  Accessing people's personality traits has always been a challenging task. On
the other hand, acquiring personality traits based on behavioral data is one of
the growing interest of human beings. Numerous researches showed that people
spend a large amount of time on social networks and show behaviors that create
some personality patterns in cyberspace. One of these social networks that have
been widely welcomed in some countries, including Iran, is Telegram. The basis
of this research is automatically identifying users' personalities based on
their behavior on Telegram. For this purpose, messages from Telegram group
users are extracted, and then the personality traits of each member according
to the NEO Personality Inventory are identified. For personality analysis, the
study is employed three approaches, including; Cosine Similarity, Bayes, and
MLP algorithms. Finally, this study provides a recommender system that uses the
Cosine similarity algorithm to explore and recommend relevant Telegram channels
to members according to the extracted personalities. The results show a 65.42%
satisfaction rate for the recommender system based on the proposed personality
analysis.

Social and Information Networks;Computers and Society