Neural Networks Assist Crowd Predictions in Discerning the Veracity of Emotional Expressions
This provides a method for applications like fake news prevention and lie detection, but it is incremental as it builds on existing crowd prediction and neural network techniques.
The study tackled the problem of discerning the veracity of emotional expressions using crowd predictions, achieving an accuracy of 99.69% with neural networks that aggregate participants' answers, compared to 80% from collective discernment and 63% from individual performance.
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.