A Community of Curious Souls: An Analysis of Commenting Behavior on TED Talks Videos
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 TED (Technology, Entertainment, Design) Talks website hosts video recordings of various experts, celebrities, academics, and others who discuss their topics of expertise. Funded by advertising and members but provided free online, TED Talks have been viewed over a billion times and are a science communication phenomenon. Although the organization has been derided for its populist slant and emphasis on entertainment value, no previous research has assessed audience reactions in order to determine the degree to which presenter characteristics and platform affect the reception of a video. This article addresses this issue via a content analysis of comments left on both the TED website and the YouTube platform (on which TED Talks videos are also posted). It was found that commenters were more likely to discuss the characteristics of a presenter on YouTube, whereas commenters tended to engage with the talk content on the TED website. In addition, people tended to be more emotional when the speaker was a woman (by leaving comments that were either positive or negative). The results can inform future efforts to popularize science amongst the public, as well as to provide insights for those looking to disseminate information via Internet videos.
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.
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 | 0.001 | 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) | 0.001 | 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 it