Using Speech Acts to Elicit Positive Emotions for Complainants on Social Media
Why this work is in the frame
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Bibliographic record
Abstract
A carefully tailored tone in response to a complaint on social media can create positive emotions for an upset customer. However, very few studies have identified what response tones, based on an established theory, would be most effective for complaint management. This study conceptualizes a service agent's response tones based on Ballmer and Brennenstuhl's (1981) classification of speech acts and examines how an agent's use of speech acts elicit positive emotions for the complainant. Ballmer and Brennenstuhl classify speech acts within the dimensions of conventionality and dialogicality, and they suggest the two dimensions interact. Thus, we examine the impact of each dimension of speech acts and the interactions between the two dimensions on the elicitation of positive emotions for complainants. We collected over 100,000 tweets and classified firm agents’ speech acts and complainants’ emotions by designing deep learning architectures (i.e., bi-directional recurrent neural networks). Our fixed-effect regression results show that a low level of each speech act leads to the elicitation of customers’ positive emotions but that the combination of the two erodes the individual advantages. This study expands Ballmer and Brennenstuhl's (1981) speech act classification from a speaker's perspectives to a listener's perspectives by contextualizing it in an analysis of service agents’ tones and their roles in eliciting positive emotions among complainants.
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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 | 0.001 | 0.002 |
| 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