Is Oprah Contagious? Identifying Demand Spillovers in Product Networks
Bibliographic record
Abstract
We study the online contagion of exogenous demand shocks generated by book reviews featured on the Oprah Winfrey TV show and published in the New York Times, through the co-purchase recommendation network on Amazon.com. These exogenous events may ripple through and affect the demand for a 'network' of related books that were not explicitly mentioned in a review but were located 'close' to reviewed books in this network. Using a difference-in-differences matched-sample approach, we identify the extent of the variations caused by the visibility of the online network and distinguish this effect from variation caused by hidden product complementarities. Our results show that the demand shock diffuses to books that are upto five links away from the reviewed book, and that this diffused shock persists for a substantial number of days, although the depth and the magnitude of diffusion varies widely across books at the same network distance from the focal product. We then analyze how product characteristics, assortative mixing and local network structure, play a role in explaining this variation in the depth and persistence of the contagion. Specifically, more clustered local networks 'trap' the diffused demand shocks and cause it to be more intense and of a greater duration but restrict the distance of its spread, while less clustered networks lead to wider contagion of a lower magnitude and duration. Our results provide new evidence of the interplay between a firm's online and offline media strategies and we contribute methods for modeling and analyzing contagion in networks.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".