The Critical Periphery in the Growth of Social Protests
Publication Date
November 30, 2015
Journal
PLOS ONE
Authors
Pablo Barberá, Ning Wang, Richard Bonneau, John T. Jost, et al
Volume
10
Issue
11
Pages
e0143611
DOI
https://dx.plos.org/10.1371/journal.pone.0143611
Publisher URL
http://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0143611
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/26618352
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4664236
Europe PMC
http://europepmc.org/abstract/MED/26618352
Web of Science
000365889800052
Scopus
84957556785
Mendeley
http://www.mendeley.com/research/critical-periphery-growth-social-protests
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Mendeley | Further Information

{"title"=>"The critical periphery in the growth of social protests", "type"=>"journal", "authors"=>[{"first_name"=>"Pablo", "last_name"=>"Barberá", "scopus_author_id"=>"36599873900"}, {"first_name"=>"Ning", "last_name"=>"Wang", "scopus_author_id"=>"57198399223"}, {"first_name"=>"Richard", "last_name"=>"Bonneau", "scopus_author_id"=>"7006793027"}, {"first_name"=>"John T.", "last_name"=>"Jost", "scopus_author_id"=>"24492154100"}, {"first_name"=>"Jonathan", "last_name"=>"Nagler", "scopus_author_id"=>"35869396100"}, {"first_name"=>"Joshua", "last_name"=>"Tucker", "scopus_author_id"=>"8159413200"}, {"first_name"=>"Sandra", "last_name"=>"González-Bailón", "scopus_author_id"=>"30967549800"}], "year"=>2015, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"19326203", "arxiv"=>"e0143611", "scopus"=>"2-s2.0-84957556785", "pui"=>"608065182", "doi"=>"10.1371/journal.pone.0143611", "isbn"=>"1932-6203", "sgr"=>"84957556785", "pmid"=>"26618352"}, "id"=>"fb993fdc-31d4-320c-acf2-b729e6474808", "abstract"=>"Social media have provided instrumental means of communication in many recent political protests. The efficiency of online networks in disseminating timely information has been praised by many commentators; at the same time, users are often derided as \"slacktivists\" because of the shallow commitment involved in clicking a forwarding button. Here we consider the role of these peripheral online participants, the immense majority of users who surround the small epicenter of protests, representing layers of diminishing online activity around the committed minority. We analyze three datasets tracking protest communication in different languages and political contexts through the social media platform Twitter and employ a network decomposition technique to examine their hierarchical structure. We provide consistent evidence that peripheral participants are critical in increasing the reach of protest messages and generating online content at levels that are comparable to core participants. Although committed minorities may constitute the heart of protest movements, our results suggest that their success in maximizing the number of online citizens exposed to protest messages depends, at least in part, on activating the critical periphery. Peripheral users are less active on a per capita basis, but their power lies in their numbers: their aggregate contribution to the spread of protest messages is comparable in magnitude to that of core participants. An analysis of two other datasets unrelated to mass protests strengthens our interpretation that core-periphery dynamics are characteristically important in the context of collective action events. Theoretical models of diffusion in social networks would benefit from increased attention to the role of peripheral nodes in the propagation of information and behavior.", "link"=>"http://www.mendeley.com/research/critical-periphery-growth-social-protests", "reader_count"=>116, "reader_count_by_academic_status"=>{"Unspecified"=>2, "Professor > Associate Professor"=>3, "Researcher"=>13, "Student > Doctoral Student"=>10, "Student > Ph. D. Student"=>37, "Student > Postgraduate"=>2, "Student > Master"=>27, "Other"=>7, "Student > Bachelor"=>10, "Lecturer"=>2, "Professor"=>3}, "reader_count_by_user_role"=>{"Unspecified"=>2, "Professor > Associate Professor"=>3, "Researcher"=>13, "Student > Doctoral Student"=>10, "Student > Ph. D. Student"=>37, "Student > Postgraduate"=>2, "Student > Master"=>27, "Other"=>7, "Student > Bachelor"=>10, "Lecturer"=>2, "Professor"=>3}, "reader_count_by_subject_area"=>{"Unspecified"=>3, "Agricultural and Biological Sciences"=>4, "Philosophy"=>2, "Arts and Humanities"=>3, "Business, Management and Accounting"=>7, "Computer Science"=>6, "Decision Sciences"=>2, "Economics, Econometrics and Finance"=>5, "Engineering"=>2, "Environmental Science"=>2, "Nursing and Health Professions"=>3, "Biochemistry, Genetics and Molecular Biology"=>1, "Mathematics"=>4, "Medicine and Dentistry"=>6, "Design"=>2, "Pharmacology, Toxicology and Pharmaceutical Science"=>1, "Psychology"=>10, "Social Sciences"=>53}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>6}, "Social Sciences"=>{"Social Sciences"=>53}, "Decision Sciences"=>{"Decision Sciences"=>2}, "Psychology"=>{"Psychology"=>10}, "Mathematics"=>{"Mathematics"=>4}, "Unspecified"=>{"Unspecified"=>3}, "Environmental Science"=>{"Environmental Science"=>2}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>1}, "Arts and Humanities"=>{"Arts and Humanities"=>3}, "Design"=>{"Design"=>2}, "Engineering"=>{"Engineering"=>2}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>5}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>4}, "Computer Science"=>{"Computer Science"=>6}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>7}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>3}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>1}, "Philosophy"=>{"Philosophy"=>2}}, "reader_count_by_country"=>{"Hong Kong"=>1, "Hungary"=>1, "United States"=>2, "Portugal"=>1, "Spain"=>1}, "group_count"=>5}

CrossRef

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/2593936"], "description"=>"<p>In this schematic representation, there are three protest participants that accumulate six unique followers. The relative reach of each participiant (nodes in red) is the fraction of their direct followers over the total available in the system (nodes in orange). We normalize these counts to fall in the interval [0,1] for the three networks.</p>", "links"=>[], "tags"=>["network decomposition technique", "core participants", "media platform Twitter", "protest messages", "user", "Social Protests Social media"], "article_id"=>1613894, "categories"=>["Uncategorised"], "users"=>["Pablo Barberá", "Ning Wang", "Richard Bonneau", "John T. Jost", "Jonathan Nagler", "Joshua Tucker", "Sandra González-Bailón"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0143611.g001", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Reach_measured_as_a_fraction_of_all_followers_/1613894", "title"=>"Reach measured as a fraction of all followers.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-11-30 02:42:08"}
  • {"files"=>["https://ndownloader.figshare.com/files/2593937"], "description"=>"<p>This technique recursively prunes the network to remove nodes with the lowest degree. The coreness of a vertex is <i>k</i> if it belongs to the <i>k</i>-core but not to the (<i>k</i>+1)-core.</p>", "links"=>[], "tags"=>["network decomposition technique", "core participants", "media platform Twitter", "protest messages", "user", "Social Protests Social media"], "article_id"=>1613895, "categories"=>["Uncategorised"], "users"=>["Pablo Barberá", "Ning Wang", "Richard Bonneau", "John T. Jost", "Jonathan Nagler", "Joshua Tucker", "Sandra González-Bailón"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0143611.g002", "stats"=>{"downloads"=>1, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Schematic_representation_of_the_k_core_decomposition_for_a_random_network_with_N_16_vertices_and_E_24_edges_/1613895", "title"=>"Schematic representation of the <i>k</i>-core decomposition for a random network with N = 16 vertices and E = 24 edges.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-11-30 02:42:08"}
  • {"files"=>["https://ndownloader.figshare.com/files/2593939"], "description"=>"<p>Participants have been grouped in their corresponding <i>k</i>-shells, here represented by nodes. Lower <i>k-</i>shells contain participants at the periphery of the network; higher <i>k</i>-shells contain core participants. Node size is proportional to aggregated activity, measured as total number of protest messages (not just retweets). Arcs indicate retweeting activity, and their width is proportional to normalized strength (arcs with lower strength have been filtered to improve the visualization of the network). The darkness of nodes is proportional to the percentage of participants who reported being in the Taksim Gezi Park (the geographical epicenter of the protests), as indicated by the geographic information attached to their tweets. Most of these participants are at the core of the network where most RTs are also sourced from, thus allowing information to flow from the core to the periphery.</p>", "links"=>[], "tags"=>["network decomposition technique", "core participants", "media platform Twitter", "protest messages", "user", "Social Protests Social media"], "article_id"=>1613897, "categories"=>["Uncategorised"], "users"=>["Pablo Barberá", "Ning Wang", "Richard Bonneau", "John T. Jost", "Jonathan Nagler", "Joshua Tucker", "Sandra González-Bailón"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0143611.g003", "stats"=>{"downloads"=>2, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_K_core_decomposition_of_the_network_of_retweets_that_emerged_during_the_2013_Taksim_Gezi_Park_protests_in_Turkey_see_S1_Text_/1613897", "title"=>"<i>K</i>-core decomposition of the network of retweets that emerged during the 2013 Taksim Gezi Park protests in Turkey (see S1 Text).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-11-30 02:42:08"}
  • {"files"=>["https://ndownloader.figshare.com/files/2593940"], "description"=>"<p>Panel A shows the distribution in number of followers (or reach) across <i>k</i>-shells. Panel B plots the distributions in number of tweets sent (or activity) across k-shells. Panel C shows the effects on overall reach and activity of removing <i>k</i>-cores progressively, starting from the lowest or most peripheral as illustrated by the networks below the horizontal axis. Removing the first five <i>k</i>-cores results in a drop of slightly more than 50% in total reach capacity, suggesting that the sphere of influence of core participants is much reduced without peripheral contributors. The random benchmark is based on 10,000 permutations of the data where assignment to <i>k</i>-cores is randomly re-shuffled; this benchmark can be interpreted as a line of perfect equality, i.e. a scenario in which all <i>k</i>-cores contribute the same amount to overall activity and reach.</p>", "links"=>[], "tags"=>["network decomposition technique", "core participants", "media platform Twitter", "protest messages", "user", "Social Protests Social media"], "article_id"=>1613898, "categories"=>["Uncategorised"], "users"=>["Pablo Barberá", "Ning Wang", "Richard Bonneau", "John T. Jost", "Jonathan Nagler", "Joshua Tucker", "Sandra González-Bailón"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0143611.g004", "stats"=>{"downloads"=>1, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Audience_size_and_activity_levels_across_k_cores_/1613898", "title"=>"Audience size and activity levels across <i>k</i>-cores.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-11-30 02:42:08"}
  • {"files"=>["https://ndownloader.figshare.com/files/2593943"], "description"=>"<p>Panels A and E visualize the connections across <i>k</i>-cores (arcs with lower strength have been filtered to improve visualization). Unlike the Turkish case, these protests did not have a clear epicenter, so nodes are colored in proportion to the number of retweets received (i.e. normalized instrength). Core-periphery dynamics are, however, similar to the Turkish case: most of the information flows from the core to the periphery, where users are significantly less active on a per capita basis but who, on the aggregate, contribute a similar volume of messages. Panels D and H show that removing the three outer <i>k</i>-cores results in a drop of audience of about 50%. The random benchmark is, again, based on 10,000 permutations of the data and it can be interpreted as a line of perfect equality.</p>", "links"=>[], "tags"=>["network decomposition technique", "core participants", "media platform Twitter", "protest messages", "user", "Social Protests Social media"], "article_id"=>1613901, "categories"=>["Uncategorised"], "users"=>["Pablo Barberá", "Ning Wang", "Richard Bonneau", "John T. Jost", "Jonathan Nagler", "Joshua Tucker", "Sandra González-Bailón"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0143611.g005", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Core_periphery_analysis_for_the_Occupy_and_Indignados_networks_/1613901", "title"=>"Core-periphery analysis for the Occupy and Indignados networks.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-11-30 02:42:08"}
  • {"files"=>["https://ndownloader.figshare.com/files/2593944"], "description"=>"<p>Arcs with lower strength have been filtered to improve network visualization.</p>", "links"=>[], "tags"=>["network decomposition technique", "core participants", "media platform Twitter", "protest messages", "user", "Social Protests Social media"], "article_id"=>1613902, "categories"=>["Uncategorised"], "users"=>["Pablo Barberá", "Ning Wang", "Richard Bonneau", "John T. Jost", "Jonathan Nagler", "Joshua Tucker", "Sandra González-Bailón"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0143611.g006", "stats"=>{"downloads"=>1, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Audience_size_and_activity_levels_across_k_cores_for_the_Oscars_network_panels_A_D_and_the_minimum_wage_network_panels_E_H_/1613902", "title"=>"Audience size and activity levels across <i>k</i>-cores for the Oscars network (panels A-D) and the minimum wage network (panels E-H).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-11-30 02:42:08"}
  • {"files"=>["https://ndownloader.figshare.com/files/2593946"], "description"=>"<p>The diagrams on the upper-right corner indicate the areas that were computed in each panel. The Oscars dataset is the only one in which removing the lower cores has a greater impact on activity than reach–hence its different color in the barplot of panel B.</p>", "links"=>[], "tags"=>["network decomposition technique", "core participants", "media platform Twitter", "protest messages", "user", "Social Protests Social media"], "article_id"=>1613904, "categories"=>["Uncategorised"], "users"=>["Pablo Barberá", "Ning Wang", "Richard Bonneau", "John T. Jost", "Jonathan Nagler", "Joshua Tucker", "Sandra González-Bailón"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0143611.g007", "stats"=>{"downloads"=>4, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Area_above_reach_and_activity_curves_bounded_by_random_benchmark_panel_A_and_area_between_reach_and_activity_curves_which_serves_as_an_index_of_core_periphery_dynamics_panel_B_/1613904", "title"=>"Area above reach and activity curves, bounded by random benchmark (panel A) and area between reach and activity curves, which serves as an index of core-periphery dynamics (panel B).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-11-30 02:42:08"}
  • {"files"=>["https://ndownloader.figshare.com/files/2593947"], "description"=>"<p>Summary statistics for the three retweet networks analyzed (largest weakly connected component).</p>", "links"=>[], "tags"=>["network decomposition technique", "core participants", "media platform Twitter", "protest messages", "user", "Social Protests Social media"], "article_id"=>1613905, "categories"=>["Uncategorised"], "users"=>["Pablo Barberá", "Ning Wang", "Richard Bonneau", "John T. Jost", "Jonathan Nagler", "Joshua Tucker", "Sandra González-Bailón"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0143611.t001", "stats"=>{"downloads"=>0, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Summary_statistics_for_the_three_retweet_networks_analyzed_largest_weakly_connected_component_/1613905", "title"=>"Summary statistics for the three retweet networks analyzed (largest weakly connected component).", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-11-30 02:42:08"}
  • {"files"=>["https://ndownloader.figshare.com/files/2593951"], "description"=>"<div><p>Social media have provided instrumental means of communication in many recent political protests. The efficiency of online networks in disseminating timely information has been praised by many commentators; at the same time, users are often derided as “slacktivists” because of the shallow commitment involved in clicking a forwarding button. Here we consider the role of these peripheral online participants, the immense majority of users who surround the small epicenter of protests, representing layers of diminishing online activity around the committed minority. We analyze three datasets tracking protest communication in different languages and political contexts through the social media platform Twitter and employ a network decomposition technique to examine their hierarchical structure. We provide consistent evidence that peripheral participants are critical in increasing the reach of protest messages and generating online content at levels that are comparable to core participants. Although committed minorities may constitute the heart of protest movements, our results suggest that their success in maximizing the number of online citizens exposed to protest messages depends, at least in part, on activating the critical periphery. Peripheral users are less active on a <i>per capita</i> basis, but their power lies in their numbers: their aggregate contribution to the spread of protest messages is comparable in magnitude to that of core participants. An analysis of two other datasets unrelated to mass protests strengthens our interpretation that core-periphery dynamics are characteristically important in the context of collective action events. Theoretical models of diffusion in social networks would benefit from increased attention to the role of peripheral nodes in the propagation of information and behavior.</p></div>", "links"=>[], "tags"=>["network decomposition technique", "core participants", "media platform Twitter", "protest messages", "user", "Social Protests Social media"], "article_id"=>1613909, "categories"=>["Uncategorised"], "users"=>["Pablo Barberá", "Ning Wang", "Richard Bonneau", "John T. Jost", "Jonathan Nagler", "Joshua Tucker", "Sandra González-Bailón"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0143611", "stats"=>{"downloads"=>13, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_Critical_Periphery_in_the_Growth_of_Social_Protests_/1613909", "title"=>"The Critical Periphery in the Growth of Social Protests", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-11-30 02:42:08"}

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Relative Metric

{"start_date"=>"2015-01-01T00:00:00Z", "end_date"=>"2015-12-31T00:00:00Z", "subject_areas"=>[]}
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Net::HTTPTooManyRequests

Source
Scopus
Time
2019-08-27 21:08:09 UTC
Target URL
https://api.elsevier.com/content/search/index:SCOPUS?query=DOI(10.1371%2Fjournal.pone.0143611)
Trace

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