The Impact of Twitter Users' Characteristics on Behaviors
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
Researchers have focused on leadership, often overlooking followership. The notion of followership was irreversibly transformed with the advent and societal adoption of followership systems, such as Twitter. To examine such emergent systems, this paper advances a distinct form of followership: eFollowership. To understand Twitter and its users, the eFollowership concept is explicated and synthesized by adapting several followership lenses from the literature. The authors empirically examined eFollowership by assessing the roles constructed by 301 Twitter users and the relationships between these users' role-based characteristics and behaviors with partial least squares structural equation modeling (PLS-SEM). Results showed that users' voicing and empowering behaviors were significantly influenced by users' characteristics: personal sense of power, eCourage, and social capital. Users' helping behaviors were related to users' personal sense of power and social capital, but not to eCourage. Surprisingly, users' disempowering behaviors were unrelated to all three users' characteristics.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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 it