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.
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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]
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Phishing URL Detection Using URL Ranking
Feroz, M. N., & Mengel, S. A. (2015) · IEEE International Congress on Big Data (BigData Congress) · doi:10.1109/bigdatacongress.2015.97 · Cited by 89
Towards detecting fake user accounts in facebook
Gupta, A., & Kaushal, R. (2017) · ISEA Asia Security and Privacy (ISEASP) · doi:10.1109/iseasp.2017.7976996 · Cited by 88
Detecting Spam URLs in Social Media via Behavioral Analysis
Cao, C., & Caverlee, J. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-16354-3_77 · Cited by 89
Profile characteristics of fake Twitter accounts
Gurajala, S., White, J. S., Hudson, B., Voter, B. R., & Matthews, J. N. (2016) · Big Data & Society · doi:10.1177/2053951716674236 · Cited by 86
DeepSBD: A Deep Neural Network Model With Attention Mechanism for SocialBot Detection
Fazil, M., Sah, A. K., & Abulaish, M. (2021) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2021.3102498 · Cited by 91
Paying for Likes?: Understanding Facebook Like Fraud Using Honeypots
De Cristofaro, E., Friedman, A., Jourjon, G., Kaafar, M. A., & Shafiq, M. Z. (2014) · Proceedings of the 2014 Conference on Internet Measurement Conference · doi:10.1145/2663716.2663729 · Cited by 88
“s from these studies as they all conduct passive measurements, whereas we rely on the deployment of several honeypot pages and (paid) campaigns to actively engage with fake profiles. Stringhini et al. [23] and Lee et al. [17] created honeypot profiles in Facebook, MySpace, and Twitter to detect spammers. Their work diff…”
MethodLeveraging Crowdsourcing for Efficient Malicious Users Detection in Large-Scale Social Networks
Yang, G., He, S., & Shi, Z. (2016) · IEEE Internet of Things Journal · doi:10.1109/jiot.2016.2560518 · Cited by 85
Graph-Based Fraud Detection in the Face of Camouflage
Hooi, B., Shin, K., Song, H. A., Beutel, A., Shah, N., & Faloutsos, C. (2017) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3056563 · Cited by 84
Improving spam detection in Online Social Networks
Gupta, A., & Kaushal, R. (2015) · International Conference on Cognitive Computing and Information Processing · doi:10.1109/ccip.2015.7100738 · Cited by 86
The Good, the Bad and the Bait: Detecting and Characterizing Clickbait on YouTube
Zannettou, S., Chatzis, S., Papadamou, K., & Sirivianos, M. (2018) · IEEE Security and Privacy Workshops (SPW) · doi:10.1109/spw.2018.00018 · Cited by 91
Algarni, A., Xu, Y., & Chan, T. (2017) · European Journal of Information Systems · doi:10.1057/s41303-017-0057-y · Cited by 94
An MCL-Based Approach for Spam Profile Detection in Online Social Networks
Ahmed, F., & Abulaish, M. (2012) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom.2012.83 · Cited by 83
Chatzakou, D., Kourtellis, N., Blackburn, J., Cristofaro, E. D., Stringhini, G., & Vakali, A. I. (2017) · International World Wide Web Conference (WWW) · doi:10.1145/3041021.3053890 · Cited by 80
Self-citationClassification of various attacks and their defence mechanism in online social networks: a survey
Sahoo, S. R., & Gupta, B. B. (2019) · Enterprise Information Systems · doi:10.1080/17517575.2019.1605542 · Cited by 80
Identifying Fake Profiles in LinkedInPreprint
Adikari, S., & Dutta, K. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.01381 · Cited by 80
Key Challenges in Defending Against Malicious Socialbots
Boshmaf, Y., Muslukhov, I., Beznosov, K., & Ripeanu, M. (2012) · doi:10.5281/zenodo.3264613 · Cited by 76
Kaur, R., Singh, S., & Kumar, H. (2018) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2018.03.015 · Cited by 80
Detecting Fake Accounts in Online Social Networks at the Time of Registrations
Yuan, D., Miao, Y., Gong, N. Z., Yang, Z., Li, Q. L., Song, D. X., Wang, Q., & Liang, X. (2019) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3319535.3363198 · Cited by 75
Early filtering of ephemeral malicious accounts on Twitter
Lee, S., & Kim, J. K. (2014) · Computer Communications · doi:10.1016/j.comcom.2014.08.006 · Cited by 76
Towards Robustness Against Natural Language Word SubstitutionsPreprint
Dong, X., Luu, A. T., Ji, R., & Liu, H. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.13541 · Cited by 85
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 ›
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