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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The feature sets from [8, 2] are used to train and test machine learning classifiers in order to distinguish between humans and spammers.
“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]
Works [8, 2] propose a list of features to be extracted from manually classified datasets of accounts.
“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]
With both the [2] and [8] feature sets, BN, kNN, and LR classifiers achieve overall worse performances than the other classifiers.
“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]
The content of spambots' tweets exhibits the so-called message similarity.
“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]
The study presented in [8] focuses on spambot detection.
“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]
The classifiers built over the feature set of Yang et al. [2] achieve slightly better results than those built over the feature set of Stringhini et al. [8].
“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]
Spambots do not have thousands of friends.
“1. spambots do not have thousands of friends;”
From Fame for sale: Efficient detection of fake Twitter followers (2015) · cites this paper as [8]
All classifiers based on the feature set of [2] have a higher AUC than the corresponding classifiers built with the feature set of [8].
“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]
Classifiers based on the feature set of [8] also obtain high detection levels: for RF, D, and J48, accuracy, precision, and recall are around 0.98, with MCC around 0.96.
“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]
The lower precision and recall of the [8] feature set compared to the [2] feature set show that the features of Stringhini et al. [8] tend to consider some human accounts as fake followers.
“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]
Spambots have a high friends/(followers^2) ratio value (i.e., lower ratio values mean legitimate users).
“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]
The authors of [8] exploit several characteristics gathered by crawling an account's details, both from its profile and timeline, and use them in a Random Forest algorithm that outputs whether the account is a spambot or not.
“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]
Even though the proposed features were originally designed for spam detection, this paper is the first to consider them to spot fake followers on Twitter.
“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]
Spambots have a high tweets containing URLs ratio (URL ratio).
“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]
Spambots have sent less than 20 tweets.
“2. spambots have sent less than 20 tweets;”
From Fame for sale: Efficient detection of fake Twitter followers (2015) · cites this paper as [8]
The Class C classifiers, built over all the features considered in the 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.
“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]
After pruning, the Yang et al. [2] classifier is able to miss fewer fakes than the Stringhini et al. [8] classifier.
“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]
The decision tree obtained with the feature set of Stringhini et al. [8] has 22 leaves, 43 nodes, and a height of 7, with the friends/(followers^2) ratio at the root.
“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]
There are differences between the fake followers in the BAS dataset and the spam accounts characterized in [8] and [2].
“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]
The pruned version of Stringhini et al. [8] reduces its recall by 0.017, while the pruned version of Yang et al. [2] drops recall by only 0.004.
“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]
Even reducing the features, it is possible to have a detection rate higher than 0.95, as shown for Stringhini et al. [8] and Yang et al. [2] in the last lines of Table 13.
“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]
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.
“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]
Unlike prior honeypot studies, the paper leverages timeline-based features for detection of fake accounts.
“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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- 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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