Introducing technological disruption: how breaking media attention on corporate events impacts online sentiment
Bibliographic record
Abstract
One modern strategy to anticipate consumer reaction to new products and services involves looking towards social media sites to explore consumer opinions. A rich body of literature on social media marketing suggests that an effective way to leverage social media platforms is the empirical analysis of electronic word-of-mouth (eWOM), particularly through sentiment analysis (SA). We propose a novel method for innovators to leverage social media by exploring how breaking media attention on notable corporate events impacts the general public sentiment surrounding a pre-introduced, potentially disruptive innovation (PPDI). Twitter conversations surrounding Facebook’s pre-introduced payment system called Libra, a permissioned blockchain-based cryptocurrency, were analysed as a case study. The analysis suggests that breaking media attention leads to a significant change in sentiment polarity. An event with a preannouncement leads to an emotional momentum effect whereby sentiment polarity accumulates across an anticipation period. Implications for how managers may leverage these insights are discussed.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".