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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Aboud, A., Rokbani, N., Mirjalili, S., Qahtani, A. M., Alharithi, F. S., Almutiry, O., Hussain, A., Alimi, A. M., & Chabchoub, H. (2023) · doi:10.36227/techrxiv.19461080.v2 · Cited by 1
Bots on Social Media: The Past, Present and Future
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Engineering collective attention in the age of artificial intelligence
Zhu, R. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-01084-8 · Cited by 1
Detection of bots in social networks by means of multilayered perceptron
Баумана, М. И. Н. Э., Khachatrian, M. G., Чепик, П. И., & Баумана, М. И. Н. Э. (2019) · Politechnical student journal · doi:10.18698/2541-8009-2019-4-463
“Для оценки качества обнаружения ботов были использованы данные, представленные в статье [9].”
An Investigation on Multi View Based User Behavior Towards Spam Detection in Social Networks
Koggalahewa, D. N., & Xu, Y. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-26142-9_2
Determination of Bot Groups in Social Network Accounts by Multiple Sequence Alignment Method
Sevi, M., & Aydın, İ. (2019) · International Informatics and Software Engineering Conference (IISEC) · doi:10.1109/ubmyk48245.2019.8965460
“Figure 1 shows a summary of the data set [7].”
BackgroundFeature Selection Optimization Using Genetic Algorithm for Spambot Detection in an OSN
Singh, A., & Sood, M. (2020) · Communications in computer and information science · doi:10.1007/978-981-15-4451-4_26
Two-stage Unsupervised Approach for Combating Social Spammers
Koggalahewa, D. N., Xu, Y., & Foo, E. (2020) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci47803.2020.9308315 · Cited by 1
“In terms of mentioning, Fake Project dataset does not have users with many mutual mentions.”
BackgroundInfluentialA Multi-agent Approach for Online Twitter Bot Detection
Abreu, J. V. F., Ralha, C. G., & Gondim, J. J. C. (2021) · Colección Jornadas y congresos · doi:10.18239/jornadas_2021.34.03
A Hierarchical Attention-Based Neural Network Model for Socialbot Detection in OSN
Fazil, M., Sah, A. K., & Abulaish, M. (2020) · IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) · doi:10.1109/wiiat50758.2020.00146
“The proposed model is evaluated over the dataset provided by [31] which includes three socialbot datasets, namely, social spambots#1 ( SD 1 ), social spambots#2 ( SD 2 ), social spambots”
MethodA Custom Classifier to Detect Spambots on CRESCI-2017 Dataset
Thavasimani, K., & Srinath, N. K. (2021) · Lecture notes in electrical engineering · doi:10.1007/978-981-16-1338-8_16
A variational-autoencoder approach to solve the hidden profile task in hybrid human-machine teamsPreprint
Pescetelli, N., Reichert, P., & Rutherford, A. (2021) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/67jku
Identifying Bots on Twitter with Benford’s Law
Bhosale, S. (2021) · doi:10.31979/etd.4ad2-q4wm
Persily, N., & Tucker, J. A. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108890960.001
Online Social Networks Misuse, Cyber Crimes, and Counter Mechanisms
Rao, S., Verma, A. K., & Bhatia, T. (2022) · IGI Global eBooks · doi:10.4018/978-1-6684-5594-4.ch023
Persily, N., & Tucker, J. A. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108890960.015
A variational-autoencoder approach to solve the hidden profile task in hybrid human-machine teamsPreprint
Pescetelli, N., & Reichert, P. (2021) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/s67zf
Aboud, A., Rokbani, N., Mirjalili, S., Qahtani, A. M., Alharithi, F. S., Almutiry, O., Hussain, A., Alimi, A. M., & Chabchoub, H. (2022) · doi:10.36227/techrxiv.19461080.v1
Kollányi, B. (2022) · CUB Ph.D. Dissertations (Corvinus University of Budapest) · doi:10.14267/phd.2022050
“The broader literature on automated social media accounts includes papers on spambots (Chen & Subramanian, 2018, Cresci et al., 2017), bots promoting academic papers (Haustein et al., 2014), newsbots and chatbots used by the media (Diakopoulos, 2019; Jones & Jones, 2019), bots used in crisis…”
BackgroundA Deep Learning Approach for Robust Detection of Bots in Twitter Using Transformers Model
Pazhanivel, M. K., Ajai Kumar., B., Mageshwaran., M., & Dhivakar., K. (2022) · International Journal of Advanced Research in Science Communication and Technology · doi:10.48175/ijarsct-4610
Social Bot Detector using Graph Neural Networks
Nguyen, H.-D., Nguyen, Q. D., Pham, H. L., et al. (2022) · RIVF International Conference on Computing and Communication Technologies (RIVF) · doi:10.1109/rivf55975.2022.10013786
“The majority of Twitter social bot detection studies employ the MIB dataset, which is made up of two distinct datasets we shall call to as MIB-1 [11] and MIB2 [12].”
Backgroundto Protect the Public Opinion Against New Types of Bots?
Reubold, J. L., Escher, S., Wressnegger, C., & Strufe, T. (2022) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata55660.2022.10020758
Towards a Comprehensive Approach for Socialbot Detection on Twitter: Integrating Multiple FeaturesPreprint
Owais, M., Shoaib, M., & Waseem, M. (2023) · Research Square · doi:10.21203/rs.3.rs-2764404/v1
Aboud, A., Rokbani, N., Mirjalili, S., Hussain, A., Chabchoub, H., & Alimi, A. M. (2023) · Research Square · doi:10.21203/rs.3.rs-3154443/v1
Research on measuring happiness
Deng, Y. (2023) · Advances in Education, Humanities and Social Science Research · doi:10.56028/aehssr.7.1.614.2023
“There are not just a few bots on the internet, with one study showing bot presence at 8-18% on some social media platforms, which would introduce a considerable amount of bias into internet-based measurements (Cresci et al., 2017; Fukuda et al., 2020; Kosinski, 2023).”
Using graph solutions to identify "troll farms" and fake news propagation channels
Sulej, P., & Hryniów, K. (2023) · Annals of Computer Science and Information Systems · doi:10.15439/2023f2738
“Due to the known problems with the collection download and the Twitter limits [6][12][14][15], it was eventually possible to obtain 6 240 964 unique identifiers of users.”
BackgroundSocial Robot Detection Based on User Behavioral RepresentationPreprint
Ma, X., Yan, Z., & Li, Y. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4634069
I’m not a human: A comparison of bot and human self-presentation
Madahali, L., & Tian, J. W. (2023) · IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) · doi:10.1109/uemcon59035.2023.10316069
“We use two sets of data from [51], fake followers, genuine accounts, and three sets of spambot.”
MethodAnalyzing the Efficacy of Bot Detection Methods on Twitter/XPreprint
Javed, D., Zaman, N., Khan, N. A., Ray, S. K., Mazroa, A. A., Ayaz, R., & Das, S. R. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4665612
A Social Bot Recognition Method Combing Emojis InformationPreprint
Wang, X., Wang, Z., Wang, K., Chen, K., Gao, M., & Kangfeng, Z. (2024) · Research Square · doi:10.21203/rs.3.rs-4223128/v1
A Social Bot Detection Method Using Multi-features Fusion and Model Optimization Strategy
Huang, X., Li, S., Li, S., et al. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-2390-4_24
Munir: Weakly Supervised Transformer for Arabic Computational Propaganda Detection on Social Media
Almotairy, B. M. (2024) · Journal of Electrical Systems · doi:10.52783/jes.3414
“On the other hand, malicious accounts continuously change their behaviors and techniques, making them easier to escape from the detectors [5].”
BackgroundAkhila, V. H., & Chacko, A. M. (2024) · ACM Symposium on Applied Computing · doi:10.1145/3605098.3636038
“For this work, we have chosen Twitter as our OSN and we used the Cresci, Di Pietro, Petrocchi, Spognardi and Tesconi (2017) dataset (MIB dataset) in our work [8].”
MethodMalicious Intent Detection Framework for Social Networks
Fausak, A. T. (2024) · doi:10.12794/metadc2332602
Botnet Identification on Twitter: A Novel Clustering Approach Based on Similarity
Samper-Escalante, L. D., Loyola‐González, O., Monroy, R., & Medina‐Pérez, M. A. (2024) · IEEE Access · doi:10.1109/access.2024.3471630
“Khalil et al. [44] used four botnet datasets from the public collection of Cresci et al. [38] to compare two clustering methods from the literature: DBSCAN [46] and K-Means [47].”
BackgroundMethodInfluentialWang, X., Wang, Z., Chen, K., et al. (2024) · doi:10.1145/3709026.3709117
“Nowadays, there are social robot networks controlled by robot managers that can more effectively influence users through collaboration and synchronization [2, 4, 5].”
Efficient Internet Community Analysis using Integrated ADB-CB for Fake Account Detection
Bhaskar, S., Ramani, D. R., Srujana, R., et al. (2024) · doi:10.1109/ciscsd63381.2024.00044
OSN Bots Traffic Transformer : MAE-Based Multimodal Social Bots Behavior Pattern Mining
Zhai, H., Liang, R., Li, Z., et al. (2024) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom63139.2024.00175
“Cresci et al.[3], and others[4] [5], identify real users and bot accounts by analyzing content topics, language style, and account activity patterns.”
Detection of LLM-powered bots using image classification
Paolo, E. D., Petrocchi, M., & Spognardi, A. (2025) · First Monday · doi:10.5210/fm.v30i5.13651
Self-citationdos Santos, R. S. R. (2025) · Advances in wireless technologies and telecommunication book series · doi:10.4018/979-8-3693-9999-6.ch013 · Cited by 1
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Exploring Writing Style Consistency to Timely Identify Heterogeneous Social Bots
Laudanna, S., Cardaioli, M., Sorbo, A. D., Visaggio, C. A., & Conti, M. (2025) · Lecture notes in social networks · doi:10.1007/978-3-031-85386-9_21
Generative AI - Signal to Noise
Kane, A., Correia, R. A., Healy, K., & Jackson, A. L. (2025) · Digital Society · doi:10.1007/s44206-025-00209-3
Adapting Bot Detection Models for Romania’s Disinformation Ecosystem
Stoica, S. E. (2025) · European Conference on Cyber Warfare and Security · doi:10.34190/eccws.24.1.3573
Lightweight Early-Warning Bot Detection on X (Twitter): Temporal Patterns and Entropy Insights
Alipour, S. A., Janssen, J., Orji, R., et al. (2025) · IEEE Annual Computers, Software, and Applications Conference (COMPSAC) · doi:10.1109/compsac65507.2025.00040
Saucier, C. J., Linvill, D. L., Okoronkwo, A. H., Tatineni, G., Sezgin, A., & Wack, M. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5492309
Classification of social media users via generalized multilevel functional data analysis
Weishampel, A., Staicu, A., & Rand, W. M. (2025) · Arabian Journal of Mathematics · doi:10.1007/s40065-025-00576-2
Milea, T. D., & Siminiuc, R. (2025) · RoEduNet Conference: Networking in Education and Research (RoEduNet) · doi:10.1109/roedunet68395.2025.11208498
“More advanced approaches now use machine learning models trained on large labeled datasets of known bots and human accounts [47].”
Who Pays for Payment Fraud? Detection and Liability Rules under Strategic Fraudster AdaptationPreprint
Mosk, T. C., Balasubramaniam, V., & Uettwiller, A. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5703762
Bot-Induced Social Media Manipulation and Stock Market DistortionPreprint
Bradley, D., Hanousek, J., Hanousek, J., et al. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5912222
Moreno, F., Perdomo-Quinteiro, P., Hernández-Peñaloza, G., Álvarez, F., Belmonte-Hernández, A., & Barbero-Álvarez, M. A. (2026) · Social Network Analysis and Mining · doi:10.1007/s13278-025-01567-z
“A large body of research has relied on metadata features including account age, posting frequency, follower/following ratios, or profile attributes combined with textual analysis of user-generated content (Ferrara et al. 2016 ; Subrahmanian et al. 2016; Cresci et al. 2017).”
InfluentialBehavior-Aware Graph Attention Network for Spam Detection in Social Networks
Wanza, N. E., Deepthi, L. R., Lekshmi Nandana, M., et al. (2025) · International Conference on Communication and Electronics Systems (ICCES) · doi:10.1109/icces67310.2025.11337185
Feature Selection Techniques for Enhancing Sybil Accounts Detection in Online Social Networks
Sonkhla, D. (2025) · Indian Journal of Computer Science · doi:10.17010/ijcs/2025/v10/i5/175883
Sinha, A., Saxena, B., & Sabherwa, S. (2025) · doi:10.1109/c2i666499.2025.11366994
Multi-classification Approach for Malicious Social Bots Detection Based on Transformers
Ellaky, Z., & Benabbou, F. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-15147-6_33
Anti-Abuse Protection of Online Social Networks using Machine Learning
El-Mawass, N. (2020) · theses.fr (ABES) · doi:10.70675/4da28cc2z492dz4ef1z8221z98c8beb02143
Comparative Study of BiLSTM and XGBoost for Twitter Bot Detection
Alwi, J. R. P., & Fitriyani, F. (2025) · International Conference on Data Science and Geoinformatics (ICDSG) · doi:10.1109/icdsg67714.2025.11381307
Breaking the Imitation Game: Can LLMs Fool Humans and Machines Alike?
Ullah, U., Laudanna, S., Sorbo, A. D., & Visaggio, C. A. (2026) · International Journal of Intelligent Systems · doi:10.1155/int/8117975
Truth, Trust, and Technology: Political Communication in the Era of Synthetic MediaPreprint
Garajamirli, N. (2026) · Research Square · doi:10.21203/rs.3.rs-9473319/v1
Behavior Change as a Signal for Identifying Social Media Manipulation
Ariyarathne, I., Ariyarathne, G., Flammini, A., Menczer, F., & Nwala, A. C. (2026) · ACM Web Science Conference (WebSci) · doi:10.1145/3795766.3799748 · Cited by 1
“# Bots # Humans astroturf-20 [32] 505 0 botometer-feedback-19 [41] 123 364 botwiki-19 [42] 695 0 celebrity-19 [41] 0 20,911 cresci-17 [11] 5,812 2,744 cresci-rtbust-19 [25] 352 340 cresci-stock-18 [12] 6,926 6,155 gilani-17 [21] 1,058 1,381 midterm-18[42] 0 7,409 political-bots-19 [41] 62 0…”
When bots mislead markets: asymmetric contamination risk in sentiment-based ınvestment decisions
Teti̇k, M. (2026) · Future Business Journal · doi:10.1186/s43093-026-00863-0
“Cresci et al. [6] developed early detection approaches based on behavioral patterns, while Shao et al. [23] and Yang et al. [30] examined the role of bots in information dissemination through network structures.”
Account-History Features for Social Bot Detection in the Era of Large Language ModelsPreprint
Katyal, G. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6821398
Unified network ensemble and chunk-based feature selection for improved social bot detection
Sharma, J., & Borah, S. (2026) · Knowledge and Information Systems · doi:10.1007/s10115-026-02807-0
EntroBot: Entropy-Based Modeling of Account Behavior for Social Bot Detection
Jiang, S.-Y., Xie, Y., Xu, X.-K., & Lü, L. (2026) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2026.3695076
“In this study, we first selected the Cresci-2017 dataset because it contains multiple well-characterized social bot categories, enabling a systematic analysis and comparison of behavioral patterns across different bot types [39].”
Jain, A., Jaulkar, P., Agrawal, A., Garvin, F., & Khobragade, R. H. (2026) · Cureus Journal of Computer Science. · doi:10.7759/s44389-026-00148-1
RB-GNN: Securing Social Collaboration via Residual Behavior Graph Learning
Wang, Z., Ji, L., & Liu, S. (2026) · International Conference on Computer Supported Cooperative Work in Design (CSCWD) · doi:10.1109/cscwd68734.2026.11581654
“To evaluate the effectiveness of the proposed RB-GNN in collaborative social environments, we utilize two widely adopted benchmarks: Cresci-2015 [18] and TwiBot-20 [19].”
BotCHF: camouflage-heterogeneity-aware fusion for social bot detection
Wang, X., Shi, S., Qiao, K., Hu, H., Liu, Z., Zhang, Y., Han, J., & Yan, B. (2026) · Journal of King Saud University - Computer and Information Sciences · doi:10.1007/s44443-026-01013-w
“However, such methods require substantial domain expertise, and carefully designed features can be easily circumvented through targeted evasion (Cresci et al. 2017).”
An Explainable Feature-Based Approach for Understanding Social Bots Behaviour
López-Joya, S., Diaz-Garcia, J. A., Ruiz, M. D., & Martín-Bautista, M. J. (2026) · Applied System Innovation · doi:10.3390/asi9070150
“This is a dataset of user accounts and tweets obtained from X with the help of users of the CrowdFlower platform [50].”
SNBot: Modeling Self–Neighborhood Representation Discrepancy for Social Bot Detection
Lin, Q., & Zhou, J. (2026) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3805712.3809610
Distinguishing Human from Bot Texts: A Graph-Based and Few-Shot Learning Approach
Minshidawi, O. A., & Vahabie, A. (2026) · Journal of Information Systems and Telecommunication (JIST) · doi:10.66224/jist.49750.14.53.59
Arya, M., Gupta, R., Sarkar, S., et al. (2026) · International Conference on Computing, Communication, Security and Intelligent Systems (IC3SIS) · doi:10.1109/ic3sis69949.2026.11608305
“The architecture of GAT takes into account attention computations and it has [11].”
A HybridBERT and Metadata Deep Learning for Robust Detection of Twitter Bots
Ahamad, S. S., Basha, S. K. M., Rizwana, S., Sireesha, M., Tirumalarao, S. N., Kumar, M. P., & Mastanvali, S. C. (2025) · IEEE International Conference on Innovate for Humanitarian: Tech Solutions for Global Challenge (ICIH) · doi:10.1109/icih67754.2025.11608477
An Explainable AI-based XGBoost Model for Spambot Detection Across Multiple Social Media Platforms
Dhanni, C., & Pallavi, K. V. (2026) · doi:10.1109/icdca69396.2026.11620085
OTPCL: Optimal Transport Driven Pseudo-Labeling with Contrastive Learning for Social Bot Detection
Xu, R., Hu, M., Yang, X., Jiang, M., Wang, Z., Gao, H., & Cheng, R. (2026) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3770855.3818069
“Early detectors mainly relied on handcrafted profile and behavioral features with conventional classifiers [5, 7, 36, 38, 41, 42].”
An Ethical and Emotionally Intelligent Social Media Plugin Using Responsible & Explainable AI
Wickremasinghe, N. S., Ullandupitiya, U. P. L. I., Jayawardena, D. S., Jayanetti, J. K. D. D. T., Rathnayake, S. J., & Nawarathne, M. (2026) · International Conference on Emerging Smart Technologies and Applications (eSmarTA) · doi:10.1109/esmarta70636.2026.11652165
“Social bot detection has advanced from features and behavioral patterns to deep learning and generalization-based methods [2], [17].”
A Review of Models and Methods for Assessing Social Media User Influence
Zhang, Z. (2026) · International Conference on Digital Society and Artificial Intelligence (DSAI) · doi:10.1145/3833541.3833542
Mahesh Sankar, S., Renjith, M., R, S. N. M., & Sreenivasan, A. (2026) · International Conference on Inventive Systems and Control (ICISC) · doi:10.1109/icisc69558.2026.11681691
Wahlberg, L. (2026) · Scandinavian Political Studies · doi:10.1111/1467-9477.70060
Erazo, J. L. S., & Cruz‐Cárdenas, J. (2026) · Informatics · doi:10.3390/informatics13100156
Temporally Robust Misinformation Spreader Detection via Behavioral Fingerprinting
Jadhav, A., Barhate, Y., Sable, D., Aghav, C., Shinde, S., & Buchade, A. (2026) · International Conference on Computing Communication Control and automation · doi:10.1109/iccubea69821.2026.11701812
“Cresci et al. [30] have proved that behavioral patterns in social spambots still remain detectable even if their content is used for camouflage.”
Mahadev, V. (2026) · Research Square · doi:10.21203/rs.3.rs-9815689/v1
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
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