Clustering Blogs with Collective Wisdom

Agarwal, Nitin; Galan, Magdiel; Liu, H.; Subramanya, Shankara B.

doi:10.1109/icwe.2008.9

Cited by 19 publications

(11 citation statements)

References 2 publications

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“…Hence we set threshold = 5 for rest of the experiments. More results and analysis regarding the setting of the threshold value with respect to the transition point are reported in [1] and will be evaluated in the future with a larger dataset.…”

Section: Link Strengthmentioning

confidence: 98%

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WisColl: Collective wisdom based blog clustering

Agarwal

Galan

Liu

et al. 2010

Information Sciences

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Section: Link Strengthmentioning

confidence: 98%

“…By August 2008, the amount of weblogs tracked had increased by another 7.7 million, as reported by Technorati. 1 With such a significant growth of the 2. Related work…”

Section: Introductionmentioning

confidence: 98%

WisColl: Collective wisdom based blog clustering

Agarwal

Galan

Liu

et al. 2010

Information Sciences

Self Cite

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“…In other words, blog clustering can be exploited. By first clustering blogs based on categories [1] and then extracting the similar bloggers only from the same cluster it would be possible for the SocialTagger algorithm to handle a very large number of blogs. We believe this is a promising direction for further study.…”

Section: Discussionmentioning

confidence: 99%

“…In Collaborative(social) Tagging, the users of the content create and share tags to annotate the content which provide valuable indices for navigation and discovery. An example of such a collaborative system on the web which has become widely popular is Del.icio.us or Delicious 1 . Delicious allows users to collaboratively tag webpages, blogs and other resources on the web.…”

Section: Introductionmentioning

confidence: 99%

Socialtagger - collaborative tagging for blogs in the long tail

Subramanya

Liu

2008

Proceedings of the 2008 ACM Workshop on Search in Social Media

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Social bookmarking is the process through which users share tags for online resources like blogs with others. Such collaborative tags provide valuable metadata for retrieval systems. While the successes of collaborative tagging systems have been demonstrated by popular websites like Del.icio.us, these sites cover only a small fraction of the available blogs on the web. The vast majority of the blogs are not available on any collaborative tagging system and are often tagged only by the authors. This lack of coverage of collaborative tags is a considerable roadblock in using the tag metadata in a web scale information retrieval system. To solve this problem we propose and implement a system to automatically recommend collaborative tags for a blog. The automatically generated tags will help to surface the blogs by making them available on social book marking sites and allow them to be easily discovered and potentially further tagged by a wider population.

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“…For instance, in [25] the authors build a word-page matrix by downloading weblog pages and apply the k-means clustering algorithm with different weights assigned to the title, body, and comment parts. In [1], the authors use weblog categories to build a category relation graph in order to join different weblog classes; they use edges in the category relation graph to represent similarity between different categories and they represent nodes as categories.…”

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confidence: 99%

Prototype/topic based clustering method for weblogs

Perez-Tellez

Cardiff

Rosso

et al. 2016

IDA

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Abstract. In the last 10 years, the information generated on weblog sites has increased exponentially, resulting in a clear need for intelligent approaches to analyse and organise this massive amount of information. In this work, we present a methodology to cluster weblog posts according to the topics discussed therein, which we derive by text analysis. We have called the methodology Prototype/Topic Based Clustering, an approach which is based on a generative probabilistic model in conjunction with a Self-Term Expansion methodology. The usage of the Self-Term Expansion methodology is to improve the representation of the data and the generative probabilistic model is employed to identify relevant topics discussed in the weblogs. We have modified the generative probabilistic model in order to exploit predefined initialisations of the model and have performed our experiments in narrow and wide domain subsets. The results of our approach have demonstrated a considerable improvement over the pre-defined baseline and alternative state of the art approaches, achieving an improvement of up to 20% in many cases. The experiments were performed on both narrow and wide domain datasets, with the latter showing better improvement. However in both cases, our results outperformed the baseline and state of the art algorithms.

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Clustering Blogs with Collective Wisdom

Cited by 19 publications

References 2 publications

WisColl: Collective wisdom based blog clustering

WisColl: Collective wisdom based blog clustering

Socialtagger - collaborative tagging for blogs in the long tail

Prototype/topic based clustering method for weblogs

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