Social movements use social computing systems to complement offline mobilizations, but prior literature has focused almost exclusively on movement actors' use of social media. In this paper, we analyze participation and attention to topics connected with the Black Lives Matter movement in the English language version of Wikipedia between 2014 and 2016. Our results point to the use of Wikipedia to (1) intensively document and connect historical and contemporary events, (2) collaboratively migrate activity to support coverage of new events, and (3) dynamically re-appraise pre-existing knowledge in the aftermath of new events. These findings reveal patterns of behavior that complement theories of collective memory and collective action and help explain how social computing systems can encode and retrieve knowledge about social movements as they unfold.

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Amid the COVID-19 global pandemic, a highly troublesome influx of viral misinformation threatens to exacerbate the crisis through its deleterious effects on public health outcomes and health behavior decisions. This “misinfodemic” has ignited a surge of ongoing research aimed at characterizing its content, identifying its sources, and documenting its effects. Noticeably absent as of yet is a cogent strategy to disrupt misinformation. We start with the premise that the diffusion and persistence of COVID-19 misinformation are networked phenomena that require network interventions. To this end, we propose five classes of social network intervention to provide a roadmap of opportunities for disrupting misinformation dynamics during a global health crisis. Collectively, these strategies identify five distinct yet interdependent features of information environments that present viable opportunities for interventions. (Am J Public Health. Published online ahead of print January 21, 2021: e1–e6. https://doi.org/10.2105/AJPH.2020.306063 )

No abstract

Social movements use social computing systems to complement offline mobilizations, but prior literature has focused almost exclusively on movement actors' use of social media. In this paper, we analyze participation and attention to topics connected with the Black Lives Matter movement in the English language version of Wikipedia between 2014 and 2016. Our results point to the use of Wikipedia to (1) intensively document and connect historical and contemporary events, (2) collaboratively migrate activity to support coverage of new events, and (3) dynamically re-appraise preexisting knowledge in the aftermath of new events. These findings reveal patterns of behavior that complement theories of collective memory and collective action and help explain how social computing systems can encode and retrieve knowledge about social movements as they unfold.