Reading between the lines: Automatic inference of self-assessed personality traits from dyadic social chats

Abstract

Interaction through text-based platforms (e.g., WhatsApp) is a common everyday activity, typically referred to as “chatting”. However, the computing community paid relatively little attention to the automatic analysis of social and psycho-logical phenomena taking place during chats. This article proposes experiments aimed at the automatic inference of self-assessed personality traits from data collected during online dyadic chats. The proposed approach is multimodal and takes into account the two main components of chat-based interactions, namely what people type (the text) and how they type it (the keystroke dynamics). To the best of our knowledge, this is one of the very first works that includes keystroke dynamics in an approach for the inference of personality traits. The experiments involved 60 people and the results suggest that it is possible to recognize whether someone is below median or not along the Big-Five traits. Such a result suggests that personality leaves traces in both what people type it and how they type it, the two types of information the approach takes into account.