Modeling and Data Mining in Blogosphere

Liu, Huan; Agarwal, Nitin

doi:10.1007/978-3-031-01898-5

Cited by 29 publications

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

A study on mental models of taggers and experts for article indexing based on analysis of keyword usage

Chen

2014

Asso for Info Science & Tech

View full text Add to dashboard Cite

This article explores the mental models of article indexing of taggers and experts in keyword usage. Better understanding of the mental models of taggers and experts and their usage gap may inspire better selection of appropriate keywords for organizing information resources. Using a data set of 3,972 tags from CiteULike and 6,708 descriptors from Library and Information Science Abstracts (LISA) from 1,489 scholarly articles of 13 library and information science journals, social network analysis and frequent-pattern tree methods were used to capture and build up the mental models of article indexing of taggers and experts when using keywords, and to generalize their structures and patterns.When measured with respect to the terms used, a powerlaw distribution, a comparison of terms used as tags and descriptors, social network analysis (including centrality, overall structure, and role equivalent) and frequentpattern tree analysis, little similarity was found between the mental models of taggers and experts. Twenty-five patterns of path-based rules and 12 identical rules of frequent-pattern trees were shared by taggers and experts. Title-and topic-related keyword categories were the most popular keyword categories used in pathbased rules of frequent-pattern trees, and also the most popular members of 25 patterns and the starting point of the 12 identical rules.

show abstract

A study on mental models of taggers and experts for article indexing based on analysis of keyword usage

Chen

2014

Asso for Info Science & Tech

View full text Add to dashboard Cite

show abstract

Influence diffusion detection using the influence style (INFUSE) model

Tan

Ding

2015

Asso for Info Science & Tech

View full text Add to dashboard Cite

Blogs are readily available sources of opinions and sentiments that in turn could influence the opinions of the blog readers. Previous studies have attempted to infer influence from blog features, but they have ignored the possible influence styles that describe the different ways in which influence is exerted. We propose a novel approach to analyzing bloggers' influence styles and using the influence styles as features to improve the performance of influence diffusion detection among linked bloggers. The proposed influence style (INFUSE) model describes bloggers' influence through their engagement style, persuasion style, and persona. Methods used include similarity analysis to detect the creating−sharing aspect of engagement style, subjectivity analysis to measure persuasion style, and sentiment analysis to identify persona style. We further extend the INFUSE model to detect influence diffusion among linked bloggers based on the bloggers' influence styles. The INFUSE model performed well with an average F1 score of 76% compared with the in‐degree and sentiment‐value baseline approaches. Previous studies have focused on the existence of influence among linked bloggers in detecting influence diffusion, but our INFUSE model is shown to provide a fine‐grained description of the manner in which influence is diffused based on the bloggers' influence styles.

show abstract

Understanding online groups through social media

Barbier

Tang

Liu

2011

WIREs Data Min & Knowl

View full text Add to dashboard Cite

Multiple fields including sociology, anthropology, and business are interested in understanding group behavior. Applying data mining techniques to social media can help provide insights into group behavior and divulge a group's characteristics by identifying a group, developing a profile for a group, revealing the sentiment of a group, and detailing a group's composition. The ability to accomplish these tasks has practical business and scientific applications such as understanding customers better and providing new insights into influence propagation, as well as the ability to accurately categorize groups over time. This paper highlights some ongoing research efforts aiming at understanding groups through social media. © 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 330–338 DOI: 10.1002/widm.37 This article is categorized under: Algorithmic Development > Web Mining Application Areas > Society and Culture Fundamental Concepts of Data and Knowledge > Data Concepts Fundamental Concepts of Data and Knowledge > Human Centricity and User Interaction

show abstract

Modeling and Data Mining in Blogosphere

Cited by 29 publications

References 56 publications

A study on mental models of taggers and experts for article indexing based on analysis of keyword usage

A study on mental models of taggers and experts for article indexing based on analysis of keyword usage

Influence diffusion detection using the influence style (INFUSE) model

Understanding online groups through social media

Contact Info

Product

Resources

About