Discovering Inconsistency in Multimedia News Based on a Material-Opinion Model

The advantages of the multimedia make the video news presented believable and impressed to the viewers when the personal opinions and ideological perspectives hidden in the contents still cause the effect. To reduce the risk of the misleading, based on a Material-Opinion model, we propose a method of detecting the inconsistent news items reporting the same event when the viewer is watching one of them. In the Material-Opinion Model, main participants filmed as the materials are presented to the viewer through the video stream, which is used to support the arguments put forward. Based on this model, given a series of multimedia news items reporting a same event, we explore inconsistency between any two of them by computing their dissimilarities of materials and of opinions. Material-dissimilarity is based on the appearance of the main participants in the video. Opinion-dissimilarity is calculated as the vector difference of two vectors consisting of the argument points extracted from the closed captions. If one of the dissimilarities is high and the other is low, we consider that there exists the inconsistency as a result. We also show some experimental results to validate the proposed methods.