After Twitter: Fragmentation, Platform Polities and Protective Sociality
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article argues that a profound change has occurred in the spaces of social media, centring on the region formerly occupied by Twitter. More than Twitter rebranding as X, After Twitter refers to a historical punctuation point in the timeline of social media and an emerging social media reality. After Twitter registers the slow death of a set of ideals and related practices specific to platforms like Twitter, but also to the waning of ideals in relation to the communicative potentials of the open web more generally. We make three broad claims which characterise social media After Twitter: First, by way of an overview of alternatives and competitors including Bluesky, Mastodon, Threads, Truth Social and more, we observe a social media fragmentation. Such fragmentation is not solely driven by economic forces or technological development and instead is understood along explicitly political lines. Second, we observe the rise of polarised platform polities. These polities reflect divergent political positions, create distinct political realities and foster different modes of interaction and belonging. Third, we observe a general shift from connective to protective forms of sociality, where users approach social media as if they are constantly in the presence of adversaries, and the ‘weak ties’ that once defined a web of opportunities are replaced by an assumed toxicity of ties. We conclude by reflecting on the nostalgia for the Twitter-that-was, suggesting the need to foster a critical and reflective relationship with the Twitter of old.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it