Applying weighted PageRank to author citation networks

This paper aims to identify whether different weighted PageRank algorithms can be applied to author citation networks to measure the popularity and prestige of a scholar from a citation perspective. Information Retrieval (IR) was selected as a test field and data from 1956-2008 were collected from Web of Science (WOS). Weighted PageRank with citation and publication as weighted vectors were calculated on author citation networks. The results indicate that both popularity rank and prestige rank were highly correlated with the weighted PageRank. Principal Component Analysis (PCA) was conducted to detect relationships among these different measures. For capturing prize winners within the IR field, prestige rank outperformed all the other measures.Comment: 19 pages, 4 figures, 5 table

An Exploratory Study about Microblogging Acceptance at Work

Microblogging is the new Web 2.0 hype in the media. Techies, politicians, family members and many more use Twitter to keep in touch with their interest groups, their voters or their friends and relatives. We wanted to know whether Twitter can also keep us aware about our team colleagues, how this improves teamwork and finally why Twitter is accepted and used in teams. Based on an action research study about Twitter usage in a team of seven researchers and the findings of prior literature, we attempt to extend the unified theory of technology acceptance (Venkatesh 2003) and adapt it to the specific context of microblogging in teams. Extending the performance expectancy construct, we propose two groups of factors inherent to social software that should be integrated into the UTAUT: the task characteristics of other users and the individual motivations for using social software

Understanding Factors Influencing Users’ Retweeting Behavior---A Theoretical Perspective

Currently, a large percentage of tweets in micro-blogging platform are retweets. In this study, we propose to examine the factors that motivate users’ retweeting behavior, leading users to prefer to transform others’ tweets than posting their own. We suggest that Information Sharing Self-Efficacy, Attachment Motivation and Critical Mass are the three antecedents contributing to the users’ retweeting behavior. Both theoretical and practical implications of this study are also discussed

Automated Social Hierarchy Detection through Email Network Analysis

We present our work on automatically extracting social hierarchies from electronic communication data. Data mining based on user behavior can be leveraged to analyze and catalog patterns of communications between entities to rank relationships. The advantage is that the analysis can be done in an automatic fashion and can adopt itself to organizational changes over time. We illustrate the algorithms over real world data using the Enron corporation's email archive. The results show great promise when compared to the corporations work chart and judicial proceeding analyzing the major players

Search Personalization: Knowledge-Based Recommendation in Digital Libraries

Recommendation engines have made great strides in understanding and implementing search personalization techniques to provide interesting and relevant documents to users. The latest research effort advances a new type of recommendation technique, Knowledge Based (KB) engines, that strive to understand the context of the user’s current information need and then filter information accordingly. The KB engine proposed in this paper requires less effort from the user in representing the search task and is the first of its kind implemented in a digital library setting. The KB engine performance was compared with Content Based (CB) and Collaborative Filtering (CF) recommendation techniques and the text search engine Lucene by asking sixty subjects to perform two different tasks to find relevant documents in a database of 212,000 documents from 22 National Science Digital Library (NSDL) collections. Our KB engine design outperforms CB, CF, and text search techniques in nearly all areas of evaluation

Effectiveness of Corporate Social Media Activities to Increase Relational Outcomes

This study applies social media analytics to investigate the impact of different corporate social media activities on user word of mouth and attitudinal loyalty. We conduct a multilevel analysis of approximately 5 million tweets regarding the main Twitter accounts of 28 large global companies. We empirically identify different social media activities in terms of social media management strategies (using social media management tools or the web-frontend client), account types (broadcasting or receiving information), and communicative approaches (conversational or disseminative). We find positive effects of social media management tools, broadcasting accounts, and conversational communication on public perception

A Network-based Approach to Mining Competitor Relationships from Online News

Identifying competitors for an individual company or a group of companies is important for businesses. Although people can consult paid company profile resources such as Hoover’s and Mergent, these sources are incomplete in company relationship coverage. We present an approach that uses graph-theoretic measures and machine learning techniques to achieve automated discovery of competitor relationships on the basis of structure of an intercompany network derived from company citations (cooccurrence) in online news articles. We also estimate to what extent our approach could extend the competitor relationships available from the data sources, Hoover’s and Mergent