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      Divergent discourse between protests and counter-protests: #BlackLivesMatter and #AllLivesMatter

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          Abstract

          Since the shooting of Black teenager Michael Brown by White police officer Darren Wilson in Ferguson, Missouri, the protest hashtag #BlackLivesMatter has amplified critiques of extrajudicial killings of Black Americans. In response to #BlackLivesMatter, other Twitter users have adopted #AllLivesMatter, a counter-protest hashtag whose content argues that equal attention should be given to all lives regardless of race. Through a multi-level analysis of over 860,000 tweets, we study how these protests and counter-protests diverge by quantifying aspects of their discourse. We find that #AllLivesMatter facilitates opposition between #BlackLivesMatter and hashtags such as #PoliceLivesMatter and #BlueLivesMatter in such a way that historically echoes the tension between Black protesters and law enforcement. In addition, we show that a significant portion of #AllLivesMatter use stems from hijacking by #BlackLivesMatter advocates. Beyond simply injecting #AllLivesMatter with #BlackLivesMatter content, these hijackers use the hashtag to directly confront the counter-protest notion of “All lives matter.” Our findings suggest that Black Lives Matter movement was able to grow, exhibit diverse conversations, and avoid derailment on social media by making discussion of counter-protest opinions a central topic of #AllLivesMatter, rather than the movement itself.

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          Most cited references36

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          Community structure in social and biological networks

          A number of recent studies have focused on the statistical properties of networked systems such as social networks and the World-Wide Web. Researchers have concentrated particularly on a few properties which seem to be common to many networks: the small-world property, power-law degree distributions, and network transitivity. In this paper, we highlight another property which is found in many networks, the property of community structure, in which network nodes are joined together in tightly-knit groups between which there are only looser connections. We propose a new method for detecting such communities, built around the idea of using centrality indices to find community boundaries. We test our method on computer generated and real-world graphs whose community structure is already known, and find that it detects this known structure with high sensitivity and reliability. We also apply the method to two networks whose community structure is not well-known - a collaboration network and a food web - and find that it detects significant and informative community divisions in both cases.
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            Finding and evaluating community structure in networks

            We propose and study a set of algorithms for discovering community structure in networks -- natural divisions of network nodes into densely connected subgroups. Our algorithms all share two definitive features: first, they involve iterative removal of edges from the network to split it into communities, the edges removed being identified using one of a number of possible "betweenness" measures, and second, these measures are, crucially, recalculated after each removal. We also propose a measure for the strength of the community structure found by our algorithms, which gives us an objective metric for choosing the number of communities into which a network should be divided. We demonstrate that our algorithms are highly effective at discovering community structure in both computer-generated and real-world network data, and show how they can be used to shed light on the sometimes dauntingly complex structure of networked systems.
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              The Dynamics of Protest Recruitment through an Online Network

              The recent wave of mobilizations in the Arab world and across Western countries has generated much discussion on how digital media is connected to the diffusion of protests. We examine that connection using data from the surge of mobilizations that took place in Spain in May 2011. We study recruitment patterns in the Twitter network and find evidence of social influence and complex contagion. We identify the network position of early participants (i.e. the leaders of the recruitment process) and of the users who acted as seeds of message cascades (i.e. the spreaders of information). We find that early participants cannot be characterized by a typical topological position but spreaders tend to be more central in the network. These findings shed light on the connection between online networks, social contagion, and collective dynamics, and offer an empirical test to the recruitment mechanisms theorized in formal models of collective action.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: MethodologyRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: Formal analysisRole: MethodologyRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Formal analysisRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: MethodologyRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                2018
                18 April 2018
                : 13
                : 4
                : e0195644
                Affiliations
                [1 ] Department of Mathematics and Statistics, University of Vermont, Burlington, VT, United States of America
                [2 ] Computational Story Lab, Vermont Complex Systems Center, University of Vermont, Burlington, VT, United States of America
                [3 ] Vermont Advanced Computing Core, University of Vermont, Burlington, VT, United States of America
                Universidad de Zaragoza, SPAIN
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0003-3040-4500
                Article
                PONE-D-18-01492
                10.1371/journal.pone.0195644
                5906010
                29668754
                2a087c2a-7f9b-4467-8a7e-62575819003a
                © 2018 Gallagher et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 15 January 2018
                : 26 March 2018
                Page count
                Figures: 8, Tables: 2, Pages: 23
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100000001, National Science Foundation;
                Award ID: 1447634
                Award Recipient :
                PSD and CMD acknowledge support from National Science Foundation Big Data Grant #1447634 ( https://www.nsf.gov/awardsearch/showAward?AWD_ID=1447634). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Social Sciences
                Sociology
                Communications
                Social Communication
                Social Media
                Twitter
                Computer and Information Sciences
                Network Analysis
                Social Networks
                Social Media
                Twitter
                Social Sciences
                Sociology
                Social Networks
                Social Media
                Twitter
                Social Sciences
                Linguistics
                Semantics
                Biology and Life Sciences
                Ecology
                Ecological Metrics
                Species Diversity
                Shannon Index
                Ecology and Environmental Sciences
                Ecology
                Ecological Metrics
                Species Diversity
                Shannon Index
                Social Sciences
                Sociology
                Criminology
                Police
                People and Places
                Population Groupings
                Professions
                Police
                Computer and Information Sciences
                Network Analysis
                Centrality
                Social Sciences
                Law and Legal Sciences
                Criminal Justice System
                Law Enforcement
                Physical Sciences
                Physics
                Thermodynamics
                Entropy
                Physical Sciences
                Mathematics
                Probability Theory
                Statistical Distributions
                Custom metadata
                The raw tweet data used in this study cannot be released under the Twitter Terms of Agreement. We make available the IDs of all tweets used in this study, which can be used to retrieve the tweets through Twitter’s Public API. These tweet IDs are available on Harvard Dataverse: http://dx.doi.org/10.7910/DVN/IQ525U.

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