Detecting Spammers and Content Promoters in Online Video Social Networks

Benevenuto, Fabrí­cio; Rodrigues, Tiago; Almeida, Jussara M.; Gonçalves, Mariana; Almeida, Virgílio A. F.

doi:10.1109/infcomw.2009.5072127

Cited by 87 publications

(89 citation statements)

References 4 publications

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“…The most straightforward way to aggregate the ratings collected by a given object is to compute their arithmetical mean (this method is referred to as mean below). However, such direct averaging is rather sensitive to noisy information (provided by the users who significantly differ from the prevailing opinion about the object) and manipulation (ratings intended to obtain the resulting ranking of objects) [145,146]. To enhance the robustness of results, one usually introduces a reputation system where reputation of users is determined along with the ranking of objects.…”

Section: H Rating-based Ranking Algorithms On Bipartite Networkmentioning

confidence: 99%

Ranking in evolving complex networks

et al. 2017

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Complex networks have emerged as a simple yet powerful framework to represent and analyze a wide range of complex systems. The problem of ranking the nodes and the edges in complex networks is critical for a broad range of real-world problems because it affects how we access online information and products, how success and talent are evaluated in human activities, and how scarce resources are allocated by companies and policymakers, among others. This calls for a deep understanding of how existing ranking algorithms perform, and which are their possible biases that may impair their effectiveness. Well-established ranking algorithms (such as the popular Google's PageRank) are static in nature and, as a consequence, they exhibit important shortcomings when applied to real networks that rapidly evolve in time. The recent advances in the understanding and modeling of evolving networks have enabled the development of a wide and diverse range of ranking algorithms that take the temporal dimension into account. The aim of this review is to survey the existing ranking algorithms, both static and time-aware, and their applications to evolving networks. We emphasize both the impact of network evolution on well-established static algorithms and the benefits from including the temporal dimension for tasks such as prediction of real network traffic, prediction of future links, and identification of highly-significant nodes.Comment: 54 pages, 16 figure

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Section: H Rating-based Ranking Algorithms On Bipartite Networkmentioning

confidence: 99%

Ranking in evolving complex networks

et al. 2017

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“…We report the results of our active-learning based one-class classifier with different feature combinations. 3 For setting 1 (leaveone-out), we report the performance w.r.t the accuracy (fraction of known spammers identified by the method) and observe that our method performs significantly well with only HMPS feature -it achieves an accuracy of 0.77, outperforming all baseline methods. However, incorporating OSN2 features along with HMPS further enhances 9.1% performance of our classifier, achieving an accuracy of 0.84.…”

Section: Methodsmentioning

confidence: 99%

Collective Classification of Spam Campaigners on Twitter

Gupta

Khattar

Gogia

et al. 2018

Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18

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Cybercriminals have leveraged the popularity of a large user base available on Online Social Networks (OSNs) to spread spam campaigns by propagating phishing URLs, attaching malicious contents, etc. However, another kind of spam attacks using phone numbers has recently become prevalent on OSNs, where spammers advertise phone numbers to attract users' attention and convince them to make a call to these phone numbers. The dynamics of phone number based spam is different from URL-based spam due to an inherent trust associated with a phone number. While previous work has proposed strategies to mitigate URL-based spam attacks, phone number based spam attacks have received less attention.In this paper, we aim to detect spammers that use phone numbers to promote campaigns on Twitter. To this end, we collected information (tweets, user meta-data, etc.) about 3, 370 campaigns spread by 670, 251 users. We model the Twitter dataset as a heterogeneous network by leveraging various interconnections between different types of nodes present in the dataset. In particular, we make the following contributions -(i) We propose a simple yet effective metric, called Hierarchical Meta-Path Score (HMPS) to measure the proximity of an unknown user to the other known pool of spammers. (ii) We design a feedback-based active learning strategy and show that it significantly outperforms three state-of-the-art baselines for the task of spam detection. Our method achieves 6.9% and 67.3% higher F1-score and AUC, respectively compared to the best baseline method. (iii) To overcome the problem of less training instances for supervised learning, we show that our proposed feedback strategy achieves 25.6% and 46% higher F1-score and AUC respectively than other oversampling strategies. Finally, we perform a case study to show how our method is capable of detecting those users as spammers who have not been suspended by Twitter (and other baselines) yet. CCS CONCEPTS• Information systems; • Security and privacy; • Applied computing; This paper is published under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. Authors reserve their rights to disseminate the work on their personal and corporate Web sites with the appropriate attribution.

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“…Spam is a common problem in online media, and can be found in the form of emails (Cormack, 2008), websites (Spirin & Han, 2012), videos (Benevenuto, Rodrigues, Almeida, Almeida, & Gonçalves, 2009), microblogging (Benevenuto, Magno, Rodrigues, & Almeida, 2010), comments (Mishne, Carmel, & Lempel, 2005), and reviews (Jindal & Liu, 2007) to name a few. However, spammers use platform-specific techniques to elude spam detection systems.…”

Section: Related Workmentioning

confidence: 99%

An analysis of 14 Million tweets on hashtag‐oriented spamming*

Sedhai

Sun

2017

Asso for Info Science & Tech

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Over the years, Twitter has become a popular platform for information dissemination and information gathering. However, the popularity of Twitter has attracted not only legitimate users but also spammers who exploit social graphs, popular keywords, and hashtags for malicious purposes. In this paper, we present a detailed analysis of the HSpam14 dataset, which contains 14 million tweets with spam and ham (i.e., nonspam) labels, to understand spamming activities on Twitter. The primary focus of this paper is to analyze various aspects of spam on Twitter based on hashtags, tweet contents, and user profiles, which are useful for both tweet‐level and user‐level spam detection. First, we compare the usage of hashtags in spam and ham tweets based on frequency, position, orthography, and co‐occurrence. Second, for content‐based analysis, we analyze the variations in word usage, metadata, and near‐duplicate tweets. Third, for user‐based analysis, we investigate user profile information. In our study, we validate that spammers use popular hashtags to promote their tweets. We also observe differences in the usage of words in spam and ham tweets. Spam tweets are more likely to be emphasized using exclamation points and capitalized words. Furthermore, we observe that spammers use multiple accounts to post near‐duplicate tweets to promote their services and products. Unlike spammers, legitimate users are likely to provide more information such as their locations and personal descriptions in their profiles. In summary, this study presents a comprehensive analysis of hashtags, tweet contents, and user profiles in Twitter spamming.

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Detecting Spammers and Content Promoters in Online Video Social Networks

Cited by 87 publications

References 4 publications

Ranking in evolving complex networks

Ranking in evolving complex networks

Collective Classification of Spam Campaigners on Twitter

An analysis of 14 Million tweets on hashtag‐oriented spamming*

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