Bibliographic record
Abstract
Purpose – The purpose of this paper is to specify the role of emotions played in information seeking and sharing taking place in online discussion forum. To this end, an explorative study was made that focussed on consumer awareness. Design/methodology/approach – The study is based on the analysis of a sample of 30 discussion threads containing altogether 1,630 messages available in Canadian Content – a major online platform. The expression of emotions was examined by using the categories of the interaction process analysis (IPA) model. Two research questions were addressed: first, what kind of emotions are expressed in the four functional areas of the IPA model when discussing online about consumer awareness? and second, what is the role of positive and negative emotions in information seeking and sharing about the above topic? The data were analyzed by means of descriptive statistics and qualitative content analysis. Findings – Of the emotional expressions, 42 percent were positive and 58 percent negative. The most frequent emotions were amusement, contempt, worry, irritation and pleasure. The frequencies of positive and emotional expressions varied in the context of 12 IPA categories. Positive emotions predominated when participants showed solidarity or agreed, while negative emotions were particularly prevalent when indicating antagonism. The repertoire of positive and negative emotions was broadest while providing opinions or sharing information with others. In contrast, emotions were expressed rarely in the context of information seeking. Research limitations/implications – The study is explorative in nature and the findings are based on the examination of an online discussion group focussed on the issues of consumer awareness. Originality/value – The study contributes to the study of affective factors in computer-mediated interaction by empirically specifying the repertoire of positive and negative emotions expressed in online discussion.
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.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".