Bursts of online social disapproval: leveraging analytics for comprehension and detection
Bibliographic record
Abstract
Purpose This paper theorizes online social disapproval (OSD) as a distinct, multilevel phenomenon that can rapidly escalate into bursts of public responses with significant reputational and financial consequences. This study aims to conceptualize OSD bursts, distinguish them from traditional crises and develop an analytics-based toolkit to guide organizations in detecting and managing them. Design/methodology/approach Drawing on literature in organizational crisis management and social media analytics, this study uses a multilevel lens to theorize micro–macro linkages in OSD. It develops a four-phase framework – preburst, initial burst, spreading and contagion and recalibration – and proposes a managerial toolkit that specifies analytics objectives, guiding questions and indicators for each phase. Findings This study identifies OSD bursts as sudden, cross-platform escalations triggered by microlevel criticism that diffuses through digital networks. Unlike stage-based crisis models, OSD bursts are diverse in origin, erratic in development and resistant to full resolution. This framework highlights how analytics could mediate managerial sensemaking by expanding attention, shaping interpretation and constraining response options. The toolkit provides managers with methods to detect early warning signals, quantify burst severity and assess long-term reputational impacts. Originality/value This paper advances scholarship by conceptualizing OSD as a multilevel process that challenges conventional crisis management paradigms. It introduces an analytics-driven managerial toolkit that positions analytics as sociotechnical mediators of organizational sensemaking. For practitioners, it provides actionable guidance on detecting, interpreting and managing OSD bursts, enabling organizations to adapt to the “new normal” of digital disapproval.
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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 | 0.000 | 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 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".