Anchoring Bias in Online Voting

TL;DR

This paper investigates anchoring bias in online voting, revealing that individuals tend to give ratings similar to their previous ratings, with bias strength decreasing logarithmically with more votes, impacting system design.

Contribution

It uncovers and quantifies the systematic anchoring bias in online voting, validated through real system analysis and null model comparisons.

Findings

Bias decays logarithmically with number of votes

People tend to give similar ratings to their previous ones

Findings can inform recommender system design

Abstract

Voting online with explicit ratings could largely reflect people's preferences and objects' qualities, but ratings are always irrational, because they may be affected by many unpredictable factors like mood, weather, as well as other people's votes. By analyzing two real systems, this paper reveals a systematic bias embedding in the individual decision-making processes, namely people tend to give a low rating after a low rating, as well as a high rating following a high rating. This so-called \emph{anchoring bias} is validated via extensive comparisons with null models, and numerically speaking, the extent of bias decays with interval voting number in a logarithmic form. Our findings could be applied in the design of recommender systems and considered as important complementary materials to previous knowledge about anchoring effects on financial trades, performance judgements, auctions, and so on.