Hassan et al, ICWSM 2009
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.
The authors approach 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.
TREC BLOG06 dataset has 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 apply the diffusion model and count 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. experimented by splitting the data i
In addition to detecting these changes, the authors also propose a tf-idf based scoring method to represent the breakpoints. They find the keywords by looking at the tfidf of the words while making sure that a word from the current time do not increase the prominence of the same word from an older time period.
The authors report the analysis of Tiger Wood's car accident topic in 2009. They found several possible breaks within the tweets and some of them are related to the events from reported news. They were also able to produce prominent words that describes the breakpoint.
Related to the paper, the authors produce a news tracking applicationon Twitter where a user can click on a period to see the events of the period with related prominent words.
A related work Ku et al, AAAI 2006 also focused on identifiying temporal changes in opinion by using language characteristics of Chinese.
