The Impact of Twitter Users' Characteristics on Behaviors
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.
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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 | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".