ABSTRACT Argumentation, the act of defending one's inherent knowledge or views through speech expression, is a kind of widespread information expression and communication behavior. In this poster, we aim to explore the characteristics and patterns of argumentation in social media to examine basic rules the patterns follow by conducting content analysis of the transcript of a WeChat Group Chat. We build a theoretical model of argumentation behavior in mobile social media using the inductive coding and find that social media has a great influence on argumentation.
In this paper, a method for modeling distance dependent head-related transfer functions is presented. The HRTFs are first decomposed by spatial principal component analysis. Using deep neural networks, we model the spatial principal component weights of different distances. Then we realize the prediction of HRTFs in arbitrary spatial distances. The objective and subjective experiments are conducted to evaluate the proposed distance model and the distance variation function model, and the results have shown that the proposed model has less spectral distortions than distance variation function model, and the virtual sound generated by the proposed model has better performance in terms of distance localization.