The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
Uncovering Large Groups of Active Malicious Accounts in Online Social Networks
Abstract
The success of online social networks has attracted a constant interest in attacking and exploiting them. Attackers usually control malicious accounts, including both fake and compromised real user accounts, to launch attack campaigns such as social spam, malware distribution, and online rating distortion. To defend against these attacks, we design and implement a malicious account detection system called SynchroTrap. We observe that malicious accounts usually perform loosely synchronized actions in a variety of social network context. Our system clusters user accounts according to the similarity of their actions and uncovers large groups of malicious accounts that act similarly at around the same time for a sustained period of time. We implement SynchroTrap as an incremental processing system on Hadoop and Giraph so that it can process the massive user activity data in a large online social network efficiently. We have deployed our system in five applications at Facebook and Instagram. SynchroTrap was able to unveil more than two million malicious accounts and 1156 large attack campaigns within one month.
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In the citing paper
“Identifying organic ASO activities is an open problem due to the ability of such workers to evade existing detection solutions, e.g., that leverage lockstep behaviors [32, 43, 51, 72, 73, 78, 85, 86, 89, 92, 93] or review bursts [27, 28, 35, 39, 40, 42, 44, 45, 50-53, 53, 5759, 65, 66, 84, 87, 88, 92].”
From RacketStore: measurements of ASO deception in Google play via mobile and app usage (2021) · cites this paper as [32]
In the citing paper
“Aiming to counter fraudulent activities, including like farms, Facebook has recently deployed detection tools such as CopyCatch [4] and SynchroTrap [10].”
From Measuring, Characterizing, and Detecting Facebook Like Farms (2017) · cites this paper as [10]
In the citing paper
“Another one, SynchroTrap, relies on the fact that malicious accounts usually perform loosely synchronized actions in a variety of social network context, and can cluster malicious accounts that act similarly at around the same time for a sustained period of time [10].”
From Measuring, Characterizing, and Detecting Facebook Like Farms (2017) · cites this paper as [10]
In the citing paper
“Similar to CopyCatch [4] and SynchroTrap [10], the clusters identified in the user-page bipartite graph represent near-bipartite cores, and the set of users in a near-bipartite core like the same set of pages.”
From Measuring, Characterizing, and Detecting Facebook Like Farms (2017) · cites this paper as [10]
In the citing paper
“We then evaluated the effectiveness of existing graph based fraud detection algorithms, such as CopyCatch [4] and SynchroTrap [10], and demonstrated that sophisticated like farms can successfully evade detection.”
From Measuring, Characterizing, and Detecting Facebook Like Farms (2017) · cites this paper as [10]
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Cited by
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Items 201–275 of 275
Spam Classification Based on Signed Network Analysis
Jeong, S., & Lee, K.-H. (2020) · Applied Sciences · doi:10.3390/app10248952 · Cited by 3
“SynchroTrap uses time-stamped user actions to detect malicious account groups that generate “like spam” on Facebook or “follow spam” on Instagram [19].”
BackgroundSecurity Threats and Dealing with Social Networks
Barati, R. (2022) · SN Computer Science · doi:10.1007/s42979-022-01434-0 · Cited by 3
“Attackers make malicious accounts to obtain their purpose [13] and because of the structure of OSN, malware attacks are easier in online social networks as compared to other networks.”
BackgroundA Potent Technique for Identifying Fake Accounts on Social Platforms
Kajal, K., Singh, U. K., Akhtar, N., Vishwakarma, S. K., Kumar, N., & Perwej, Y. (2023) · International Journal of Scientific Research in Computer Science Engineering and Information Technology · doi:10.32628/cseit2390425 · Cited by 3
Identifying Risky Vendors in Cryptocurrency P2P Marketplaces
Tsuchiya, T., Cuevas, A., & Christin, N. (2024) · International World Wide Web Conference (WWW) · doi:10.1145/3589334.3645475 · Cited by 8
Analytics for characterising and measuring the naturalness of online personae
Nurse, J. R. C., Erola, A., Gibson−Robinson, T., Goldsmith, M. H., & Creese, S. (2016) · Security Informatics · doi:10.1186/s13388-016-0028-1 · Cited by 3
“, posting and uploading behaviour) [7].”
Detection of fraudulent users in P2P financial market
Wang, H. (2018) · MATEC Web of Conferences · doi:10.1051/matecconf/201818906004 · Cited by 3
“Later they came up with a new invention called SynchroTrap [2] to detect synchronized attack.”
BackgroundBargar, A., Pitts, S., Butkevics, J., & McCulloh, I. (2019) · doi:10.23919/cycon.2019.8756832 · Cited by 4
“Identifying this opportunistic targeting requires different approaches, such as the detection of synchronized actions [17] that appear to focus on a single topic, set of keywords or hashtags, or users.”
BackgroundCharacterizing and detecting livestreaming chatbots
Jain, S., Niranjan, D., Lamba, H., Shah, N., & Kumaraguru, P. (2019) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1145/3341161.3345308 · Cited by 4
“SynchroTrap [23]: SynchroTrap is an unsupervised method that operates on user groups; hence, we apply it for each stream to identify constitutent chatbots.”
MethodLayered Malicious Nodes Detection with Graph Attention Network in Human-Cyber-Physical Networks
Lin, Y., Huang, Y., Hsieh, S., Lin, L., & Xia, F. (2022) · IEEE International Conference on Mobile Ad-Hoc and Sensor Systems (MASS) · doi:10.1109/mass56207.2022.00079 · Cited by 3
“SynchroTrap [15] clustered node accounts according to the similarity of their actions (e.g., photo uploading) and uncovered a large cluster of malicious accounts that had very similar behavior over a period of time.”
BackgroundMao, J., Li, X., Lin, Q., & Guan, Z. (2020) · China Communications · doi:10.23919/jcc.2020.10.006 · Cited by 3
“Another study [11] shows that more than 70% of Sybil nodes are not connected to each other, meaning that Sybil nodes may not form strongly-connected Fig.”
BackgroundThe Art of Social Bots: A Review and a Refined TaxonomyPreprint
Latah, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.03240 · Cited by 3
“A time-stamped action is represented with a tuple abstraction that allows the system design to be independent of the OSN applications that SynchroTrap protects.”
BackgroundInfluentialPrivate Two-Party Cluster Analysis Made Formal & ScalablePreprint
Meng, X., Papadopoulos, D., Oprea, A., & Triandopoulos, N. (2019) · arXiv (Cornell University) · 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
“Another line of research uses unsupervised techniques to detect anomalous users and activity clusters that deviate significantly from normal ones [29, 35, 56, 87].”
A Network Embedding Based Approach for Telecommunications Fraud Detection
Liu, X., & Wang, X. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00560-3_31 · Cited by 2
Social Media Fraud: Students Participating in Social Media Fraud
Subhash, S., verma, I., Khan, S., Diwakar, R., Garg, M., & Muabshshir, M. (2024) · International Journal of Research Publication and Reviews · doi:10.55248/gengpi.5.0524.1328 · Cited by 2
Privacy-Preserving Hierarchical Clustering: Formal Security and Efficient Approximation.
Meng, X., Papadopoulos, D., Oprea, A., & Triandopoulos, N. (2019) · Cited by 2
A New Approach to Detect User Collusion Behavior in Online QA System
Zhen-hui, Z. H. U., Yang, Z. C., & Dai, Y. (2017) · International Conference on Computer Networks and Communication Technology (CNCT) · doi:10.2991/cnct-16.2017.116 · Cited by 2
“Most studies on this topic are done through network-based features [1, 9] or behavior patterns [10, 11].”
BackgroundTOmCAT: Target-Oriented Crowd Review Attacks and Countermeasures
Kaghazgaran, P., Alfifi, M., & Caverlee, J. (2019) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v13i01.3231 · Cited by 2
“…Squicciarini 2018) to identify fraudulent users who are distant in a local graph but 1 The naming is inspired by sequencing nature of RNN models (Cao et al. 2014) with a SynchroTrap system to deal with co-ordinated Likes on selected pages and in Twitter (Viswanath et al. 2015) with the Stamper…”
MethodFARE: Schema-Agnostic Anomaly Detection in Social Event Logs
Shah, N. (2019) · IEEE International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa.2019.00049 · Cited by 2
“As a result, the broader research community has invested substantial resources on tackling a wide variety of application-specific anti-abuse anomaly detection problems spanning e-mail phishing attacks [2], malware detection [3], network intrusion [4], livestreaming abuse [5], social and content spam [6], fake account…”
BackgroundMeasuring data collection diligence for community healthcare
KARUNASENA, G. R. S., Ambiya, M. S., Sinha, A., Nagar, R., Dalal, S., Abdullah, H., Thakkar, D., Narayanan, D., & Tambe, M. (2021) · Singapore Management University Institutional Knowledge (InK) (Singapore Management University) · Cited by 2
DRAGON: Detection of Related Account Groups for Online services with uncertain graphs
Chen, B.-J., Liou, W.-C., Shih, H.-Y., & Lin, T.-N. (2021) · Globecom · doi:10.1109/globecom46510.2021.9685398 · Cited by 2
“In social media, existing methods used social graph [1]–[3] (e.g., friendship on Facebook), similar behaviors [4], [5] (e.g., shared messages or posting patterns) and loosely synchronized actions [6] (i.e., similar actions at around the same time).”
A Qualitative Analysis of Challenges in Online Fraud Detection and Investigation
Espinoza, M. D. (2024) · Advances in medical technologies and clinical practice book series · doi:10.4018/979-8-3693-3555-0.ch009 · Cited by 2
Fake reviews tell no tales? Dissecting click farming in content-generated social networks
Zheng, H., Li, N., Xue, M., Du, S., & Zhu, H. (2017) · IEEE/CIC International Conference on Communications in China (ICCC) · doi:10.1109/iccchina.2017.8330469 · Cited by 2
“Specifically, CopyCatch [19] and SynchroTrap [11], implementing mixed approaches, scored comparatively low false positive rates with respect to single feature-based approaches.”
MethodEconomic feasibility study for stealthy socialbot network establishment
Moghaddam, S. H., & Abbaspour, M. (2020) · IET Information Security · doi:10.1049/iet-ifs.2019.0152 · Cited by 2
“Cao et al. [46] proved that accounts tend to perform loosely synchronised actions in a variety of OSN applications, all from a limited set of IP addresses.”
BackgroundSecurity Analysis of Malicious Socialbots on the Web
Boshmaf, Y. (2015) · cIRcle (University of British Columbia) · doi:10.14288/1.0167277 · Cited by 2
“In contrast, the number of fake accounts involved in an attack campaign is often on the order of thousands [16].”
BackgroundMitigating Colluding Attacks in Online Social Networks and Crowdsourcing Platforms
Kamhoua, G. A. (2019) · doi:10.25148/etd.fidc007703 · Cited by 2
Analysis of Malicious Behavior on Social Media Platforms Using Agent-Based Modeling
Onuchowska, A. (2020) · Digital Commons - University of South Florida (University of South Florida) · Cited by 2
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
“Ag-gregate behavior based approaches (Cao et al., 2014; Zheng et al., 2017; Yuan et al., 2019) focus on uncovering a group of users by identifying the synchronized group activities.”
Wang, Y., Umashankar, M., Chandrasekharan, E., & Sundaram, H. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3757668 · Cited by 3
Content Abuse and Privacy Concerns in Online Social Networks
Kayes, I. (2015) · Digital Commons - University of South Florida (University of South Florida) · Cited by 1
Private Hierarchical Clustering and Efficient Approximation
Meng, X., Papadopoulos, D., Oprea, A., & Triandopoulos, N. (2021) · Cloud Computing Security Workshop · doi:10.1145/3474123.3486760 · Cited by 1
“, detect botnets [46], malicious traffic [79], compromised accounts [19], or malware [13].”
BackgroundSaeed, M. H., Ali, S., Paudel, P., Blackburn, J., & Stringhini, G. (2024) · International Symposium on Research in Attacks, Intrusions and Defenses (RAID) · doi:10.1145/3678890.3678911 · Cited by 3
“Cao et al. propose SynchroTrap , a detection system that groups malicious accounts based on their synchronized actions and timing [9], while Stringhini et al. introduce EvilCohort , a system that identifies sets of social network accounts utilized by botnets by examining communities of accounts…”
Exploring Behavioral Patterns for Security in Cloud Computing: a Case Study
Kaur, M., & Garg, P. (2025) · International Conference on Advancement in Computation & Computer Technologies (InCACCT) · doi:10.1109/incacct65424.2025.11011337 · Cited by 1
“Behavioral analysis may also be used for malicious account identification [37, 38] and risk of financial default using online money transactions [39].”
Exploring the Controlled Experiment by Social Bots
Min, Y., Zhou, Y., Jiang, T., & Wu, Y. (2021) · Big data management · doi:10.1007/978-981-16-2609-8_11 · Cited by 1
Towards Secure and Privacy-Preserving Online Social Networking Services
Gong, Z. (2015) · UC Berkeley · Cited by 1
Detecting Anomalously Similar Entities in Unlabeled Data
Friedland, L. (2021) · Scholarworks (University of Massachusetts Amherst) · doi:10.7275/9058326.0 · Cited by 1
BadLink: Combining Graph and Information-Theoretical Features for Online Fraud Group DetectionPreprint
Ban, Y., Liu, X., Zhang, T., Huang, L., Duan, Y., Liu, X., & Xu, W. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.10053 · Cited by 1
“Much literature has pointed out that the large cluster size is a crucial indicator of frauds [5, 29].”
BackgroundBan, Y., Duan, Y., & Huang, L. (2018) · arXiv (Cornell University) · Cited by 1
Early Identification of Pathogenic Social Media AccountsPreprint
Alvari, H., Shaabani, E., & Shakarian, P. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.09331 · Cited by 7
“The work of [26], used similarity to cluster accounts and uncover groups of malicious users.”
BackgroundVit, A. P., Puzis, R., & Shabtai, A. (2017) · Springer eBooks · doi:10.1007/978-1-4614-7163-9_110212-1 · Cited by 1
The Limits of Query Monitoring against Distributed Model Extraction
Khilar, S. C. (2026) · arXiv
“…2013) and Fraudar (Hooi et al., 2016) find lockstep groups through singular vectors or dense blocks; DeBot (Chavoshi et al., 2016), SynchroTrap (Cao et al., 2014) and point-process models (Sharma et al., 2021) use timing; ban-evasion detection links replacement accounts to banned ones…”
Fake Likers Detection on Facebook
Satya, B., & Satya, P. R. B. (2016)
If It Looks Like a Spammer and Behaves Like a Spammer, It Must Be a Spammer
Shmueli, E., Nouh, M., Alabdulkareem, A., Alsaleh, M., Alarifi, A., Alfaris, A., Pentland, A., Almaatouq, A., & Singh, V. K. (2016) · DSpace@MIT (Massachusetts Institute of Technology)
Towards trustworthy social computing systems
Viswanath, B. (2016) · Publications of the UdS (Saarland University) · doi:10.22028/d291-25429
“Two other works, SynchoTrap [51] and CopyCatch [45] also focused on analyzing behavior of a group of malicious identities.”
BackgroundTowards Measuring Content Coordination in Microblogs
Roussinov, D. G. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-76941-7_58
“The suspect string is typically identified by a manual investigation [5] or by automatically applying a clustering algorithm [2][1].”
BackgroundMethodCatching Loosely Synchronized Behavior in Face of CamouflagePreprint
Ban, Y., Sun, J., & Liu, X. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.08885
“To achieve the “economy of scale”, fraudsters often use many fraudulent user accounts 1 to conduct the same fraud [16, 17].”
BackgroundSearch Rank Fraud Prevention in Online Systems
Rahman, M. M. (2018) · doi:10.25148/etd.fidc006998
User Behavior to Identify Malicious Activities in Large-Scale Social Networks
Bane, M. P. A. (2019) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2019.5069
Understanding Propagation of Malicious Information Online
Alvari, H. (2020)
Measuring Data Collection Quality for Community HealthcarePreprint
Karunasena, R., Ambiya, M. S., Sinha, A., Nagar, R., Dalal, S., Thakkar, D., & Tambe, M. (2020) · arXiv (Cornell University)
Fast Track: Synchronized Behavior Detection in Streaming TensorsPreprint
Zhang, J., Liu, S., Hou, W., Bhatia, S., Shen, H., Yu, W., & Cheng, X. (2020) · arXiv (Cornell University)
Measuring Data Collection Diligence for Community Healthcare
Karunasena, R., Ambiya, M. S., Sinha, A., Nagar, R., Dalal, S., Abdullah, H., Thakkar, D., Narayanan, D., & Tambe, M. (2021) · ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO) · doi:10.1145/3465416.3483292
“Clustering has been used in past work to detect fraud in social networks [5], in internet advertising [32], in healthcare billing [17, 19].”
MethodSpam analysis and detection on microblog
Sedhai, S. (2017) · doi:10.32657/10356/69594
Vit, A. P., Puzis, R., & Shabtai, A. (2018) · Springer eBooks · doi:10.1007/978-1-4939-7131-2_110212
Alvari, H., Shaabani, E., & Shakarian, P. (2021) · SpringerBriefs in computer science · doi:10.1007/978-3-030-61431-7_1
Identifying Spam Activity on Public Facebook Pages
Azri, H., Belbachir, H., & Meddah, F. G. (2022) · Journal of Computing and Information Technology · doi:10.20532/cit.2021.1005221
“Other researchers do not necessarily propose graph-based Sybil detection schemes to identify groups of malicious users, but instead try to detect any existing group behavior characterizing a collaborative nature for these users, such as similar patterns when uploading or liking content, or following other users at aro…”
BackgroundInfluentialto 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
“Therefore, in cooperation with Facebook [20], Renren [21], or YouTube [22], researchers proposed models that leverage detailed data on social network account activities.”
BackgroundInfluentialAssessing the Credibility of Cyber Adversaries
Wells, J. A., LaFon, D. S., & Gratian, M. (2021) · International Journal of Cybersecurity Intelligence and Cybercrime · doi:10.52306/2578-3289.1088
Detecting Fake Users in Online Social Networks
Yin, Q., Li, Q. L., Gong, N. Z., Xu, K., Liang, X., & Hu, S. (2023) · Springer eBooks · doi:10.1007/978-3-031-42637-7_11
Verma, V., Upreti, A., Kothari, K., & Thukral, U. (2023) · International Conference on AI-ML Systems (AIMLSystems) · doi:10.1145/3639856.3639882
“The two main categories of conventional techniques for spotting multiple-ID accounts are feature-based techniques[3][5][9] and structure-based techniques[2][4][13][8].”
BackgroundMalicious Intent Detection Framework for Social Networks
Fausak, A. T. (2024) · doi:10.12794/metadc2332602
Spotting Sneaky Scammers: Malicious Account Detection from a Chinese Financial Platform
Yao, S., Liu, D., Guo, Z., Zhang, Z., & Hu, J. (2024) · Electronics · doi:10.3390/electronics13234742 · Cited by 2
Visual Recognition Systems for Real-Time Sign Language Transliteration
Singh, A. K., Saxena, S., Singh, A., Rai, V., & Bajaj, R. K. (2025) · Algorithms for intelligent systems · doi:10.1007/978-981-96-3256-5_24
STM: A Spatio-Temporal Model for Dynamic Graph Fraud Detection
Wang, Y., Chen, R., Chen, B., Yu, Z., Wang, Y., & Wu, T. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-3906-2_3
A Novel Parallel Graph Computing Model for Unsupervised Fraud Detection
Wang, J., Sun, R., Chen, F., Zhang, W., Feng, P., & Yu, C. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-4158-4_25
Meta-Graph Enhanced Heterogeneous Graph Neural Network for Cyber Attack Behavior Detection
Bi, S., Zeng, L., & Gu, Z. (2025) · IEEE International Conference on Data Science in Cyberspace · doi:10.1109/dsc67331.2025.00024
“Cao et al. [6] constructed an unsupervised by improving the usage of aggregated behavioral patterns of machine learning models to discover malicious accounts.”
Multiple identities detection in online social media
Yamak, Z. (2018) · theses.fr (ABES) · doi:10.70675/5a98bf48z0340z4837zbad3z55961dfdbdb7 · Cited by 3
“Two other works, SynchoTrap by [Cao et al., 2014] and CopyCatch by [Beutel et al.”
Discouraging abusive behavior in privacy-preserving decentralized online social networks
García-Recuero, Á. (2017) · theses.fr (ABES) · doi:10.70675/498740f2z5e14z4d2fz936dz2577e5d5b173
Anti-Abuse Protection of Online Social Networks using Machine Learning
El-Mawass, N. (2020) · theses.fr (ABES) · doi:10.70675/4da28cc2z492dz4ef1z8221z98c8beb02143
A Systematic Literature Review: Instagram fake Account Detection Based on Machine Learning
Raj, S., Kumari, R., Verma, M., Kumar, G., Agarwal, G., & Khan, N. A. (2026) · International Conference on Advancing Sustainable Solutions through Technologies (ICASST) · doi:10.1109/icasst68917.2026.11484442
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
“Network based models and clustering algorithms are often used for identifying coordinated activities on social media [5, 29].”
SessionSense: Detecting Web Attacks Through Session-Level Behavioral Feature Engineering
Cujbă, M.-C., Chiru, C.-G., Bica, I., & Ciuvăţ, A.-V. (2026) · International Conference on Communications (COMM) · doi:10.1109/comm70054.2026.11591274
“Session-level analysis has been studied in adjacent domains: fraud detection [9] and continuous authentication [10] both benefit from aggregating behavior across sessions.”
Martínez-Durive, O. E., Úcar, I., Smoreda, Z., Moro, E., & Fiore, M. (2026) · ACM Internet Measurement Conference (IMC) · doi:10.1145/3777912.3839827
“The trends above also raise growing concerns about fairness and manipulation in democratic processes, due to increasing risks that malicious actors exploit online ecosystems to amplify divisive narratives and misinformation, particularly during election periods [23, 25, 31, 73].”
Metadata
- Authors
- Cao, Q., Yang, X., Yu, J., & Palow, C.
- Affiliations
- Duke University (US)
- Meta (United States) (US)
- ORCID
- Yang, Xiaowei
- Venue
- Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security
- Publisher
- ACM
- Published
- 2014-11-03
- Pages
- 477-488
- ISSN
- 1543-7221 (ISSN-L)
- OpenAlex
- W2125490153
- Cited by
- 274
- References
- 37
- Citations by year
- 2026: 7 · 2025: 8 · 2024: 13 · 2023: 18 · 2022: 17 · 2021: 32 · 2020: 40 · 2019: 31 · 2018: 39 · 2017: 38 · 2016: 18 · 2015: 13
- FWCI
- 19.62
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM SIGSAC Conference on Computer and Communications Security (CCS) (publishedVersion)
- Funders
- Division of Computer and Network Systems (CNS-0845858, CNS-1017858)
- Topics
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Social Sciences › Law › Cybercrime and Law Enforcement Studies
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Keywords
- malicious account detection, social networking sites, fake accounts, malware distribution, Hadoop, Giraph, Facebook, Instagram
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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