News Personalization: Do Journalism Audiences Prefer Algorithms Over Editors?
Bibliographic record
Abstract
<p>This project-paper explores what motivates news organizations to employ algorithmically driven news personalization techniques and develops a methodology to determine whether journalism audiences have a preference between story lineups determined by an editor or by an algorithm. This work also examines whether news personalization systems meet the information needs of news audiences and works to determine what algorithmic approaches could better meet these needs.</p> <p>Through the creation of a simple online news personalization system, this project has developed a method driven by analytic measurement coupled with a survey approach to determine audience opinions on news recommendation systems. A small user study was conducted that supported the feasibility of the system as a research tool and identified possible improvements to my methodological choices.</p> <p>The research presented as part of this project-paper found that news organizations use news personalization systems for a variety of economic and editorial reasons. This paper also explores the social impacts of news personalization techniques and posits that there is nothing inherent to the design of personalization systems that precludes supporting the democratic and social ideals of journalism.</p>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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 | 0.003 | 0.000 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".