Domain-Specific Identification of Topics and Trends in the Blogosphere

Schirru, Rafael; Obradović, Darko; Baumann, S.; Wortmann, Peter

doi:10.1007/978-3-642-14400-4_38

Cited by 7 publications

(2 citation statements)

References 7 publications

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“…The study [30] showed that bloggers quickly posted information related to the London bombings news story as it happened, and Takeshi [25] also showed that bloggers can quickly response the earthquake information on their micro-blog and those bloggers can be regarded as sensors used to detect real-time events. Schirru [28] proposed a weighted term matrix approach by applying textual data mining techniques to monitor the trend of specific domain in the blogosphere, and used the Social Media Miner interface for showing the publication trend. Leskovec [14] developed a meme-tracking framework for tracking short, distinctive phrase which appears in the news media and blogosphere.…”

Section: A Researches Within the Blogosphere And News Storymentioning

confidence: 99%

The Retrieval of Important News Stories by Influence Propagation among Communities and Categories

Lin¹,

Kao²

2012

2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology

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Nowadays, people receive information of the news stories not only from newspapers but also from online news websites. They search important news stories in order to know what happen today. However, it is hard to browse all the news stories published on a day. It is necessary to identify which news stories are more newsworthy on the specific day. In this paper, we investigate how to automatically identify the importance of news stories for different news categories on a specific day by utilizing the influence propagation among communities and news categories. In particular, we build an influence propagation model which consists of three features: category relevance, bloggers' attention and bursty influence. Based on this influence propagation model, we propose a Cross-Category Social Influence Propagation (C-SIP) approach for scoring the importance of news stories on a specific day. We evaluate our approach by using the judgment of Story Ranking Task in TREC 2010 Blog Track. The experiment shows our approach attains a prominent performance in the retrieval of important news stories and gets 9.94% improvement over the best performance of participating systems in TREC 2010 Blog Track.

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Section: A Researches Within the Blogosphere And News Storymentioning

confidence: 99%

The Retrieval of Important News Stories by Influence Propagation among Communities and Categories

Lin¹,

Kao²

2012

2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology

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“…Based on such datasets, Schirru et al have presented a textual approach for the identification of topics and trends in specific domains [4]. By using a NMF-clustering algorithm, the articles of the domain were clustered by the content of their titles, with the goal to identify the different topics and events the bloggers are talking about.…”

Section: B Topics In the Blogospherementioning

confidence: 99%

Mining shared social media links to support clustering of blog articles

Obradović

Pimenta

Dengel

2011

2011 International Conference on Computational Aspects of Social Networks (CASoN)

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When monitoring blog articles for the tracking of a certain personality or product, the automatic identification of topic clusters is of high interest. Clustering by textual content is a popular method to accomplish this. In this paper we investigate how links between individual blog articles can be used to support this clustering with another dimension of information. Given the existing component structure of these networks, we focus on the extension with links based on shared social media resources. We show that the component structure extended in this way is of very high use for supporting textual clustering algorithms, and may be used for a new type of hybrid clustering algorithms in the future.

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