Search (6 results, page 1 of 1)

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Abstract

In this article, we use innovative full-text citation analysis along with supervised topic modeling and network-analysis algorithms to enhance classical bibliometric analysis and publication/author/venue ranking. By utilizing citation contexts extracted from a large number of full-text publications, each citation or publication is represented by a probability distribution over a set of predefined topics, where each topic is labeled by an author-contributed keyword. We then used publication/citation topic distribution to generate a citation graph with vertex prior and edge transitioning probability distributions. The publication importance score for each given topic is calculated by PageRank with edge and vertex prior distributions. To evaluate this work, we sampled 104 topics (labeled with keywords) in review papers. The cited publications of each review paper are assumed to be "important publications" for the target topic (keyword), and we use these cited publications to validate our topic-ranking result and to compare different publication-ranking lists. Evaluation results show that full-text citation and publication content prior topic distribution, along with the classical PageRank algorithm can significantly enhance bibliometric analysis and scientific publication ranking performance, comparing with term frequency-inverted document frequency (tf-idf), language model, BM25, PageRank, and PageRank + language model (p < .001), for academic information retrieval (IR) systems.

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Abstract

The authors of this paper investigate terms of consumers' diabetes based on a log from the Yahoo!Answers social question and answers (Q&A) forum, ascertain characteristics and relationships among terms related to diabetes from the consumers' perspective, and reveal users' diabetes information seeking patterns. In this study, the log analysis method, data coding method, and visualization multiple-dimensional scaling analysis method were used for analysis. The visual analyses were conducted at two levels: terms analysis within a category and category analysis among the categories in the schema. The findings show that the average number of words per question was 128.63, the average number of sentences per question was 8.23, the average number of words per response was 254.83, and the average number of sentences per response was 16.01. There were 12 categories (Cause & Pathophysiology, Sign & Symptom, Diagnosis & Test, Organ & Body Part, Complication & Related Disease, Medication, Treatment, Education & Info Resource, Affect, Social & Culture, Lifestyle, and Nutrient) in the diabetes related schema which emerged from the data coding analysis. The analyses at the two levels show that terms and categories were clustered and patterns were revealed. Future research directions are also included.

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Abstract

Bibliometric analysis based on literature in the Web of Science (WOS) has become an increasingly popular method for visualizing the structure of scientific fields. Keywords Plus and Author Keywords are commonly selected as units of analysis, despite the limited research evidence demonstrating the effectiveness of Keywords Plus. This study was conceived to evaluate the efficacy of Keywords Plus as a parameter for capturing the content and scientific concepts presented in articles. Using scientific papers about patient adherence that were retrieved from WOS, a comparative assessment of Keywords Plus and Author Keywords was performed at the scientific field level and the document level, respectively. Our search yielded more Keywords Plus terms than Author Keywords, and the Keywords Plus terms were more broadly descriptive. Keywords Plus is as effective as Author Keywords in terms of bibliometric analysis investigating the knowledge structure of scientific fields, but it is less comprehensive in representing an article's content.

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Date

8. 4.2015 16:22:13

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Abstract

We investigated a topic-based navigation guidance system in the World Health Organization portal, compared the link connection network and the semantic connection network derived from the guidance system, analyzed the characteristics of the 2 networks from the perspective of the node centrality (in_closeness, out_closeness, betweenness, in_degree, and out_degree), and provided the suggestions to optimize and enhance the topic-based navigation guidance system. A mixed research method that combines the social network analysis method, clustering analysis method, and inferential analysis methods was used. The clustering analysis results of the link connection network were quite different from those of the semantic connection network. There were significant differences between the link connection network and the semantic network in terms of density and centrality. Inferential analysis results show that there were no strong correlations between the centrality of a node and its topic information characteristics. Suggestions for enhancing the navigation guidance system are discussed in detail. Future research directions, such as application of the same research method presented in this study to other similar public health portals, are also included.

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Abstract

With the information overload of user-generated content in microblogging, users find it extremely challenging to browse and find valuable information in their first attempt. In this paper we propose a microblogging recommendation algorithm, TSI-MR (Topic-Level Social Influence-based Microblogging Recommendation), which can significantly improve users' microblogging experiences. The main innovation of this proposed algorithm is that we consider social influences and their indirect structural relationships, which are largely based on social status theory, from the topic level. The primary advantage of this approach is that it can build an accurate description of latent relationships between two users with weak connections, which can improve the performance of the model; furthermore, it can solve sparsity problems of training data to a certain extent. The realization of the model is mainly based on Factor Graph. We also applied a distributed strategy to further improve the efficiency of the model. Finally, we use data from Tencent Weibo, one of the most popular microblogging services in China, to evaluate our methods. The results show that incorporating social influence can improve microblogging performance considerably, and outperform the baseline methods.