The Role of Emotional Expression in Accessing Social Networks: The Case of Newcomers' Blogs
Bibliographic record
Abstract
Research has established the critical role of social networks in facilitating adjustment to foreign environments. Increasingly, social interactions are happening through computer mediated technology. This paper explores the role of emotional expression in newcomers’ blogs in developing and interacting with social networks in a new country. This research uses a dictionary-based text analytics approach to detect emotional expression in newcomers’ blog posts and their associated discussions. Blog posts with more emotional expression had more associated responses; discussions tended to be more positive than posts; and the relative amount of negative emotion in the discussions increases as posts become more negative. Results suggest that expression of emotion in blogs can facilitate access to social networks and increase engagement in online communities by increasing the amount of responses and triggering congruent emotional response from blog readers, which is a precursor to affiliation and understanding. The findings in this paper highlight the role of emotional expression in blog posts and discussions, and its connection to developing social networks and engaging in online communities which has the potential to facilitate access to social support.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".