Echo Chambers and Algorithmic Bias: The Homogenization of Online Culture in a Smart Society
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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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".