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Exploring Default Mode and Information Flow on the Web

Overview of attention for article published in PLOS ONE, April 2013
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Title
Exploring Default Mode and Information Flow on the Web
Published in
PLOS ONE, April 2013
DOI 10.1371/journal.pone.0060398
Pubmed ID
Authors

Mizuki Oka, Takashi Ikegami

Abstract

Social networking services (e.g., Twitter, Facebook) are now major sources of World Wide Web (called "Web") dynamics, together with Web search services (e.g., Google). These two types of Web services mutually influence each other but generate different dynamics. In this paper, we distinguish two modes of Web dynamics: the reactive mode and the default mode. It is assumed that Twitter messages (called "tweets") and Google search queries react to significant social movements and events, but they also demonstrate signs of becoming self-activated, thereby forming a baseline Web activity. We define the former as the reactive mode and the latter as the default mode of the Web. In this paper, we investigate these reactive and default modes of the Web's dynamics using transfer entropy (TE). The amount of information transferred between a time series of 1,000 frequent keywords in Twitter and the same keywords in Google queries is investigated across an 11-month time period. Study of the information flow on Google and Twitter revealed that information is generally transferred from Twitter to Google, indicating that Twitter time series have some preceding information about Google time series. We also studied the information flow among different Twitter keywords time series by taking keywords as nodes and flow directions as edges of a network. An analysis of this network revealed that frequent keywords tend to become an information source and infrequent keywords tend to become sink for other keywords. Based on these findings, we hypothesize that frequent keywords form the Web's default mode, which becomes an information source for infrequent keywords that generally form the Web's reactive mode. We also found that the Web consists of different time resolutions with respect to TE among Twitter keywords, which will be another focal point of this paper.

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Geographical breakdown

Country Count As %
Japan 2 3%
Brazil 2 3%
Germany 1 2%
Netherlands 1 2%
Ireland 1 2%
United Kingdom 1 2%
United States 1 2%
Unknown 50 85%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 17 29%
Researcher 10 17%
Student > Master 7 12%
Professor 5 8%
Professor > Associate Professor 4 7%
Other 12 20%
Unknown 4 7%
Readers by discipline Count As %
Computer Science 15 25%
Social Sciences 8 14%
Physics and Astronomy 8 14%
Business, Management and Accounting 4 7%
Psychology 4 7%
Other 12 20%
Unknown 8 14%