  We present a framework to identify whether a public speaker's body movements
are meaningful or non-meaningful ("Mannerisms") in the context of their
speeches. In a dataset of 84 public speaking videos from 28 individuals, we
extract 314 unique body movement patterns (e.g. pacing, gesturing, shifting
body weights, etc.). Online workers and the speakers themselves annotated the
meaningfulness of the patterns. We extracted five types of features from the
audio-video recordings: disfluency, prosody, body movements, facial, and
lexical. We use linear classifiers to predict the annotations with AUC up to
0.82. Analysis of the classifier weights reveals that it puts larger weights on
the lexical features while predicting self-annotations. Contrastingly, it puts
a larger weight on prosody features while predicting audience annotations. This
analysis might provide subtle hint that public speakers tend to focus more on
the verbal features while evaluating self-performances. The audience, on the
other hand, tends to focus more on the non-verbal aspects of the speech. The
dataset and code associated with this work has been released for peer review
and further analysis.
