Is there anything Left?: A Global Analysis on Changes in Engagement with Political Content on Twitter in the Musk Era
Bibliographic record
Abstract
Over the past few years, Twitter (now X) has become an influential platform for political discourse. However, prior research suggests that Twitter may be biased towards right-wing content. Following the change in ownership in October 2022, there have been several changes to Twitter’s policies, particularly in content flagging and Twitter Blue Verification. Understanding how any shifts in outcomes vary across different political ideologies is important for comprehending the evolving political discourse, especially given recent developments. To explore this issue, we examine shifts in engagement (characterized by likes and retweets) for political figures before and after November 2022, focusing on describing how engagement has changed over time. We perform a global analysis by collecting tweets from 6550 accounts belonging to political leaders and parties from twelve countries among the ones with the highest user activity on the platform, namely, Argentina, Brazil,Canada, Colombia, France, Germany, India, Japan, Mexico, Spain, the United Kingdom, and the United States, between June 2021 and June 2023. Our findings indicate that the number of likes on political tweets increased after November 2022. However, we observe that the number of retweets decreased significantly, along with a marginal decrease in the likes-to-retweet ratio, with no statistically significant difference between the Left and the Right. Our study is the first to offera global perspective by examining how platform engagement has shifted during the Musk Era. To support further research, we release the data on politicians and parties used in this study, with their Twitter data available upon request.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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 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".