Prediction of public trust in politicians using a multimodal fusion approach

This paper explores the automatic prediction of public trust in politicians through the use of speech, text, and visual modalities. It evaluates the effectiveness of each modality individually, and it investigates fusion approaches for integrating information from each modality for prediction using a multimodal setting. A database was created consisting of speech recordings, twitter messages, and images representing fifteen American politicians, and labeling was carried out per a publicly available ranking system. The data were distributed into three trust categories, i.e., the low-trust category, mid-trust category, and high-trust category. First, unimodal prediction using each of the three modalities individually was performed using the database; then, using the outputs of the unimodal predictions, a multimodal prediction was later performed. Unimodal prediction was performed by training three independent logistic regression (LR) classifiers, one each for speech, text, and images. The prediction vectors from the individual modalities were then concatenated before being used to train a multimodal decision-making LR classifier. We report that the best performing modality was speech, which achieved a classification accuracy of 92.81%, followed by the images, achieving an accuracy of 77.96%, whereas the best performing model for text-modality achieved a 72.26% accuracy. With the multimodal approach, the highest classification accuracy of 97.53% was obtained when all three modalities were used for trust prediction. Meanwhile, in a bimodal setup, the best performing combination was that combining the speech and image visual modalities by achieving an accuracy of 95.07%, followed by the speech and text combination, showing an accuracy of 94.40%, whereas the text and images visual modal combination resulted in an accuracy of 83.20%.