  Nowadays, social networks play a crucial role in human everyday life and no
longer purely associated with spare time spending. In fact, instant
communication with friends and colleagues has become an essential component of
our daily interaction giving a raise of multiple new social network types
emergence. By participating in such networks, individuals generate a multitude
of data points that describe their activities from different perspectives and,
for example, can be further used for applications such as personalized
recommendation or user profiling. However, the impact of the different social
media networks on machine learning model performance has not been studied
comprehensively yet. Particularly, the literature on modeling multi-modal data
from multiple social networks is relatively sparse, which had inspired us to
take a deeper dive into the topic in this preliminary study. Specifically, in
this work, we will study the performance of different machine learning models
when being learned on multi-modal data from different social networks. Our
initial experimental results reveal that social network choice impacts the
performance and the proper selection of data source is crucial.
