Social Media and the Transformation of Activist Communication: Exploring the Social Media Ecology of the 2010 Toronto G20 Protests
Bibliographic record
Abstract
How does the massive use of social media in contemporary protests affect the character of activist communication? Moving away from the conceptualization of social media as tools, this research explores how activist social media communication is entangled with and shaped by heterogeneous techno-cultural and political economic relations. This exploration is pursued through a case study on the social media reporting efforts of the Toronto Community Mobilization Network, which coordinated and facilitated the protests against the 2010 Toronto G-20 summit. The network urged activists to report about the protests on Twitter, YouTube, and Flickr; tagging their contributions report. In addition, it set up a Facebook group and used a blog. The investigation, first, traces the hyperlink network in which the protest communication was embedded. The hyperlink analysis provides a window on the online ecology in which this communication unfolded. In addition, the examination interrogates how the particular technological architectures, related user practices, and business models of the various social platforms steered communication. This investigation shows that the use of social media brings about an acceleration of activist communication, and greatly enhances its visual character. Moreover, as activists massively embrace corporate social media, they increasingly lose control over the data they collective produce, as well as over the very architectures of the spaces through which they communicate.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".