Echo Tunnels: Polarized News Sharing Online Runs Narrow but Deep
Bibliographic record
Abstract
Online social platforms afford users vast digital spaces to share and discuss current events. However, scholars have concerns both over their role in segregating information exchange into ideological echo chambers, and over evidence that these echo chambers are nonetheless over-stated. In this work, we investigate news-sharing patterns across the entirety of Reddit and find that the platform appears polarized macroscopically, especially in politically right-leaning spaces. On closer examination, however, we observe that the majority of this effect originates from small, hyper-partisan segments of the platform accounting for a minority of news shared. We further map the temporal evolution of polarized news sharing and uncover evidence that, in addition to having grown drastically over time, polarization in hyper-partisan communities also began much earlier than 2016 and is resistant to Reddit's largest moderation event. Our results therefore suggest that social polarized news sharing runs narrow but deep online. Rather than being guided by the general prevalence or absence of echo chambers, we argue that platform policies are better served by measuring and targeting the communities in which ideological segregation is strongest.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".