Similarities of Influencers across Different Social Media Platforms by Using Four Centrality Measures
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
Searching for influencers among a social network is important because marketers can then use this information to conduct word-of-mouth (WOM) advertisement, which is an important marketing technique. Literature Review provides detailed information about WOM advertisement. There are many ways to search influencers and often they are network centrality measurements. This paper aims to investigate whether each centrality measurement could produce similar results across different social media platforms (eg. Facebook, Twitter, Instagram). The social network data used in this research is from Huawei Company. This research uses four centrality measurements and three set similarity methods to analysis the data. As a result, this paper draws a conclusion about the binary question "Does it provide similar results or not?". Since various companies and applications may have different standards and definitions about being similar, please also check similarity data provided in this paper.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.012 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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