Segmenting internet users using emotions
Bibliographic record
Abstract
Marketers have for several years dealt with consumer heterogeneity by segmenting the market to identify and address different consumer clusters. Yet, albeit the global nature of the internet, it is surprising that only a small number of studies (e.g., Barnes et al. 2007; Brengman et al. 2005; Shiu and Dawson 2002) identified their segmentation typologies using data from more than one country. In addition, whilst the role of emotions in consumer decision making is well-documented in the marketing literature (e.g., Han, Lerner and Keltner 2007; Kwortnik and Ross 2007), there is no consumer typology based on online users’ feelings. This study comes to address these gaps by collecting data from online users within four countries, namely the UK, USA, Australia and Canada to construct typologies based on consumer emotions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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; both teacher heads agree on what is shown here.
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".