The changing public sphere on Twitter: Network structure, elites and topics of the #righttobeforgotten
Bibliographic record
Abstract
Since the ruling of the European Court of Justice, the right to be forgotten has provided more informational self-determination to users, whilst raising new questions around Google’s role as arbiter of online content and the power to rewrite history. We investigated the debate that unfolded on Twitter around the #righttobeforgotten through social network analysis. The results revealed that latent topics, namely Google’s role as authority, alternated in popularity with rising and fading flare topics. The public sphere, or Öffentlichkeit, that we observed resembles the traditional one, with elite players such as news portals, experts and corporations participating, but it also differs significantly in terms of the underlying mechanisms and means of information diffusion. Experts are critical to comment, relay and make sense of information. We discuss the implications for theories of the public sphere and examine why social media do not serve as a democratising tool for ordinary citizens.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".