Identifying opinion leaders in the blogosphere

Xue, Song; Chen, Yun; Hino, Koji; Tseng, Belle L.

doi:10.1145/1321440.1321588

Cited by 185 publications

(113 citation statements)

References 13 publications

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“…Song et al [327] define a function InfluenceRank, loosely modelled on PageRank, to rank blogs not only according to their importance relative to other blogs, but also how novel is the information they contribute to the network. It takes inspiration from PageRank's assumption that the importance of a webpage is proportional to the importance of the pages that link to it, and also the idea behind HITS [190] that good hubs point to good authorities, and good authorities are pointed to by good hubs.…”

Section: Influence On the Basis Of Topic And Sentimentmentioning

confidence: 99%

“…The calculation of information novelty used by Song et al [327] uses a method called Latent Dirichlet Allocation (LDA), which exploits co-occurrence patterns of words in documents to find semantically significant clusters of words which can then be grouped into topics. Then documents can be assigned probabilities for their membership of particular topics in the topic space, depending on how many of the key words of each topic occur in it, and how frequently.…”

Section: Influence On the Basis Of Topic And Sentimentmentioning

confidence: 99%

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Web Science: Understanding the Emergence of Macro-Level Features on the World Wide Web

O’Hara

Contractor²,

Hall

2013

FNT in Web Science

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Section: Influence On the Basis Of Topic And Sentimentmentioning

confidence: 99%

Section: Influence On the Basis Of Topic And Sentimentmentioning

confidence: 99%

Web Science: Understanding the Emergence of Macro-Level Features on the World Wide Web

O’Hara

Contractor²,

Hall

2013

FNT in Web Science

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“…Few algorithms being designed for blog analysis such as HITS algorithm [14], Random Walk technique to search for initiators, HP Labs researchers [15], used Twitter to analyze behavior of the users in a network, in [16] authors found the concept of initiator i.e. user who starts the conversation in the network and last but not least in [17], authors recommended a model which www.ijarai.thesai.org represent blogosphere as a graph and consist of nodes and edges where former represent the bloggers and later represents the blogger cites.…”

Section: Effective User Detection (Eud)mentioning

confidence: 99%

Using Mining Predict Relationships on the Social Media Network: Facebook (FB)

Mamta¹,

Chopra²

2015

ijarai

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Abstract-The objective of this paper is to study on the most famous social networking site Facebook and other online social media networks (OSMNs) based on the notion of relationship or friendship. This paper discussed the methodology which can used to conduct the analysis of the social network Facebook (FB) and also define the framework of the Web Mining platform. Lastly, various technological challenges were explored which were lying under the task of extracting information from FB and discuss in detail the about crawling agent functionality.

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“…In [25], Song, Chi, Hino, and Tseng give an algorithm and some experimental results aimed at identifying "opinion leaders" in a blogspace using a measure of a blog's influence or opinion leadership based on the novelty or originality of the information contained within the blog, giving higher weight to those blogs containing original material (versus those having a higher percentage of "reposted" material).…”

Section: Related Workmentioning

confidence: 99%

Quantifying sentiment and influence in blogspaces

Hui

Gregory

2010

Proceedings of the First Workshop on Social Media Analytics

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The weblog, or blog, has become a popular form of social media, through which authors can write posts, which can in turn generate feedback in the form of user comments. When considered in totality, a collection of blogs can thus be viewed as a sort of informal collection of mass sentiment and opinion. An obvious topic of interest might be to mine this collection to obtain some gauge of public sentiment over the wide variety of topics contained therein. However, the sheer size of the so-called blogosphere, combined with the fact that the subjects of posts can vary over a practically limitless number of topics poses some serious challenges when any meaningful analysis is attempted. Namely, the fact that largely anyone with access to the Internet can author their own blog, raises the serious issue of credibilityshould some blogs be considered to be more influential than others, and consequently, when gauging sentiment with respect to a topic, should some blogs be weighted more heavily than others? In addition, as new posts and comments can be made on almost a constant basis, any blog analysis algorithm must be able to handle such updates efficiently. In this paper, we give a formalization of the blog model. We give formal methods of quantifying sentiment and influence with respect to a hierarchy of topics, with the specific aim of facilitating the computation of a per-topic, influence-weighted sentiment measure. Finally, as efficiency is a specific endgoal, we give upper bounds on the time required to update these values with new posts, showing that our analysis and algorithms are scalable.

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Identifying opinion leaders in the blogosphere

Cited by 185 publications

References 13 publications

Web Science: Understanding the Emergence of Macro-Level Features on the World Wide Web

Web Science: Understanding the Emergence of Macro-Level Features on the World Wide Web

Using Mining Predict Relationships on the Social Media Network: Facebook (FB)

Quantifying sentiment and influence in blogspaces

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