  We conducted a data collection on the basis of the Google AudioSet database
by selecting a subset of the samples annotated with \textit{laughter}. The
selection criterion was to be present a communicative act with clear
connotation of being either positive (laughing with) or negative (being laughed
at). On the basis of this annotated data, we performed two experiments: on the
one hand, we manually extract and analyze phonetic features. On the other hand,
we conduct several machine learning experiments by systematically combining
several automatically extracted acoustic feature sets with machine learning
algorithms. This shows that the best performing models can achieve and
unweighted average recall of .7.
