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]
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.
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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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