The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Detecting spammers on social networks
Abstract
Social networking has become a popular way for users to meet and interact online. Users spend a significant amount of time on popular social network platforms (such as Facebook, MySpace, or Twitter), storing and sharing a wealth of personal information. This information, as well as the possibility of contacting thousands of users, also attracts the interest of cybercriminals. For example, cybercriminals might exploit the implicit trust relationships between users in order to lure victims to malicious websites. As another example, cybercriminals might find personal information valuable for identity theft or to drive targeted spam campaigns.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
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?
In the citing paper
“Such feature sets are then used to train and test machine learning classifiers in order to distinguish between humans and spammers.”
From Fame for sale: Efficient detection of fake Twitter followers (2015) · cites this paper as [8]
In the citing paper
“Both of them propose a list of features to be extracted from manually classified datasets of accounts.”
From Fame for sale: Efficient detection of fake Twitter followers (2015) · cites this paper as [8]
In the citing paper
“With both the [2] and [8] feature sets, BN, kNN and LR classifiers achieve, overall, worse performances.”
From Fame for sale: Efficient detection of fake Twitter followers (2015) · cites this paper as [8]
In the citing paper
“3. the content of spambots' tweets exhibits the so-called message similarity ;”
From Fame for sale: Efficient detection of fake Twitter followers (2015) · cites this paper as [8]
In the citing paper
“The study presented in [8] focuses on spambot detection.”
From Fame for sale: Efficient detection of fake Twitter followers (2015) · cites this paper as [8]
In the citing paper
“The ones built over the feature set of [2] achieve slightly better results.”
From Fame for sale: Efficient detection of fake Twitter followers (2015) · cites this paper as [8]
In the citing paper
“1. spambots do not have thousands of friends;”
From Fame for sale: Efficient detection of fake Twitter followers (2015) · cites this paper as [8]
In the citing paper
“In addition, all the classifiers based on the feature set by [2] have a higher AUC , when compared with the ones built with the feature set by [8].”
From Fame for sale: Efficient detection of fake Twitter followers (2015) · cites this paper as [8]
In the citing paper
“Anyway, the latter also obtain high detection levels: accuracy, precision, and recall are around 0.98 for RF, D and J48, with a MCC of around 0.96.”
From Fame for sale: Efficient detection of fake Twitter followers (2015) · cites this paper as [8]
In the citing paper
“The lower precision and recall with respect to the ones obtained using the set of Yang et al. [2] show that the features of Stringhini et al. [8] exhibit the tendency to consider as fake followers some human accounts.”
From Fame for sale: Efficient detection of fake Twitter followers (2015) · cites this paper as [8]
In the citing paper
“total tweets 5. spambots have a high friends ( followersˆ2 ) ratio value (i.e., lower ratio values mean legitimate users).”
From Fame for sale: Efficient detection of fake Twitter followers (2015) · cites this paper as [8]
In the citing paper
“The authors exploit several characteristics that can be gathered crawling an account's details, both from its profile and timeline. For each investigated account, such characteristics are exploited in a Random Forest algorithm [23, 43], that outputs if the account is a spambot or not.”
From Fame for sale: Efficient detection of fake Twitter followers (2015) · cites this paper as [8]
In the citing paper
“Even if the proposed features have been originally designed for spam detection, here, for the first time, we consider them to spot another category of Twitter accounts, i.e., the fake followers.”
From Fame for sale: Efficient detection of fake Twitter followers (2015) · cites this paper as [8]
In the citing paper
“4. spambots have a high tweets containing URLs ratio ( URL ratio );”
From Fame for sale: Efficient detection of fake Twitter followers (2015) · cites this paper as [8]
In the citing paper
“2. spambots have sent less than 20 tweets;”
From Fame for sale: Efficient detection of fake Twitter followers (2015) · cites this paper as [8]
In the citing paper
“We start observing that the Class C classifiers, built over all the features considered in our study, perform better than all the other ones, including the classifiers using the feature sets of Yang et al. [2] and Stringhini et al. [8], as reported in Table 12.”
From Fame for sale: Efficient detection of fake Twitter followers (2015) · cites this paper as Stringhini et al. [8]
In the citing paper
“meaning that the latter is able to miss fewer fakes than the former one after pruning.”
From Fame for sale: Efficient detection of fake Twitter followers (2015) · cites this paper as Stringhini et al. [8]
In the citing paper
“The decision tree obtained with the feature set of Stringhini et al. [8] has 22 leaves, 43 nodes, and a height of 7, whereas the best feature is the friends/(followersˆ2) ratio that places at the root.”
From Fame for sale: Efficient detection of fake Twitter followers (2015) · cites this paper as [8]
In the citing paper
“Looking at the tree structure, we observe some interesting differences between the fake followers in our BAS dataset and the spam accounts characterized in [8] and [2].”
From Fame for sale: Efficient detection of fake Twitter followers (2015) · cites this paper as [8]
In the citing paper
“Furthermore, we clearly observe that the pruned version of Stringhini et al. [8] reduces its recall of 0.017, while the one of Yang et al. [2] only drops of 0.004”
From Fame for sale: Efficient detection of fake Twitter followers (2015) · cites this paper as Stringhini et al. [8]
In the citing paper
“Overall, the results of this experiment show that, even reducing the features, it is possible to have a detection rate higher than 0.95 (as in the last lines of Table 13, for [8] and [2], respectively).”
From Fame for sale: Efficient detection of fake Twitter followers (2015) · cites this paper as Stringhini et al. [8]
In the citing paper
“Similarly, we have seen that the feature set proposed by Yang et al. [2] seems to be slightly more effective than the one proposed by Stringhini et al. [8], when used in feature-based classifiers aiming at fake followers detection.”
From Fame for sale: Efficient detection of fake Twitter followers (2015) · cites this paper as Stringhini et al. [8]
In the citing paper
“Unlike [30, 44], we leverage timeline-based features for the detection of fake accounts.”
From Measuring, Characterizing, and Detecting Facebook Like Farms (2017) · cites this paper as [44]
Citations
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Jagatic, T. N., Johnson, N. A., Jakobsson, M., & Menczer, F. (2007) · Communications of the ACM · doi:10.1145/1290958.1290968 · Cited by 1,156
Krishnamurthy, B., Gill, P., & Arlitt, M. (2008) · Proceedings of the first workshop on Online social networks · doi:10.1145/1397735.1397741 · Cited by 697
All your contacts are belong to us
Bilge, L., Strufe, T., Balzarotti, D., & Kirda, E. (2009) · International World Wide Web Conference (WWW) · doi:10.1145/1526709.1526784 · Cited by 551
Detecting spam in a Twitter network
Schoenebeck, S. Y., Romero, D. M., Schoenebeck, G. R., & boyd, D. (2009) · First Monday · doi:10.5210/fm.v15i1.2793 · Cited by 349
Social networks and context-aware spam
Brown, G., Howe, T., Ihbe, M., Prakash, A., & Borders, K. (2008) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/1460563.1460628 · Cited by 138
Social Honeypots: Making Friends With A Spammer Near You.
Webb, S., Caverlee, J., & Pu, C. (2008) · Cited by 110
Flagged first, then by citations
Research works printed but not linked: 3
- Sophos facebook id probe. http://www.sophos.com/pressoffice/news/articles/2007/08/facebook.html , 2008 . Sophos facebook id probe. http://www.sophos.com/pressoffice/news/articles/2007/08/facebook.html, 2008.
- J. Baltazar , J. Costoya , and R. Flores . Koobface: The largest web 2.0 botnet explained . 2009 . J. Baltazar, J. Costoya, and R. Flores. Koobface: The largest web 2.0 botnet explained. 2009.
- Harris Interactive Public Relations Research . A study of social networks scams . 2008 . Harris Interactive Public Relations Research. A study of social networks scams. 2008.
Cited by
Sorted by citations
Items 601–700 of 783
A Context-aware Description for Content Filtering on Video Sharing Social Networks
da Luz, A., Junior, E. A. D. V., & Araújo, A. D. A. (2012) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2012.63 · Cited by 2
A cascading framework for uncovering spammers in social networks
Chen, Z., Yang, J., & Wang, J. H. (2014) · IFIP Networking Conference (IFIP Networking) · doi:10.1109/ifipnetworking.2014.6857080 · Cited by 2
Framework for collecting social network events
Fonseca, H., Rocha, E., Salvador, P., & Nogueira, A. (2014) · International Telecommunications Network Strategy and Planning Symposium · doi:10.1109/netwks.2014.6959218 · Cited by 2
Cybersecurity Snapshot: Google, Twitter, and Other Online Databases
Rawal, B. S., Eberhardt, G., & Lee, J. (2016) · Journal of Advanced Computer Science & Technology · doi:10.14419/jacst.v5i1.6181 · Cited by 2
Washha, M., Mezghani, M., & Sèdes, F. (2017) · Ingénierie des systèmes d information · doi:10.3166/isi.22.3.65-88 · Cited by 2
Hiding in Plain Sight: The Anatomy of Malicious Pages on Facebook
Dewan, P., Bagroy, S., & Kumaraguru, P. (2018) · Lecture notes in social networks · doi:10.1007/978-3-319-78196-9_2 · Cited by 2
Toward Evaluating Trustworthiness of Social Networking Site Users: Reputation-Based Method
Algarni, A., Almakrami, H. A. A., & Alarifi, A. (2019) · Archives of Business Research · doi:10.14738/abr.73.6265 · Cited by 2
Application of System Calls in Abnormal User Behavioral Detection in Social Networks
Zhang, S., Jiang, F., & Qin, M. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-17642-6_8 · Cited by 2
Trust exploitation and attention competition: A game theoretical model
Fu, H., Li, H., Zheng, Z., Hu, P., & Mohapatra, P. (2016) · IEEE Military Communications Conference (MILCOM) · doi:10.1109/milcom.2016.7795503 · Cited by 2
SECURITY ISSUES IN SOCIAL NETWORKS
Shikhaliyev, R. (2016) · Problems of Information Society · doi:10.25045/jpis.v07.i2.08 · Cited by 2
Machine Learning For Prediction Of Malicious Or SPAM Users On Social Networks
Kumar, G., & Rishiwal, V. (2020) · International journal of scientific and technology research · Cited by 2
Malicious Message Detection on Twitter via Dissemination Paths
Tuttle, C. A., Patel, S., & Yue, H. (2020) · 2012 International Conference on Computing, Networking and Communications (ICNC) · doi:10.1109/icnc47757.2020.9049782 · Cited by 2
Hoax Detection on Twitter using Feed-forward and Back-propagation Neural Networks Method
Kencana, C. W. (2020) · Cited by 2
Comparison of Filter and Wrapper Based Feature Selection Methods on Spam Comment Classification
Anggraeni, A. N., Mustofa, K., & Priyanta, S. (2021) · IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · doi:10.22146/ijccs.66965 · Cited by 2
Analysis of Twitter Spam Detection Techniques-A Review
Singh, M. (2021) · International Conference on Technological Advancements and Innovations (ICTAI) · doi:10.1109/ictai53825.2021.9673356 · Cited by 2
Discerning Spam in Social Networking Sites
Yadav, S., Saini, A., Dhamija, A., & Narnauli, Y. (2016) · Advances in Vision Computing An International Journal · doi:10.5121/avc.2016.3201 · Cited by 2
Adoption of Social Media as Communication Channels in Government Agencies
Alotaibi, R. M., Ramachandran, M., Kor, A.-L., & Far, A. H. (2015) · Advances in electronic government, digital divide, and regional development book series · doi:10.4018/978-1-4666-8629-8.ch003 · Cited by 2
Evaluating social spammer detection systems
Ho, K., Liesaputra, V., Yongchareon, S., & Mohaghegh, M. (2018) · Australasian Computer Science Week Multiconference (ACSW) · doi:10.1145/3167918.3167936 · Cited by 2
Economic 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
Tracking triadic cardinality distributions for burst detection in high-speed graph streams
Zhao, J., Wang, P., Chen, Z., Ding, J., Lui, J. C. S., Towsley, D., & Guan, X. (2021) · Knowledge and Information Systems · doi:10.1007/s10115-021-01543-x · Cited by 2
Tu, G.-H., Li, Y., Peng, C., Li, C.-Y., Raza, M. T., Tseng, H.-Y., & Lu, S. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1510.08531 · Cited by 2
Security Analysis of Malicious Socialbots on the Web
Boshmaf, Y. (2015) · cIRcle (University of British Columbia) · doi:10.14288/1.0167277 · Cited by 2
Optimal System Maneuver for Trust Management in Social Networks.Preprint
Fu, H., Li, H., Zheng, Z., Hu, P., & Mohapatra, P. (2016) · arXiv (Cornell University) · Cited by 2
Catching liars by listening carefully
Bogaard (2017) · doi:10.26481/dis.20170127gb · Cited by 2
Combating Attacks and Abuse in Large Online Communities
Wang, G. (2016) · eScholarship (California Digital Library) · Cited by 2
Tweets on a tree: Index-based clustering of tweets
Erpam, M. K. (2017) · doi:10.5900/su_fens_wp.2017.31274 · Cited by 2
Yang, L. (2021) · UPCommons institutional repository (Universitat Politècnica de Catalunya) · doi:10.5821/dissertation-2117-355671 · Cited by 2
Comparison of Various Data Mining Techniques for Fake Profile Detection on Twitter
Gupta, S., & Saurabh, S. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-80842-5_12 · Cited by 1
LIMFADD: LLM-enabled Instagram Multi-Class Fake Account Detection Dataset
Fulzele, P., Jiss, M. M., Fulzele, S., & Das, T. (2025) · IEEE International Symposium on Technology and Society (ISTAS) · doi:10.1109/istas65609.2025.11269636 · Cited by 1
Dynamic RBFN with vector attention-guided feature selection for spam detection in social media
Elakkiya, E., Saleti, S., & Balakrishnan, A. (2025) · Complex & Intelligent Systems · doi:10.1007/s40747-025-02148-5 · Cited by 1
Twitlang(er): Interactions Modeling Language (and Interpreter) for Twitter
Nicola, R. D., Maggi, A., Petrocchi, M., Spognardi, A., & Tiezzi, F. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-22969-0_23 · Cited by 1
Untrusted User Detection in Microblogs
Yu, Z., & Yu, H. (2014) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom.2014.71 · Cited by 1
Analysis of Security Messages Posted on Twitter
Santos, L. A. F., Campiolo, R., Gerosa, M. A., & Batista, D. M. (2012) · Brazilian Symposium on Collaborative Systems (SBSC) · doi:10.1109/sbsc.2012.10 · Cited by 1
A Rice Cooker wants to be my Friend on Twitter
Mowbray, M. (2011) · Cited by 1
URL ATTACKS: Classification of URLs via Analysis and Learning
Rajesh, M., Abhilash, R., & Kumar, R. (2016) · International Journal of Electrical and Computer Engineering (IJECE) · doi:10.11591/ijece.v6i3.7208 · Cited by 1
Leveraging Behavior Diversity to Detect Spammers in Online Social Networks
Cao, J., Fu, Q., Li, Q., & Guo, D. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-27137-8_25 · Cited by 1
Warning Bird Mail Alert Based MaliciousURLs Blocker System in Twitter
Lakshmi, T. S., & Parthiban, S. (2014) · International Journal of Innovative Research in Science Engineering and Technology · Cited by 1
Bang of Social Engineering in Social Networking Sites
Sharma, S. B., Sodhi, J. S., & Gulati, S. (2016) · Advances in intelligent systems and computing · doi:10.1007/978-981-10-0767-5_36 · Cited by 1
Threshold Based Mechanism to Detect Malicious URL’s in Social Networks
Divya, D. K. S. (2016) · IOSR Journal of Computer Engineering · doi:10.9790/0661-15010020218-21 · Cited by 1
Credibility Assessment of Public Pages over Facebook
Agrawal, H., & Kaushal, R. (2016) · Communications in computer and information science · doi:10.1007/978-981-10-2738-3_16 · Cited by 1
A Survey on Data Security in Social Networking Sites
Pradhan, P., Misra, S., & Koirala, T. K. (2016) · International Journal of Computer Applications · doi:10.5120/ijca2016912325 · Cited by 1
A method for recognize malignant Facebook application
Bhise, K., & Shishupal, R. S. (2016) · International Conference on Computing, Communication and Automation · doi:10.1109/ccaa.2016.7813728 · Cited by 1
Malicious Behaviour Analysis on Twitter Through the Lens of User Interest
Alghamdi, B., Xu, Y., & Watson, J. (2018) · Communications in computer and information science · doi:10.1007/978-981-13-0292-3_15 · Cited by 1
On the Reliability of Cascade Size as a Virality Measure
Elsharkawy, S., Hassan, G., Nabhan, T., & Roushdy, M. I. (2017) · European Conference on Electrical Engineering and Computer Science (EECS) · doi:10.1109/eecs.2017.53 · Cited by 1
Master of Sheets: A Tale of Compromised Cloud Documents
Onaolapo, J. O., Lazarov, M., & Stringhini, G. (2019) · IEEE European Symposium on Security and Privacy Workshops (EuroS&PW) · doi:10.1109/eurospw.2019.00053 · Cited by 1
Self-citationDetecting Twitter Compromised Accounts Using Anomalous User Behavior
Varpe, A., & Mahajan, M. (2019) · International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT) · doi:10.1109/icicict46008.2019.8993276 · Cited by 1
Behavioral Anomaly Model for Detecting Compromised Accounts on a Social Network
Fuchs, A., & Mikušová, M. (2021) · Communications in computer and information science · doi:10.1007/978-3-030-88113-9_20 · Cited by 1
Adoption of Social Media as Communication Channels in Government Agencies
Alotaibi, R. M., Ramachandran, M., Kor, A.-L., & Far, A. H. (2018) · IGI Global eBooks · doi:10.4018/978-1-5225-5201-7.ch035 · Cited by 1
Detecting Deceptive Reviews Utilizing Review Group Model
Li, Y., Wang, F., Zhang, S., & Niu, X. (2019) · EAI International Conference on Mobile Multimedia Communications (MobiMedia) · doi:10.4108/eai.29-6-2019.2282593 · Cited by 1
Recognizing User Portraits for Fraudulent Identification on Online Social Networks
Senthilkumar, S. S., Satha Sivam, S., & Brindha, K. (2022) · Advances in information security, privacy, and ethics book series · doi:10.4018/978-1-6684-3991-3.ch013 · Cited by 1
Tool to Detect Fake Accounts in Twitter
Gopal, M. K., Asha, V., Saju, B., -, R. S., & Aishwarya, R. (2023) · Advances in computer science research · doi:10.2991/978-94-6463-250-7_29 · Cited by 1
Implementing Machine Learning Approaches to Identify Fabricated Profiles
George Princess, T., Kothai, G., Heartlin Maria, H., Rajadurai, K., & Ajai, M. R. (2024) · doi:10.1109/icstem61137.2024.10560730 · Cited by 1
Uncovering the Role of Support Infrastructure in Clickbait PDF Campaigns
Stivala, G., Stefano, G. D., Mengascini, A., Graziano, M., & Pellegrino, G. (2024) · IEEE European Symposium on Security and Privacy · doi:10.1109/eurosp60621.2024.00017 · Cited by 1
Neighborhood Difference-Enhanced Graph Neural Network Based on Hypergraph for Social Bot Detection
Shi, S., Yan, L., Liu, Z., Chen, C., Chen, J., & Yan, B. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-8490-5_6 · Cited by 1
Adopting Community Features to Detect Social Spammers
Sarlati, Y., Hashemi, S., & Mozafari, N. (2015) · European Intelligence and Security Informatics Conference · doi:10.1109/eisic.2015.44 · Cited by 1
A Cross-Modal CCA-Based Astroturfing Detection Approach
Bai, X., Xiang, Y., Niu, W., Liu, J., Chen, T., Liu, J., & Wu, T. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-89500-0_50 · Cited by 1
Bernard-Jones, E., Onaolapo, J. O., & Stringhini, G. (2018) · International World Wide Web Conference (WWW) · doi:10.1145/3184558.3191529 · Cited by 1
Self-citationBuilding Reference Datasets to Support Socialbots Detection
Pacheco, C. C., Garcia, A. D. V., Machado, R., & Salles, R. M. (2018) · IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4.0&IoT) · doi:10.1109/metroi4.2018.8428331 · Cited by 1
ProfileGen: Generation of Automatic and Realistic Artificial Profiles
Vit, A. P., Cohen, D., Shabtai, A., & Puzis, R. (2018) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1109/asonam.2018.8508242 · Cited by 1
Review of Community Detection over Social Media:Graph Prospective
Jain, P., & Singh, D. (2019) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2019.0100274 · Cited by 1
Angel or Demon? Characterizing Variations Across Twitter Timeline of Technical Support Campaigners
Gupta, S., Bhatia, G., Suri, S., Kuchhal, D., Gupta, P., Ahamad, M., Gupta, M., & Kumaraguru, P. (2020) · Forschungszentrum L3S · doi:10.34962/jws-67 · Cited by 1
Elakkiya, E., & Selvakumar, S. (2019) · IEEE Region 10 Conference (TENCON) · doi:10.1109/tencon.2019.8929284 · Cited by 1
Survey on Spam Filtering Using Netspam Framework
Solanke, K., & Kulkarni, M. (2019) · International Conference on Recent Advances in Energy-efficient Computing and Communication (ICRAECC) · doi:10.1109/icraecc43874.2019.8995147 · Cited by 1
Detection of Spammers in the Reconnaissance Phase by machine learning techniques
Jeyasudha, J., & Usha, G. (2018) · International Conference on Inventive Computation Technologies (ICICT) · doi:10.1109/icict43934.2018.9034457 · Cited by 1
Chen, P., & Saman, T. N. (2020) · Human Systems Management · doi:10.3233/hsm-190825 · Cited by 1
Um Modelo Baseado em Regras para a Detecção de bots no Twitter
Leite, M. A. G. L., Guelpeli, M. V. C., & Santos, C. Q. (2020) · doi:10.5753/brasnam.2020.11161 · Cited by 1
Fake Profile Detection in Facebook
Ranjana, S., Sathian, R., & Kamalesh, M. D. (2021) · Lecture notes in electrical engineering · doi:10.1007/978-981-15-7511-2_74 · Cited by 1
Understanding online malicious behavior: social malware and email spam
Faloutsos, M., Madhyastha, H. V., & Huang, T.-K. (2013) · Cited by 1
Adoption of Social Media as Communication Channels in Government Agencies
Alotaibi, R. M., Ramachandran, M., Kor, A.-L., & Far, A. H. (2017) · IGI Global eBooks · doi:10.4018/978-1-5225-3929-2.ch007 · Cited by 1
Jain, A., Gairola, R., Jain, S., & Arora, A. (2022) · IGI Global eBooks · doi:10.4018/978-1-6684-6291-1.ch037 · Cited by 1
Exploring Event-synced Navigation Attacks across User-generated Content Platforms in the Wild
Nakano, H., Chiba, D., Koide, T., Akiyama, M., Yoshioka, K., & Matsumoto, T. (2022) · Journal of Information Processing · doi:10.2197/ipsjjip.30.372 · Cited by 1
Técnicas para Análise Dinâmica de Malware
Filho, D. S. F., Afonso, V. M., Martins, V. F., Gregio, A. R. A., de Geus, P. L., Jino, M., & Santos, R. M. (2011) · doi:10.5753/sbc.9559.1.3 · Cited by 1
Towards Secure and Privacy-Preserving Online Social Networking Services
Gong, Z. (2015) · UC Berkeley · Cited by 1
Scalable Analysis of Information Flows in Networks
Subbian, K. (2014) · University of Minnesota Digital Conservancy (University of Minnesota) · Cited by 1
New Threats to SMS-Assisted Mobile Internet Services from 4G LTE Networks.Preprint
Tu, G.-H., Li, Y., Peng, C., Li, C.-Y., Raza, M. T., Tseng, H.-Y., & Lu, S. (2015) · arXiv (Cornell University) · Cited by 1
Robards, B. (2012) · Griffith Research Online · doi:10.25904/1912/2842 · Cited by 1
EGGS: A Flexible Approach to Relational Modeling of Social Network SpamPreprint
Brophy, J., & Lowd, D. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.04909 · Cited by 1
Correlated Feature Selection for Tweet Spam ClassificationPreprint
Mishra, P. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.05495 · Cited by 1
Vit, A. P., Puzis, R., & Shabtai, A. (2017) · Springer eBooks · doi:10.1007/978-1-4614-7163-9_110212-1 · Cited by 1
Kyanko, T. J. (2017) · doi:10.33915/etd.6025 · Cited by 1
Bots on Social Media: The Past, Present and Future
Rossi, S. (2024) · doi:10.22439/phd.19.2024 · Cited by 2
Tracking versus security : investigating the two facets of browser fingerprinting
Vastel, A. (2019) · doi:10.70675/69a8128dz1778z4623z9b82zec48662623ff · Cited by 1
REPLOT: REtrieving Profile Links On Twitter for malicious campaign discovery
Pérez, C., Birregah, B., Layton, R., Lemercier, M., & Watters, P. (2015) · AI Communications · doi:10.3233/aic-150659
On the threats to Cloud-based online service users (and what we can do about them)
Stringhini, G. (2015) · International Conference on High Performance Computing & Simulation (HPCS) · doi:10.1109/hpcsim.2015.7237026
Self-citationInvestigating the effects of virtual social networks on entrepreneurial marketing
Talebi, K. H. S., Behrad, M., & Ekhlassi, A. (2014) · Management Science Letters · doi:10.5267/j.msl.2014.9.007
Improved URL Shortening With Traffic Analysis and Spam Filter
Shriram, J. (2015)
Research on Characteristics Analysis and Classification of Microblog Users
Jiang, X., & Li, W. (2015) · Advances in computer science research · doi:10.2991/icmmita-15.2015.142
Hiding in Plain Sight: The Anatomy of Malicious Facebook PagesPreprint
Dewan, P., & Kumaraguru, P. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1510.05828
Real-Time Detection System for Suspicious URLs
Chouty, K. P., Thogiti, A. C., & Vudatha, K. S. (2015) · OPUS - Open Portal to University Scholarship (Governors State University)
Spying on Spammers: Tracking Like Farm Accounts on Facebook.Preprint
Ikram, M., Onwuzurike, L., Cristofaro, E. D., Friedman, A., Jourjon, G., Kâafar, M. A., & Shafiq, M. (2015) · arXiv (Cornell University)
Review of Social Networking Sites’ Security and Privacy
Yang, S. (2015) · Työväentutkimus Vuosikirja
Notawale, S. R., & Khan, J. A. (2016) · International Journal of Modern Trends in Engineering and Research
Survey on Suspicious URL Detection Schemes in Twitter Stream
G, A. P., & M S, S. (2014)
Data science and spatio-temporal resolved social networks
Mocanu, D. (2014) · Digital Repository Service (Northeastern University) · doi:10.17760/d20005098
Combating Fraud in Online Social Networks: Characterizing and Detecting Facebook Like FarmsPreprint
Ikram, M., Onwuzurike, L., Farooqi, S., Cristofaro, E. D., Friedman, A., Jourjon, G., Kaafar, M. A., & Shafiq, M. (2015) · arXiv (Cornell University)
Harkreader, R. C. (2012) · OakTrust (Texas A&M University Libraries)
VoteTrust: A System to Defend against Social Network Sybils in Facebook
Priyanka, P., & Deepthi, K. (2016) · International Journal of Computer Applications · doi:10.5120/ijca2016909854
Behind Box-Office Sales: Understanding the Mechanics of Automation Spam in Classifieds
Kaizer, A. J., Gupta, M., Sajib, M., Acharjee, A., & Ismail, Q. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-30505-9_19
Metadata
- Authors
- Stringhini, G., Kruegel, C., & Vigna, G.
- Affiliations
- University of California, Santa Barbara (US)
- Venue
- Proceedings of the 26th Annual Computer Security Applications Conference
- Publisher
- ACM
- Published
- 2010-12-06
- Pages
- 1-9
- ISSN
- 1063-9527 (ISSN-L)
- OpenAlex
- W1986678144
- Cited by
- 791
- References
- 7
- Citations by year
- 2026: 6 · 2025: 13 · 2024: 27 · 2023: 24 · 2022: 39 · 2021: 49 · 2020: 57 · 2019: 81 · 2018: 80 · 2017: 85 · 2016: 93 · 2015: 84 · 2014: 63 · 2013: 40 · 2012: 35
- FWCI
- 20.43
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Annual Computer Security Applications Conference (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Funders
- Office of Naval Research (N000140911042)
- Division of Computer and Network Systems (CNS-0845559CNS-0905537)
- 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
- spam detection, social networks, spammers, cybercrime, trust relationships, malicious websites, identity theft, personal data
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 ›
Are you an author, or do you see a problem with this page? Request a review
