The Geospatial Characteristics of a Social Movement Communication Network
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{"title"=>"The Geospatial Characteristics of a Social Movement Communication Network", "type"=>"journal", "authors"=>[{"first_name"=>"Michael D.", "last_name"=>"Conover", "scopus_author_id"=>"35941618400"}, {"first_name"=>"Clayton", "last_name"=>"Davis", "scopus_author_id"=>"55614670400"}, {"first_name"=>"Emilio", "last_name"=>"Ferrara", "scopus_author_id"=>"42661257200"}, {"first_name"=>"Karissa", "last_name"=>"McKelvey", "scopus_author_id"=>"55613996900"}, {"first_name"=>"Filippo", "last_name"=>"Menczer", "scopus_author_id"=>"6701785703"}, {"first_name"=>"Alessandro", "last_name"=>"Flammini", "scopus_author_id"=>"7003731568"}], "year"=>2013, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"19326203", "scopus"=>"2-s2.0-84874631930", "pui"=>"368465847", "doi"=>"10.1371/journal.pone.0055957", "sgr"=>"84874631930", "pmid"=>"23483885"}, "id"=>"37c8231c-7911-3187-86ac-5c9445af2ad7", "abstract"=>"Social movements rely in large measure on networked communication technologies to organize and disseminate information relating to the movements' objectives. In this work we seek to understand how the goals and needs of a protest movement are reflected in the geographic patterns of its communication network, and how these patterns differ from those of stable political communication. To this end, we examine an online communication network reconstructed from over 600,000 tweets from a thirty-six week period covering the birth and maturation of the American anticapitalist movement, Occupy Wall Street. We find that, compared to a network of stable domestic political communication, the Occupy Wall Street network exhibits higher levels of locality and a hub and spoke structure, in which the majority of non-local attention is allocated to high-profile locations such as New York, California, and Washington D.C. Moreover, we observe that information flows across state boundaries are more likely to contain framing language and references to the media, while communication among individuals in the same state is more likely to reference protest action and specific places and times. Tying these results to social movement theory, we propose that these features reflect the movement's efforts to mobilize resources at the local level and to develop narrative frames that reinforce collective purpose at the national level.", "link"=>"http://www.mendeley.com/research/geospatial-characteristics-social-movement-communication-network-8", "reader_count"=>132, "reader_count_by_academic_status"=>{"Unspecified"=>4, "Professor > Associate Professor"=>6, "Librarian"=>1, "Researcher"=>24, "Student > Doctoral Student"=>9, "Student > Ph. D. Student"=>40, "Student > Postgraduate"=>2, "Student > Master"=>24, "Other"=>2, "Student > Bachelor"=>8, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>2, "Professor"=>9}, "reader_count_by_user_role"=>{"Unspecified"=>4, "Professor > Associate Professor"=>6, "Librarian"=>1, "Researcher"=>24, "Student > Doctoral Student"=>9, "Student > Ph. D. Student"=>40, "Student > Postgraduate"=>2, "Student > Master"=>24, "Other"=>2, "Student > Bachelor"=>8, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>2, "Professor"=>9}, "reader_count_by_subject_area"=>{"Unspecified"=>6, "Agricultural and Biological Sciences"=>1, "Arts and Humanities"=>7, "Philosophy"=>1, "Business, Management and Accounting"=>1, "Computer Science"=>23, "Earth and Planetary Sciences"=>3, "Economics, Econometrics and Finance"=>2, "Engineering"=>4, "Environmental Science"=>6, "Nursing and Health Professions"=>1, "Materials Science"=>1, "Mathematics"=>5, "Medicine and Dentistry"=>1, "Design"=>1, "Physics and Astronomy"=>6, "Psychology"=>3, "Social Sciences"=>60}, "reader_count_by_subdiscipline"=>{"Materials Science"=>{"Materials Science"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>1}, "Social Sciences"=>{"Social Sciences"=>60}, "Physics and Astronomy"=>{"Physics and Astronomy"=>6}, "Psychology"=>{"Psychology"=>3}, "Mathematics"=>{"Mathematics"=>5}, "Unspecified"=>{"Unspecified"=>6}, "Environmental Science"=>{"Environmental Science"=>6}, "Arts and Humanities"=>{"Arts and Humanities"=>7}, "Design"=>{"Design"=>1}, "Engineering"=>{"Engineering"=>4}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>3}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>2}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>1}, "Computer Science"=>{"Computer Science"=>23}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>1}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>1}, "Philosophy"=>{"Philosophy"=>1}}, "reader_count_by_country"=>{"Romania"=>1, "Hungary"=>1, "United States"=>9, "Japan"=>1, "United Kingdom"=>1, "Portugal"=>2, "Spain"=>1, "Canada"=>2, "Venezuela"=>1, "Netherlands"=>1, "Bangladesh"=>1, "Belgium"=>1, "China"=>1, "Finland"=>1, "Italy"=>1}, "group_count"=>11}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/978520"], "description"=>"<p>Ordered by the amount of traffic associated with each state, it is clear that a few high-profile locations serve as the dominant sources of content in the Occupy stream. This concentration stands in contrast to the more heterogeneous activity profile for the stream of domestic political communication.</p>", "links"=>[], "tags"=>["retweet"], "article_id"=>645475, "categories"=>["Information And Computing Sciences"], "users"=>["Michael D. Conover", "Clayton Davis", "Emilio Ferrara", "Karissa McKelvey", "Filippo Menczer", "Alessandro Flammini"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0055957.g001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Proportion_of_retweet_traffic_associated_with_each_state_for_each_content_stream_/645475", "title"=>"Proportion of retweet traffic associated with each state, for each content stream.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-03-07 08:35:50"}
  • {"files"=>["https://ndownloader.figshare.com/files/978521"], "description"=>"<p>This cartogram uses color to represent the extent to which the number of Occupy Wall Street tweets in each state deviates from the domestic political communication baseline, computed as: . Redder colors indicate that proportionally more Occupy content originated from the associated state, while whiter colors indicate the opposite. To minimize the effect of outliers on the visualization and to highlight variation between states, colors for Maine and Oregon have been fixed, indicating that the deviation from baseline is more than three times the expected rate.</p>", "links"=>[], "tags"=>["geographic"], "article_id"=>645476, "categories"=>["Information And Computing Sciences"], "users"=>["Michael D. Conover", "Clayton Davis", "Emilio Ferrara", "Karissa McKelvey", "Filippo Menczer", "Alessandro Flammini"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0055957.g002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Divergences_in_geographic_distribution_of_users_/645476", "title"=>"Divergences in geographic distribution of users.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-03-07 08:36:25"}
  • {"files"=>["https://ndownloader.figshare.com/files/978524"], "description"=>"<p>Occupy Wall Street users, by state, exhibit a lower content production to consumption ratio relative to users in the domestic political communication stream. The disproportionately high ratio observed for Kentucky can be attributed to the activity of a prolific, highly popular left-leaning user from that state.</p>", "links"=>[], "tags"=>["computer science"], "article_id"=>645479, "categories"=>["Information And Computing Sciences"], "users"=>["Michael D. Conover", "Clayton Davis", "Emilio Ferrara", "Karissa McKelvey", "Filippo Menczer", "Alessandro Flammini"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0055957.g003"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Ratio_of_content_production_versus_content_consumption_by_stream_/645479", "title"=>"Ratio of content production versus content consumption, by stream.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-03-07 08:36:50"}
  • {"files"=>["https://ndownloader.figshare.com/files/978529"], "description"=>"<p>Stable domestic political communication is shown at left, Occupy Wall Street at right. Edges adjacent to New York, California, and Washington D.C. are shown in red. Note that Occupy Wall Street exhibits a clear hub-and-spoke pattern, with the majority of traffic flowing to and from New York, California, and Washington, D.C. Likewise, observe that the Occupy Wall Street network exhibits diminished levels of interstate connectivity compared to the network of domestic political communication. We note that the structure of this network backbone is robust to different parameterizations of alpha.</p>", "links"=>[], "tags"=>["backbone", "continental", "interstate"], "article_id"=>645484, "categories"=>["Information And Computing Sciences"], "users"=>["Michael D. Conover", "Clayton Davis", "Emilio Ferrara", "Karissa McKelvey", "Filippo Menczer", "Alessandro Flammini"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0055957.g004"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Multiscale_backbone_of_the_continental_interstate_communication_networks_/645484", "title"=>"Multiscale backbone () of the continental interstate communication networks.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-03-07 08:37:19"}
  • {"files"=>["https://ndownloader.figshare.com/files/978530"], "description"=>"<p>The edge weight corresponding to each cell is mapped to a color hue on a logarithmic scale ranging from white for edges with the least weight to black for edges with the most weight. The strong diaonalization and limited off-diagonal mass apparent in the Occupy Wall Street matrix is indicative of highly localized communication activity.</p>", "links"=>[], "tags"=>["matrices", "describing", "directed", "interstate"], "article_id"=>645485, "categories"=>["Information And Computing Sciences"], "users"=>["Michael D. Conover", "Clayton Davis", "Emilio Ferrara", "Karissa McKelvey", "Filippo Menczer", "Alessandro Flammini"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0055957.g005"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Connectivity_matrices_describing_directed_interstate_communication_volume_/645485", "title"=>"Connectivity matrices describing directed interstate communication volume.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-03-07 08:37:48"}
  • {"files"=>["https://ndownloader.figshare.com/files/1011797"], "description"=>"<p>‘Ratio’, defined as , is small when a token is more common in intrastate traffic and large when a token is more common in interstate traffic. Terms relating to rallying supporters are more predominant in intrastate communication, while interstate traffic tends to favor terms such as protest slogans and references to the media.</p>", "links"=>[], "tags"=>["tokens", "overrepresented", "intrastate", "interstate"], "article_id"=>672206, "categories"=>["Information And Computing Sciences"], "users"=>["Michael D. Conover", "Clayton Davis", "Emilio Ferrara", "Karissa McKelvey", "Filippo Menczer", "Alessandro Flammini"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0055957.t001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Lists_of_tokens_most_overrepresented_in_intrastate_and_interstate_communication_/672206", "title"=>"Lists of tokens most overrepresented in intrastate and interstate communication.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-03-06 00:36:46"}

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/Population biology", "average_usage"=>[269, 448, 558, 658, 744, 830, 914, 995, 1068, 1139, 1214, 1284, 1349]}, {"subject_area"=>"/Biology and life sciences/Psychology", "average_usage"=>[284, 458, 579, 688, 782, 879, 957, 1048, 1126, 1204, 1280, 1357, 1415]}, {"subject_area"=>"/Computer and information sciences/Network analysis", "average_usage"=>[340, 548, 701, 844, 945, 1073, 1189, 1282, 1391, 1488, 1562, 1660, 1731]}, {"subject_area"=>"/Engineering and technology", "average_usage"=>[254, 440, 558, 675, 785, 883, 972, 1061, 1154, 1248, 1336, 1414, 1489]}, {"subject_area"=>"/People and places", "average_usage"=>[270, 456, 573, 676, 773, 867, 955, 1037, 1120, 1205, 1286, 1363, 1436]}]}
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