Online monitoring activism: civic surveillance practices as a reaction to the rise of the far-right in the COVID-19 pandemic
Bibliographic record
Abstract
The rise of far-right movements during the COVID-19 pandemic has prompted a new form of individual digital activism: online monitoring activism (OMA). During the pandemic, online monitoring activists systematically collected, processed and published information from far-right Telegram channels and groups. Against the theoretical background of concepts like the monitory democracy and surveillance culture, we conducted eight semi-structured interviews with German online monitoring activists, investigating their motivations, roles, and the intricacies of their monitoring practices. Our findings reveal that online monitoring activists are driven by a sense of duty to counteract anti-democratic tendencies, which they perceive as inadequately addressed by institutional power. They gain localized and thematically specialized expertise that they share within loose networks of like-minded others. We highlight the activists’ liminal identity oscillating between virtuous citizenship and vigilantism, as well as the broader societal implications of their actions. On the one hand, they fulfill the role of active citizenship in monitory democracy; on the other, they also reinforce the transparency imperative and the wish for far-reaching security and control inherent to surveillance culture. The transparency potential afforded by their adversaries’ online connective action and mobilizing efforts legitimizes their surveillance and demands surveillance. Further normative work is needed to critically examine the extent and desirability of increased social control introduced to liberal democracy by online monitoring activism.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".