Difference between revisions of "Hassan et al, ICWSM 2009"
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== Summary == | == Summary == | ||
| − | The aim of this [[Category::paper]] is to find the important and influential blogs with recurring interest in a specific topic. Given a set of blogs related to a particular topic, the authors are trying to find a subset of blogs that represents the larger set. | + | The aim of this [[Category::paper]] is to find the important and influential blogs with recurring interest in a specific topic. Given a set of blogs related to a particular topic, the authors are trying to find a subset of blogs that represents the larger set by using a stochastic graph based method. |
| − | The authors | + | The authors approached to this [[AddressesProblem::blog retrieval]] problem with the assumption that important and representative blogs tend to be lexically similar to other important and representative blogs. Therefore they used textual similarity between posts as a way to understand which blog is affecting the others and so to determine the authorities. |
| − | The authors | + | The authors used a [[UsesMethod::PageRank]] like algorithm, called BlogRank, to rank the blogs by their popularity. In their algorithm they represented each blog with a node and put an edge between two nodes if they are lexically similar. Iterations over this graph calculates the importance score of a blog by using the scores of its neighbors. |
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| + | [[File:BlogRank.jpg]] | ||
| − | + | [[UsesMethod::Cosine similarity]] between tf-idf vector representations of posts are used the calculate the text similarity between posts. The authors also used blog related attributes such as number of posts, average length of posts etc. as priors. BlogRank algorithm takes diversity into account and penalize blogs that are quite similar to already selected blogs. | |
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| + | [[UsesDataset::TREC BLOG06]] and [[UsesDataset::UCLA Blogocenter]] datasets had been used in the experiments. They used [[UsesMethod::diffusion models]] to measure the performance of their algorithm. Initially they marked the selected nodes as active and then applied the diffusion model and counted the number of activated nodes at the end. | ||
| − | + | The authors tried several other algorithms to compare with their ranking algorithm. The experiments showed that BlogRank outperforms other methods both in coverage and in running time. They also performed experiments in order to see whether BlogRank algorithm can be used in predicting. The results indicated that BlogRank method generalizes well for the future. | |
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| − | + | This work is similar to the Blog Distillation task in the TREC Blog Track. However in blog distillation task, given a query the aim is to return all relevant blogs. In this paper, given set of blogs related to topic, the aim is to select smaller set of blogs. Some related works are [[RelatedPaper::Arguello et al, ICWSM 2008]] and [[RelatedPaper::Elsas et al, TREC 2007]]. | |
Latest revision as of 02:59, 31 March 2011
Citation
Ahmed Hassan, Dragomir R. Radev, Junghoo Cho, Amruta Joshi. 2009. Content Based Recommendation and Summarization in the Blogosphere. The International Conference on Weblogs and Social Media (ICWSM 2009).
Online version
Summary
The aim of this paper is to find the important and influential blogs with recurring interest in a specific topic. Given a set of blogs related to a particular topic, the authors are trying to find a subset of blogs that represents the larger set by using a stochastic graph based method.
The authors approached to this blog retrieval problem with the assumption that important and representative blogs tend to be lexically similar to other important and representative blogs. Therefore they used textual similarity between posts as a way to understand which blog is affecting the others and so to determine the authorities.
The authors used a PageRank like algorithm, called BlogRank, to rank the blogs by their popularity. In their algorithm they represented each blog with a node and put an edge between two nodes if they are lexically similar. Iterations over this graph calculates the importance score of a blog by using the scores of its neighbors.
Cosine similarity between tf-idf vector representations of posts are used the calculate the text similarity between posts. The authors also used blog related attributes such as number of posts, average length of posts etc. as priors. BlogRank algorithm takes diversity into account and penalize blogs that are quite similar to already selected blogs.
TREC BLOG06 and UCLA Blogocenter datasets had been used in the experiments. They used diffusion models to measure the performance of their algorithm. Initially they marked the selected nodes as active and then applied the diffusion model and counted the number of activated nodes at the end.
The authors tried several other algorithms to compare with their ranking algorithm. The experiments showed that BlogRank outperforms other methods both in coverage and in running time. They also performed experiments in order to see whether BlogRank algorithm can be used in predicting. The results indicated that BlogRank method generalizes well for the future.
This work is similar to the Blog Distillation task in the TREC Blog Track. However in blog distillation task, given a query the aim is to return all relevant blogs. In this paper, given set of blogs related to topic, the aim is to select smaller set of blogs. Some related works are Arguello et al, ICWSM 2008 and Elsas et al, TREC 2007.
