After Twitter: Fragmentation, Platform Polities and Protective Sociality
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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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.033 |
| Scholarly communication | 0.012 | 0.019 |
| Open science | 0.001 | 0.012 |
| 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".