Government's Social Media: A Study of Twitter Use and Network among Seven Nations
Bibliographic record
Abstract
이 연구는 트위터 계정을 가지고 있는 7개 국가 175개 정부부처의 트위터 이용 및 연결망을 분석했다. 분석 결과 트위터 이용 특징 대부분에서 국가 별 뚜렷한 차이를 찾기 힘들었으나 맞팔율에서는 국가 간 극명한 차이가 있었다. 미국이 다른 국가로부터 팔로잉을 가장 많이 받는 국가인 반면, 다른 국가를 팔로잉 하는 데는 소극적인 것으로 나타났다. 또 국가 간 연결관계에서 같은 언어나 문화적, 역사적 유사성이 어느 정도 영향을 주고 있음을 알 수 있었다. 그밖에 비슷한 업무를 담당하는 정부부처 간 연결의 경우가 많았다. 이 연구는 트위터 연결망을 가시화함으로 해서 미국이 비공식채널에서도 주목을 받고 있음을 확인하는 동시에, 언어 및 업무 유사성에 의한 연결관계도 확인함으로 해서 향후 소셜 미디어에서의 연성 권력 형성을 통한 새로운 지식 패권 구도가 출현할 수 있는 잠재성을 제시한다. The present study analyzes a Twitter network of some 175 government organizations belonging to seven countries. They are South Korea, U.S., U.K., Australia, Canada, Singapore, and Japan. The results showed that the U.S. occupied the most central position in terms of the incoming followings. Next, some clusters among countries were also noticeable according to their cultural proximities including national languages. The findings also indicate that some governmental organizations are likely to make international ties with others whose main duties are similar to each other. Finally, the structural connectivity pattern of some inter-governmental Twitter networks was visualized using a social network software. On the other hand, it suggests that there is a potential for a soft power through social media and as a result, it could be assumed that a new knowledge hegemony appears in the future.
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How this classification was reachedexpand
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".