Spam detection in social bookmarking websites

Poorgholami, Maryam; Jalali, Mehrdad; Rahati, Saeed; Asgari, Taha

doi:10.1109/icsess.2013.6615254

Cited by 7 publications

(3 citation statements)

References 5 publications

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“…Zhai et al (2016) have developed a spam detection mechanism based on the userbehavior, that employs criteria such as the neighbors' honesty within the group, which the user is member of. A similar filtering approach by Poorgholami et al (2013) mixes features of tags and users together in the spam classification, that include the spamicity for tags from Baysian classification along with some user features derived from the social connectivity.…”

Section: Related Workmentioning

confidence: 99%

“…To reduce processing time, for every run we selected randomly subsets of folksonomies corresponding to 3.000 users. As opposed to other researchers who used pre-labeled datasets with spam data (e.g., Yazdani et al (2012); Poorgholami et al (2013); Zhai et al (2016)), in our case, due to the absence of such bogus data we chose to evaluate over synthetic ones, we generated out of the original. Moreover, this enabled us to study exclusive cases of attacks.…”

Section: Datasetmentioning

confidence: 99%

See 1 more Smart Citation

Securing Tag-based recommender systems against profile injection attacks: A comparative study. (Extended Report)

Pitsilis,

Ramampiaro,

Langseth

2019

Preprint

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This work addresses the challenges related to attacks on collaborative tagging systems, which often comes in a form of malicious annotations or profile injection attacks. In particular, we study various countermeasures against two types of such attacks for social tagging systems, the Overload attack and the Piggyback attack. The countermeasure schemes studied here include baseline classifiers such as, Naive Bayes filter and Support Vector Machine, as well as a Deep Learning approach. Our evaluation performed over synthetic spam data generated from del.icio.us dataset, shows that in most cases, Deep Learning can outperform the classical solutions, providing high-level protection against threats.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Datasetmentioning

confidence: 99%

Securing Tag-based recommender systems against profile injection attacks: A comparative study. (Extended Report)

Pitsilis,

Ramampiaro,

Langseth

2019

Preprint

View full text Add to dashboard Cite

show abstract

“…This would result in more precise expert findings and is more resilient to spammers. Keeping this in mind, Poorgholami et al (2013) outlined set of features by considering the tags, resources, users and relationship among them. The authors claimed that these features are used by many machine learning algorithms to detect spammers with 99% of accuracy.…”

Section: Detection and Removal Of Spam Usersmentioning

confidence: 99%

Quality-protected folksonomy maintenance approaches: a brief survey

Jabeen

Khusro

2015

The Knowledge Engineering Review

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Folksonomy gives liberty to its users to freely assign chosen keywords as tags, and this is the main reason behind its popularity. Apart from freedom, this system also reflects the collective intelligence of the crowd. However, this freedom and liberty can degrade quality of the folksonomy. It is required that quality of the folksonomy must remain consistently excellent and does not degrade with the passage of time. This is a survey paper, in which we present a brief survey of the research efforts intended to maintain a quality-protected folksonomy. We have organized our paper by looking at the problem from four aspects namely selection of quality tags, tag management features provided by folksonomy applications, folksonomy cleaning and interoperability of tags across platforms. We conclude our review with some of the interesting research topics, which need to be explored further. Our conclusion will be relevant and beneficial for engineers and designers who aim to design and maintain a quality-protected folksonomy.

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Semantics discovery in social tagging systems: A review

Jabeen

Khusro

Majid

et al. 2014

Multimed Tools Appl

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Web 2.0 has brought many collaborative and novel applications which transformed the web as a medium and resulted in its exponential growth. Tagging systems are one of these killer applications. Tags are in free-form but represent the link between objective information and users' cognitive information. However, tags have ambiguity problem reducing precision. Hence search and retrieval pose a challenge on folksonomy systems which have flat, unstructured, non-hierarchical organization with unsupervised vocabulary. We present a brief survey of different approaches for adding semantics in folksonomies thus bringing structure and precision in search and navigation. We did comparative analysis to estimate the significance of each source of semantics. Then, we have categorized the approaches in a systematic way and summarized the feature set support. Based on the survey we end up with recommendations. Our survey and conclusion will prove to be relevant and beneficial for engineers and designers aiming to design and maintain well structured folksonomy with precise search and navigation results.

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Spam detection in social bookmarking websites

Cited by 7 publications

References 5 publications

Securing Tag-based recommender systems against profile injection attacks: A comparative study. (Extended Report)

Securing Tag-based recommender systems against profile injection attacks: A comparative study. (Extended Report)

Quality-protected folksonomy maintenance approaches: a brief survey

Semantics discovery in social tagging systems: A review

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