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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 ...
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Keyword:
Altruism, Morality, and Social Solidarity; Collective Behavior and Social Movements; Communication; Computational Linguistics; FOS Languages and literature; FOS Sociology; Human Rights; Linguistics; Peace, War, and Social Conflict; Political Economy of the World System; Social and Behavioral Sciences; Social Influence and Political Communication; Social Media; Sociology
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URL: https://dx.doi.org/10.17605/osf.io/t426r https://osf.io/preprints/socarxiv/t426r/
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Sentiment analysis methods for understanding large-scale texts: a case for using continuum-scored words and word shift graphs ...
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Sentiment analysis methods for understanding large-scale texts: a case for using continuum-scored words and word shift graphs ...
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Forecasting the onset and course of mental illness with Twitter data
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The Lexicocalorimeter: Gauging public health through caloric input and output on social media
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Forecasting the onset and course of mental illness with Twitter data
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