  The increase in the prevalence of mental health problems has coincided with a
growing popularity of health related social networking sites. Regardless of
their therapeutic potential, On-line Support Groups (OSGs) can also have
negative effects on patients. In this work we propose a novel methodology to
automatically verify the presence of therapeutic factors in social networking
websites by using Natural Language Processing (NLP) techniques. The methodology
is evaluated on On-line asynchronous multi-party conversations collected from
an OSG and Twitter. The results of the analysis indicate that therapeutic
factors occur more frequently in OSG conversations than in Twitter
conversations. Moreover, the analysis of OSG conversations reveals that the
users of that platform are supportive, and interactions are likely to lead to
the improvement of their emotional state. We believe that our method provides a
stepping stone towards automatic analysis of emotional states of users of
online platforms. Possible applications of the method include provision of
guidelines that highlight potential implications of using such platforms on
users' mental health, and/or support in the analysis of their impact on
specific individuals.
