Combating spam in tagging systems

Koutrika, Georgia; Effendi, Frans Adjie; Gyöngyi, Zolt ́n; Heymann, Paul; García-Molina, Héctor

doi:10.1145/1409220.1409225

Cited by 56 publications

(39 citation statements)

References 13 publications

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“…Koutrika et al [60] assigned relevance numbers to web resources based on the number of common tags they share. This is a language-independent method.…”

Section: Protection Of Folksonomy Structurementioning

confidence: 99%

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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“…Koutrika et al [60] assigned relevance numbers to web resources based on the number of common tags they share. This is a language-independent method.…”

Section: Protection Of Folksonomy Structurementioning

confidence: 99%

Semantics discovery in social tagging systems: A review

Jabeen

Khusro

Majid

et al. 2014

Multimed Tools Appl

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“…Second, we filter out the tags and edges with frequency lower than certain threshold, since these unfrequent features do not have statistical meaning. In this experiment, we exclude the tags which were annotated by fewer than ten users, as these unpopular tags have little valuable information or noises [15]. After this pre-processing, more than ten thousand tags are left per month on average, and cover over 70% tagging actions.…”

Section: Dataset Preprocessingmentioning

confidence: 99%

Bursty event detection from collaborative tags

et al. 2011

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Collaborative tagging have emerged as a ubiquitous way to annotate and organize online resources. As a kind of descriptive keyword, large amount of tags are created and associated to multiple types of resources, e.g., web pages, photos, videos and tweets. Users' tagging actions over time reflect their changing interests. Monitoring and analyzing the temporal patterns of tags can provide important insights to trace hot topics on the web. Existing work focuses on deriving temporal patterns for individual tags. However, there exist remarkable correlations among tags assigned to online resources. In this paper, we propose a new approach to detect bursty tagging event, which captures the relations among a group of correlated tags where the tags are either bursty or associated with bursty tag co-occurrence. This kind of bursty tagging event generally corresponds to a real life event. It profiles the events with more representative and comprehensible clues. The proposed approach is divided into three stages. We exploit the sliding time intervals to extract bursty features as the first step, and then adopt graph clustering techniques to group bursty features into meaningful bursty events. We discuss the choice of similarity and granularity for event detection. After that, we further utilize an automatically generated tag taxonomy to organize bursty events to facilitate the burst oriented 172 World Wide Web (2012) 15:171-195 navigation and analysis. The experimental study on a large real data set demonstrates the superiority of our new approach.

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“…This research indicates that some commonly used algorithms were vulnerable to attacks created by generating false clickstreams designed to promote particular target pages. Recently, Koutrika et al (2007) presented an analysis of tagging systems which found that these systems are also prone to manipulation by malicious agents.…”

Section: Related Workmentioning

confidence: 99%

Collaborative web search: a robustness analysis

O’Mahony

Smyth

2007

Artif Intell Rev

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Collaborative web search utilises past search histories in a community of like-minded users to improve the quality of search results. Search results that have been selected by community members for past queries are promoted in response to similar queries that occur in the future. The I-SPY system is one example of such a collaborative approach to search. As is the case with all open systems, however, it is difficult to establish the integrity of those who access a system and thus the potential for malicious attack exists. In this paper we investigate the robustness of the I-SPY system to attack. In particular, we consider attack scenarios whereby malicious agents seek to promote particular result pages within a community. In addition, we analyse robustness in the context of community homogeneity, and we show that this key characteristic of communities has implications for system robustness.Keywords Collaborative web search · Personalisation · Malicious attack · Robustness IntroductionTraditional search engines operate in a "one-size-fits-all" manner and return the same search results for the same query, irrespective of the interests and needs of those individuals using the service. The rationale for collaborative web search (CWS) lies in the query repetition and selection regularity that exists in the searches of communities of like-minded users. The I-SPY system described by Smyth et al. (2004) is one example of such a collaborative approach to search. I-SPY is a meta-search framework where submitted queries are first passed to base-level search engines (e.g. Google, Yahoo! etc.). The result lists obtained are

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Combating spam in tagging systems

Cited by 56 publications

References 13 publications

Semantics discovery in social tagging systems: A review

Semantics discovery in social tagging systems: A review

Bursty event detection from collaborative tags

Collaborative web search: a robustness analysis

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