Adopting, Networking, and Communicating on Twitter
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
Twitter is one of the most popular online social network platforms for political communication. This study explains how political candidates in five countries increase their online popularity and visibility by their behavior on Twitter. Also, the study focuses on cultural differences in online social relations by comparing political candidates in five countries in the East and West: South Korea, Japan, United Kingdom, Canada, and the Netherlands. Findings show that signing up to Twitter as early as possible increases one’s online popularity as predicted by the process of preferential attachment. Candidates actively following citizens and sending undirected tweets also increases the group of followers. This doesn’t apply however to conversational tweets, which decreases the number of a candidate’s followers slightly. South Korea, having a collectivistic culture, shows higher levels of reciprocity on Twitter, although this does not increase the group of followers. In other countries, including collectivistic Japan, candidates reciprocate less frequently with citizens, effectively using Twitter more as a mass medium for broadcasting.
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 | 0.002 | 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 | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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