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Abstract Political and social scientists have been relying extensively on keywords such as hashtags to mine social movement data from social media sites, particularly Twitter. Yet, prior work demonstrates that unrepresentative keyword sets can lead to flawed research conclusions. Numerous keyword expansion methods have been proposed to increase the comprehensiveness of keywords, but systematic evaluations of these methods have been lacking. Our paper fills this gap. We evaluate five diverse keyword expansion techniques (or pipelines) on five representative social movements across two distinct activity levels. Our results guide researchers who aim to use social media keyword searches to mine data. For instance, we show that word embedding-based methods significantly outperform other even more complex and newer approaches when movements are in normal activity periods. These methods are also less computationally intensive. More importantly, we also observe that no single pipeline can identify little more than half of all movement-related tweets when these movements are at their peak mobilization period offline. However, coverage can increase significantly when more than one pipeline is used. This is true even when the pipelines are selected at random.
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Beyond the eye-catchers: A large-scale study of social movement organizations’ involvement in online protests
Existing studies of social movement organizations (SMOs) commonly focus only on a small number of well-known SMOs or SMOs that belong to a single social movement industry (SMI). This is partially because current methods for identifying SMOs are labor-intensive. In contrast to these manual approaches, in our article, we use Twitter data pertaining to BlackLivesMatter and Women’s movements and employ crowdsourcing and nested supervised learning methods to identify more than 50K SMOs. Our results reveal that the behavior and influence of SMOs vary significantly, with half having little influence and behaving in similar ways to an average individual. Furthermore, we show that collectively, small SMOs contributed to the sharing of more diverse information. However, on average, large SMOs were significantly more committed to movements and decidedly more successful at recruiting. Finally, we also observe that a large number of SMOs from an extensive set of SMIs, including Occupy Wall Street, participated in solidarity or even as leaders in BlackLivesMatter. In comparison, few SMIs participated in Women’s movement.
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- PAR ID:
- 10187797
- Date Published:
- Journal Name:
- New Media & Society
- ISSN:
- 1461-4448
- Page Range / eLocation ID:
- 146144482094385
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1177/1461444820943854
