EXPRESS: Extremity Bias in Online Reviews: The Role of Attrition

In a range of studies across platforms, online ratings have been shown to be characterized by distributions with disproportionately-heavy tails. We focus on understanding the underlying process that yields such “j-shaped” or “extreme” distributions. We propose a novel theoretical mechanism behind the emergence of “j-shaped” distributions: differential attrition, or the idea that potential reviewers with moderate experiences are more likely to leave the pool of active reviewers than potential reviewers with extreme experiences. We present an analytical model that integrates this mechanism with two extant mechanisms: differential utility and base rates. We show that while all three mechanisms can give rise to extreme distributions, only the utility-based and the attrition-based mechanisms can explain our empirical observation from a large-scale field experiment that an unincentivized solicitation email from an online travel platform reduces review extremity. Subsequent analyses provide clear empirical evidence for the existence of both differential attrition and differential utility.

Solving the problem of hydrometeorological data analysis by random process

The paper reviews the hydrometeorological data analysis (precipitation, flow, water level, etc.) to evaluate and predict mutations such as flood, drought and saline extremes to reduce the impact of climate change on the economy and life. The main method for solving the problem posed is the max-domain of attraction of extreme distributions with the Gumbel copula of random variables related to hydrometeorological data. Results presented in this paper are reviewed and verified through data supplied by hydrometeorological stations at the Tan Chau and Chau Doc districts, An Giang province from 1990 to present