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

  • Zhenyue Qin
  • Tom GedeonEmail author
  • Sabrina Caldwell
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11306)


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 of 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.


Emotion veracity Crowd prediction Neural network Fake news 



The authors are grateful to Aaron Manson for access to the data, and particularly acknowledge his efforts in data collection.


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Research School of Computer ScienceAustralian National UniversityCanberraAustralia

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