Hassan et al, ICWSM 2009

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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

ICWSM09

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 uses a Pagerank like algorithm to rank the blogs by their popularity. In their algorithm they represent each blog with a node and put an edge between two nodes if they are lexically similar. Iterations over this graph will calculate the importance score of a blog by using the scores of its neighbors.

After some observations, the authors claim that the emotion pattern and word pattern of tweets change as a result of a change in public opinion. With this aim in mind, authors developed an Emotion Corpus (Upinion) to detect emotions in tweets.

Two methods are used to detect opinions

  • Vector Space Model : A binary vector has been created for each tweet. Each class of the Emotion Corpus is represented as a dimension in the vector and the value of each dimension is determined by the existence of any emotion word from the related class in the tweet. Centroid of vectors are calculated to represent an interval. Cosine similarity is applied to centroid vectors to find the opinion similarity between two intervals.
  • Set Space Model : Each time interval is represented by a single document which is the union of tweets posted in that particular time interval. Jaccard similarity is used to find the similarity between two intervals.

The authors combine these two methods to detect a change and report a breakpoint.

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.