Research on the Impact of Social Networks on News Spread
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 selected topic of this paper is to use the theories of social networks to explain how the news spread among people and how the digital technologies have changed the ways for the news to spread. This topic is selected when it is meaningful and valuable as the findings will be beneficial for studying interpersonal relations and psychological well-being, political participation and civic engagement This paper tries to conduct a preliminary research to understand this topic, and it finds out that social network could promote the dissemination of news when people are connected in a social network and they exchange messages and news. The results show that online social network will promote the spread of fake news, which will cause big negative impacts on the society. Therefore, the arguments relevant to the selected topic have been surrounding the negative and positive roles of social network for social learning and the spread of news. This paper also calls for actions from individuals to act as moral polices, and stop the spreading of fake news, and promote healthy social learning, when individuals are the gatekeepers for fake news.
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 | 0.013 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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 it