“This Candle Has No Smell”: Detecting the Effect of COVID Anosmia on Amazon Reviews Using Bayesian Vector Autoregression
Keywords:Measuring predictability of real world phenomena based on social media, e.g., spanning politics, finance, and health, Trend identification and tracking; time series forecasting, Subjectivity in textual data; sentiment analysis; polarity/opinion identification and extraction, linguistic analyses of social media behavior, Qualitative and quantitative studies of social media
AbstractWhile there have been many efforts to monitor or predict Covid using digital traces such as social media, one of the most distinctive and diagnostically important symptoms of Covid -- anosmia, or loss of smell -- remains elusive due to the infrequency of discussions of smell online. It was recently hypothesized that an inadvertent indicator of this key symptom may be misplaced complaints in Amazon reviews that scented products such as candles have no smell. This paper presents a novel Bayesian vector autoregression model developed to test this hypothesis, finding that "no smell" reviews do indeed reflect changes in US Covid cases even when controlling for the seasonality of those reviews. A series of robustness checks suggests that this effect is also seen in perfume reviews, but did not hold for the flu prior to Covid. These results suggest that inadvertent digital traces may be an important tool for tracking epidemics.
How to Cite
Beauchamp, N. (2022). “This Candle Has No Smell”: Detecting the Effect of COVID Anosmia on Amazon Reviews Using Bayesian Vector Autoregression. Proceedings of the International AAAI Conference on Web and Social Media, 16(1), 1363-1367. Retrieved from https://ojs.aaai.org/index.php/ICWSM/article/view/19388