. (2022) · IGI Global eBooks · doi:10.4018/978-1-6684-6291-1.ch037 · Cited by 1
Benford’s Law applies to word frequency rank in English, German, French, Spanish, and Italian
Golbeck, J. (2023) · PLoS ONE · doi:10.1371/journal.pone.0291337 · Cited by 1
Unsupervised Spammer Detection in Social Networks based on User Information InterestsPreprint
Koggalahewa, D. N., Xu, Y., & Foo, E. (2021) · Research Square · doi:10.21203/rs.3.rs-798887/v1 · Cited by 1
“• The Fake Project dataset [17]: Institute of Informatics and Telematics of the Italian National Research Council (CNR) released this dataset in 2017 with both twitter spambots and genuine accounts.”
BackgroundMethodInfluentialSpammer detection in social networks based on user behaviours
Koggalahewa, D. N. (2023) · Queensland University of Technology · doi:10.5204/thesis.eprints.240782 · Cited by 1
Metadata
- Authors
- Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., & Tesconi, M.
- Affiliations
- University of Pisa (IT)
- Nokia Bell Labs & University of Padua, Paris, France
- National Research Council (IT)
- Technical University of Denmark (DK)
- Venue
- Proceedings of the 26th International Conference on World Wide Web Companion - WWW '17 Companion
- Publisher
- ACM Press
- Published
- 2017
- Pages
- 963-972
- OpenAlex
- W4289253852
- Cited by
- 381
- References
- 50
- Citations by year
- 2026: 31 · 2025: 42 · 2024: 48 · 2023: 48 · 2022: 48 · 2021: 46 · 2020: 62 · 2019: 37 · 2018: 15 · 2017: 4
- FWCI
- 24.78
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- International World Wide Web Conference (WWW) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- CINECA IRIS Institutial research information system (University of Pisa) (submittedVersion)
- IRIS Research product catalog (Sapienza University of Rome) (submittedVersion)
- Funders
- EU (654024, 675320)
- Regione Toscana (SmartNews: Social sensing for Breaking News)
- 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
- Twitter, spam social media
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-09, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
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Statistics and arithmetic are recomputed, not judged.
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