  The impact of non verbal behaviour in a hiring decision remains an open
question. Investigating this question is important, as it could provide a
better understanding on how to train candidates for job interviews and make
recruiters be aware of influential non verbal behaviour. This research has
recently been accelerated due to the development of tools for the automatic
analysis of social signals, and the emergence of machine learning methods.
However, these studies are still mainly based on hand engineered features,
which imposes a limit to the discovery of influential social signals. On the
other side, deep learning methods are a promising tool to discover complex
patterns without the necessity of feature engineering. In this paper, we focus
on studying influential non verbal social signals in asynchronous job video
interviews that are discovered by deep learning methods. We use a previously
published deep learning system that aims at inferring the hirability of a
candidate with regard to a sequence of interview questions. One particularity
of this system is the use of attention mechanisms, which aim at identifying the
relevant parts of an answer. Thus, information at a fine-grained temporal level
could be extracted using global (at the interview level) annotations on
hirability. While most of the deep learning systems use attention mechanisms to
offer a quick visualization of slices when a rise of attention occurs, we
perform an in-depth analysis to understand what happens during these moments.
First, we propose a methodology to automatically extract slices where there is
a rise of attention (attention slices). Second, we study the content of
attention slices by comparing them with randomly sampled slices. Finally, we
show that they bear significantly more information for hirability than randomly
sampled slices.
