Introducing New Disruption in Uncertain Market Environments: How Breaking Media Attention on Corporate Events Impacts Online General Sentiment
Bibliographic record
Abstract
This thesis proposes a novel methodology for exploring how breaking media attention on notable corporate events impacts the public sentiment surrounding a pre-introduced, potentially disruptive innovation (PPDI) in the form of online discourse. Additionally, how online sentiment changes over time is also explored. The focus on exploration enables for insight into how digital innovators may leverage social media and media attention to assist with reducing market uncertainty and plan future online promotional activities, the new product development process (NPD) and product launch. Twitter conversations surrounding the pre-introduced payment system called Diem (formerly known as Libra), a permissioned blockchain-based payment system and cryptocurrency, were analyzed. Sentiment analysis (SA) was applied to examine online breaking media attention and coverage impacts. The results suggest that breaking media attention elicits a significant change in online sentiment. Moreover, an event with a preannouncement observes an emotional momentum effect whereby sentiment accumulates across an anticipation period.
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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 | 0.001 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".