The innovative technologies and advanced services typical of the so
called Web 2.0 have led to the development of many online social networking
Web sites where users can collaborate and share information, interests,
personal and professional activities. These sites integrate new technologies
and services, which allow users to participate to the Web not only as consumers
of the contents centrally uploaded by the providers, but also as producers
of contents and consumers of contents provided by other users.
Hence, the workload of these sites is the result of the complex social
relationships that can be developed among users.
This research project is aimed at studying and characterizing the workload of social
networking sites in terms of the characteristics and behavior of the users
and the communities of users sharing common interests. The objective is to
derive models that describe the user behavior and the interactions between
the users and the services offered within the sites.
In particular, the Big Data stored by online platforms for emotional support offering free,
anonymous, and confidential conversations with live listeners are
This study explores the utilization and the interaction features of
hundreds of thousands of users. It dissects the user's activity
levels, the patterns by which they engage in conversation with each
other, and uses machine learning methods to find factors promoting
Graph analysis techniques are applied to describe community structure
and evolution, and characteristics such as the emergence of a
rich-get-richer phenomenon in the development of the network.