Echo Chambers and Algorithmic Bias: The Homogenization of Online Culture in a Smart Society
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
The rise of smart societies, characterized by extensive use of technology and data-driven algorithms, promises to improve our lives. However, this very technology presents a potential threat to the richness and diversity of online culture. This thesis explores the phenomenon of echo chambers and algorithmic bias, examining how they contribute to the homogenization of online experiences. Social media algorithms personalize content feeds, presenting users with information that reinforces their existing beliefs. This creates echo chambers, where users are isolated from diverse viewpoints. Algorithmic bias, stemming from the data used to train these algorithms, can further exacerbate this issue. The main data in this study were sourced from previous studies (secondary data) which focused on research related homogenizing on online culture. The thesis investigates the impact of echo chambers and algorithmic bias on online culture within smart societies. It explores how these factors limit exposure to a variety of ideas and perspectives, potentially leading to a homogenized online experience. By examining the interplay between echo chambers, algorithmic bias, and the homogenization of online culture in smart societies, this thesis aims to contribute to a more nuanced understanding of the impact of technology on our online experiences.
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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 | 0.000 | 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 it