• Journal of Digital Contents Society
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• v.18 no.3
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• pp.551-558
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• 2017

The growth of blogs has two aspect of providing various information and marketing. This study collected the rankings of blog posts of large portal using OpenAPI and investigated the features of blogs ranked through the exploratory data analysis technique. As a result of the analysis, it was found that the influence of the blogger and the recent creation date of the post were highly influential factors in the top rank. Due to the weakness of these evaluation algorithms, there was a problem of showing the search results which is concentrated to the power blogger's post. In this study, we propose an algorithm that improves the reliability of content by adding the reliability DB information which is verified by the experts and reflects the fairness of the application of the ranking score through the trend index indicating various public interests. Improved algorithms have made it possible to provide more reliable information in the search results of the relevant field and have an effect of making it difficult to manipulate ranking by illegal applications that increase the number of visitors.

• The Journal of Society for e-Business Studies
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• v.17 no.4
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• pp.69-93
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• 2012

Various influence-related relationships in Social Network Services (SNS) among users, posts, and user-and-post, can be expressed using links. The current research evaluates the influence of specific users or posts by analyzing the link structure of relevant social network graphs to identify influential users. We applied the concept of mutual interactions proposed for ranking semantic web resources, rather than the voting notion of Page Rank or HITS, to blogosphere, one of the early SNS. Through many experiments with network models, where the performance and validity of each alternative approach can be analyzed, we showed the applicability and strengths of our approach. The weight tuning processes for the links of these network models enabled us to control the experiment errors form the link weight differences and compare the implementation easiness of alternatives. An additional example of how to enter the content scores of commercial or spam posts into the graph-based method is suggested on a small network model as well. This research, as a starting point of the study on identifying influential users in SNS, is distinctive from the previous researches in the following points. First, various influence-related properties that are deemed important but are disregarded, such as scraping, commenting, subscribing to RSS feeds, and trusting friends, can be considered simultaneously. Second, the framework reflects the general phenomenon where objects interacting with more influential objects increase their influence. Third, regarding the extent to which a bloggers causes other bloggers to act after him or her as the most important factor of influence, we treated sequential referencing relationships with a viewpoint from that of PageRank or HITS (Hypertext Induced Topic Selection).