Exploring Default Mode and Information Flow on the Web
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{"title"=>"Exploring Default Mode and Information Flow on the Web", "type"=>"journal", "authors"=>[{"first_name"=>"Mizuki", "last_name"=>"Oka", "scopus_author_id"=>"24802483600"}, {"first_name"=>"Takashi", "last_name"=>"Ikegami", "scopus_author_id"=>"7203071286"}], "year"=>2013, "source"=>"PLoS ONE", "identifiers"=>{"scopus"=>"2-s2.0-84876593302", "sgr"=>"84876593302", "issn"=>"19326203", "pmid"=>"23637749", "doi"=>"10.1371/journal.pone.0060398", "pui"=>"368794639"}, "id"=>"d1f1c487-df1c-3b14-b770-0b867ddf3f82", "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.", "link"=>"http://www.mendeley.com/research/exploring-default-mode-information-flow-web", "reader_count"=>48, "reader_count_by_academic_status"=>{"Unspecified"=>2, "Professor > Associate Professor"=>4, "Student > Doctoral Student"=>2, "Researcher"=>8, "Student > Ph. D. Student"=>15, "Student > Postgraduate"=>1, "Student > Master"=>6, "Other"=>2, "Student > Bachelor"=>2, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>2, "Professor"=>3}, "reader_count_by_user_role"=>{"Unspecified"=>2, "Professor > Associate Professor"=>4, "Student > Doctoral Student"=>2, "Researcher"=>8, "Student > Ph. D. Student"=>15, "Student > Postgraduate"=>1, "Student > Master"=>6, "Other"=>2, "Student > Bachelor"=>2, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>2, "Professor"=>3}, "reader_count_by_subject_area"=>{"Engineering"=>2, "Unspecified"=>3, "Mathematics"=>3, "Agricultural and Biological Sciences"=>2, "Business, Management and Accounting"=>4, "Physics and Astronomy"=>7, "Psychology"=>4, "Social Sciences"=>8, "Computer Science"=>14, "Economics, Econometrics and Finance"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>2}, "Social Sciences"=>{"Social Sciences"=>8}, "Physics and Astronomy"=>{"Physics and Astronomy"=>7}, "Psychology"=>{"Psychology"=>4}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>2}, "Computer Science"=>{"Computer Science"=>14}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>4}, "Mathematics"=>{"Mathematics"=>3}, "Unspecified"=>{"Unspecified"=>3}}, "reader_count_by_country"=>{"Netherlands"=>1, "United States"=>1, "Japan"=>3, "Ireland"=>1, "Brazil"=>2, "United Kingdom"=>1, "Germany"=>2, "Spain"=>1}, "group_count"=>3}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1034967"], "description"=>"<p>(Top) The ratio of keywords becoming sources and sinks shown as a function of keyword frequency over time. Red shows the source ratio and blue shows the sink ratio, as a function of keyword frequency over time. The frequent keywords tend to become source nodes and infrequent keywords tend to become sink nodes. (Bottom) Strong mediators are defined as having ample incoming and outgoing TE flow and weak mediators are defined as those with both weak incoming and outgoing TE flow. The number of strong and weak mediators is incorporated with the number of sinks and sources for each keyword.</p>", "links"=>[], "tags"=>["Computer applications", "Web-based applications", "text mining", "Applied mathematics", "Complex systems", "Nonlinear dynamics", "information science", "information theory", "Sociology", "social networks"], "article_id"=>690752, "categories"=>["Information And Computing Sciences", "Mathematics", "Sociology"], "users"=>["Mizuki Oka", "Takashi Ikegami"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0060398.g007", "stats"=>{"downloads"=>1, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_role_of_each_keyword_/690752", "title"=>"The role of each keyword.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-04-24 00:12:32"}
  • {"files"=>["https://ndownloader.figshare.com/files/1034965"], "description"=>"<p><i>Type a</i>) a keyword (e.g., “today” ranked 1st) acts as an information source, <i>type b</i>) a keyword (e.g., “wind” ranked 256th) acts as an information mediator, and <i>type c</i>) a keyword (e.g., “typhoon” ranked 551st) acts as an information sink.</p>", "links"=>[], "tags"=>["Computer applications", "Web-based applications", "text mining", "Applied mathematics", "Complex systems", "Nonlinear dynamics", "information science", "information theory", "Sociology", "social networks", "te", "keyword", "46", "keywords", "varying"], "article_id"=>690750, "categories"=>["Information And Computing Sciences", "Mathematics", "Sociology"], "users"=>["Mizuki Oka", "Takashi Ikegami"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0060398.g006", "stats"=>{"downloads"=>4, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Integrated_TE_of_a_given_keyword_to_from_the_other_46_keywords_by_varying_the_time_resolution_from_minute_to_minutes_/690750", "title"=>"Integrated TE of a given keyword to/from the other 46 keywords by varying the time resolution <b> from </b><b> minute to </b><b> minutes.</b>", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-04-24 00:12:30"}
  • {"files"=>["https://ndownloader.figshare.com/files/1034958"], "description"=>"<p>There was a tendency for less-frequent keywords to have a greater number of bursts and the fewer number of bursts were detected with frequent keywords.</p>", "links"=>[], "tags"=>["Computer applications", "Web-based applications", "text mining", "Applied mathematics", "Complex systems", "Nonlinear dynamics", "information science", "information theory", "Sociology", "social networks", "bursts", "keyword"], "article_id"=>690747, "categories"=>["Information And Computing Sciences", "Mathematics", "Sociology"], "users"=>["Mizuki Oka", "Takashi Ikegami"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0060398.g003", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Relationship_between_the_number_of_bursts_and_keyword_frequency_rankings_/690747", "title"=>"Relationship between the number of bursts and keyword frequency rankings.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-04-24 00:12:27"}
  • {"files"=>["https://ndownloader.figshare.com/files/1034956"], "description"=>"<p>Burst behaviors were clearly detected and synchronization between the two time series was often salient.</p>", "links"=>[], "tags"=>["Computer applications", "Web-based applications", "text mining", "Applied mathematics", "Complex systems", "Nonlinear dynamics", "information science", "information theory", "Sociology", "social networks", "infrequent", "keywords", "google", "queries", "twitter"], "article_id"=>690746, "categories"=>["Information And Computing Sciences", "Mathematics", "Sociology"], "users"=>["Mizuki Oka", "Takashi Ikegami"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0060398.g002", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Examples_of_infrequent_keywords_in_Google_queries_and_Twitter_keywords_/690746", "title"=>"Examples of infrequent keywords in Google queries and Twitter keywords.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-04-24 00:12:26"}
  • {"files"=>["https://ndownloader.figshare.com/files/1034970"], "description"=>"<p>The top 10 source and sink keywords from among 1000 keywords.</p>", "links"=>[], "tags"=>["Computer applications", "Web-based applications", "text mining", "Applied mathematics", "Complex systems", "Nonlinear dynamics", "information science", "information theory", "Sociology", "social networks", "10", "keywords", "1000"], "article_id"=>690755, "categories"=>["Information And Computing Sciences", "Mathematics", "Sociology"], "users"=>["Mizuki Oka", "Takashi Ikegami"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0060398.t001", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_top_10_source_and_sink_keywords_from_among_1000_keywords_/690755", "title"=>"The top 10 source and sink keywords from among 1000 keywords.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-04-24 00:12:35"}
  • {"files"=>["https://ndownloader.figshare.com/files/1034962"], "description"=>"<p>It was found that the information has the tendency to flow from Twitter to Google. For example, 692 keywords show that information is transferred from Twitter to Google (where ) and 308 keywords show that the information is transferred from Google to Twitter (where ) in the case of equals to one day.</p>", "links"=>[], "tags"=>["Computer applications", "Web-based applications", "text mining", "Applied mathematics", "Complex systems", "Nonlinear dynamics", "information science", "information theory", "Sociology", "social networks", "histogram", "150"], "article_id"=>690749, "categories"=>["Information And Computing Sciences", "Mathematics", "Sociology"], "users"=>["Mizuki Oka", "Takashi Ikegami"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0060398.g005", "stats"=>{"downloads"=>2, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_histogram_for_all_the_1_000_keywords_and_the_top_and_bottom_150_keywords_by_changing_from_one_to_four_days_/690749", "title"=>"The histogram for all the 1,000 keywords, and the top and bottom 150 keywords, by changing from one to four days.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-04-24 00:12:29"}
  • {"files"=>["https://ndownloader.figshare.com/files/1034960"], "description"=>"<p> Data computed from 46 keywords are superimposed in the figure, showing that the number of local peaks obeys a power-law of up to around 2<sup>9</sup>(= 512) minutes.</p>", "links"=>[], "tags"=>["Computer applications", "Web-based applications", "text mining", "Applied mathematics", "Complex systems", "Nonlinear dynamics", "information science", "information theory", "Sociology", "social networks", "peaks"], "article_id"=>690748, "categories"=>["Information And Computing Sciences", "Mathematics", "Sociology"], "users"=>["Mizuki Oka", "Takashi Ikegami"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0060398.g004", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Relationship_between_the_number_of_local_peaks_and_time_resolution_t_in_log_scales_/690748", "title"=>"Relationship between the number of local peaks and time resolution Δ<i>t</i> in log scales.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-04-24 00:12:28"}
  • {"files"=>["https://ndownloader.figshare.com/files/1034955"], "description"=>"<p>Burst behaviors are not salient and synchronization between the two time series was not observed.</p>", "links"=>[], "tags"=>["Computer applications", "Web-based applications", "text mining", "Applied mathematics", "Complex systems", "Nonlinear dynamics", "information science", "information theory", "Sociology", "social networks", "keywords", "twitter", "google"], "article_id"=>690745, "categories"=>["Information And Computing Sciences", "Mathematics", "Sociology"], "users"=>["Mizuki Oka", "Takashi Ikegami"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0060398.g001", "stats"=>{"downloads"=>5, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Examples_of_frequent_keywords_in_Twitter_keywords_and_Google_queries_/690745", "title"=>"Examples of frequent keywords in Twitter keywords and Google queries.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-04-24 00:12:25"}
  • {"files"=>["https://ndownloader.figshare.com/files/1034968"], "description"=>"<p>The top 100 keywords have a higher ratio of becoming sources compared to the bottom 100 keywords, whereas the bottom 100 keywords have a higher ratio of becoming sinks compared to the top 100 keywords.</p>", "links"=>[], "tags"=>["Computer applications", "Web-based applications", "text mining", "Applied mathematics", "Complex systems", "Nonlinear dynamics", "information science", "information theory", "Sociology", "social networks", "histogram", "ratios", "keyword", "becomes", "sources", "sinks", "computed", "100"], "article_id"=>690753, "categories"=>["Information And Computing Sciences", "Mathematics", "Sociology"], "users"=>["Mizuki Oka", "Takashi Ikegami"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0060398.g008", "stats"=>{"downloads"=>1, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_histogram_of_the_ratios_for_each_keyword_that_becomes_sources_a_and_sinks_b_computed_from_the_top_100_and_bottom_100_keywords_/690753", "title"=>"A histogram of the ratios for each keyword that becomes sources a) and sinks b) computed from the top 100 and bottom 100 keywords.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-04-24 00:12:33"}

PMC Usage Stats | Further Information

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Relative Metric

{"start_date"=>"2013-01-01T00:00:00Z", "end_date"=>"2013-12-31T00:00:00Z", "subject_areas"=>[{"subject_area"=>"/Biology and life sciences", "average_usage"=>[269, 466, 588, 697, 800, 896, 988, 1076, 1165, 1254, 1340, 1417]}, {"subject_area"=>"/Biology and life sciences/Behavior", "average_usage"=>[306, 490, 611, 718, 817, 916, 999, 1091, 1185, 1258, 1341, 1412, 1476]}, {"subject_area"=>"/Physical sciences", "average_usage"=>[254, 431, 547, 651, 748, 842, 932, 1017, 1098, 1178, 1259, 1336, 1404]}]}
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