Exploring Echo Chambers in Twitter during Two Spanish Regional Elections: An Analysis of Community Interactions
Why this work is in the frame
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Bibliographic record
Abstract
Abstract The integration of digital technology in modern society has led to an increased importance of the analysis of the digital environment in political elections. The concept of echo chambers and their influence on social networks has received significant attention in recent academic investigations. Echo chambers are commonly referred to as the digital bubble where users participate in a conversation mostly with like-minded others, and it is usually related not only to homophily but can also be directly associated with the effects of social media algorithms. This study examines the Twitter interactions during two Spanish regional elections. Data collection has been performed through Twitter Streaming API, which resulted in a total dataset of 5.5 million tweets. The study analyzes how the political communities interact inside and between them. Also, we replicate this analysis by grouping the political communities by two main affinity blocks (left-right) to evaluate if the effects of homophily are even higher under this hypothesis. Finally, the text of the tweets was analyzed to reinforce the community-interaction analysis and to conduct a sentiment analysis of the interactions. The research results indicate that within each political party community, interactions predominantly occur among individuals who hold similar political views, leading to the creation of echo chambers. These echo chambers become even more powerful when parties are unified into political affinity blocks, with over 97% of interactions occurring within each left-right block. This study aims to contribute to the ongoing academic debate by providing relevant data and reinforcing aspects studied by previous researchers.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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