Neural Networks Assist Crowd Predictions in Discerning the Veracity of Emotional Expressions

TL;DR

This paper demonstrates that neural networks combined with crowd predictions significantly improve the accuracy of detecting the truthfulness of emotional expressions, surpassing individual performance and enabling transfer learning across emotions.

Contribution

It introduces a novel approach of aggregating crowd predictions with neural networks, achieving high accuracy in emotion veracity detection and enabling transfer learning across emotion types.

Findings

Crowd discernment increases accuracy from 63% to 80%.

Neural networks reach 99.69% accuracy by aggregating answers.

High accuracy achieved with only 30 participants.

Abstract

Crowd predictions have demonstrated powerful performance in predicting future events. We aim to understand crowd prediction efficacy in ascertaining the veracity of human emotional expressions. We discover that collective discernment can increase the accuracy of detecting emotion veracity from 63%, which is the average individual performance, to 80%. Constraining data to best performers can further increase the result up to 92%. Neural networks can achieve an accuracy to 99.69% by aggregating participants' answers. That is, assigning positive and negative weights to high and low human predictors, respectively. Furthermore, neural networks that are trained with one emotion data can also produce high accuracies on discerning the veracity of other emotion types: our crowdsourced transfer of emotion learning is novel. We find that our neural networks do not require a large number of participants, particularly, 30 randomly selected, to achieve high accuracy predictions, better than any individual participant. Our proposed method of assembling peoples' predictions with neural networks can provide insights for applications such as fake news prevention and lie detection.