Propagation Phenomena in Social Media
Bibliographic record
Abstract
Social media and blogging services have become extremely popular. Every day hundreds of millions of users share random thoughts, gossip, news, and thoughts on notable social issues. Users interact by following each other’s updates and passing along interesting pieces of information to their friends. Information therefore can diffuse widely and quickly through social links. Information propagation in networks such as Twitter and Facebook is unique, in that traditional media sources and word-of-mouth propagation coexist. The availability of digitally logged propagation events in social media helps one better understand how a wide range of factors that are essential in communication, such as user influence, tie strength, repeated exposures, mass media, and agenda setting, come into play in the way people generate and consume information in modern society. This chapter reviews the roles different types of users of social media play in information propagation as well as the resulting propagation patterns. It also discusses specific examples, including the spread of social conventions and identifying topic experts in social media, in an effort to bring about better understanding of the characteristics of propagation phenomena in large social networks.
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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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".