Characterizing user behavior in online social networks: Analysis of the regular use of Facebook

The analysis of user behaviour in online social networks (OSNs) is one of the important research interests related to human-computer interactions. OSNs gives a large space to share news with no limits around the world and allows user to benefit from properties of this interactive and dynamic system. The study of user behaviour on a social and popular platform characterized by the use of new technologies requires to understand and the analysis of collective behaviour on Facebook. This paper aims to analyse the usage patterns in OSNs using the visible interactions of Facebook, by studying the time of activity and the evolution of human behaviour through a process of detection of visible and non-volatile interactions. In the first step, we perform a data collection process based on breadth first search algorithm (BFS) and semi-supervised crawler agent. In the second step, we build an interaction quantification process to measure users’ activities and analysis related time series. The study of the frequency of periodic use has shown that the communities monitored follow a weekly rhythm that decreases over time to reach a frequency of daily use, which reflects a stability of activities and a case of dependency of use

Gender-Based Analysis of User Reactions to Facebook Posts

Online Social Networks (OSNs) are based on the sharing of different types of information and on various interactions (comments, reactions, and sharing). One of these important actions is the emotional reaction to the content. The diversity of reaction types available on Facebook (namely FB) enables users to express their feelings, and its traceability creates and enriches the users’ emotional identity in the virtual world. This paper is based on the analysis of 119875012 FB reactions (Like, Love, Haha, Wow, Sad, Angry, Thankful, and Pride) made at multiple levels (publications, comments, and sub-comments) to study and classify the users’ emotional behavior, visualize the distribution of different types of reactions, and analyze the gender impact on emotion generation. All of these can be achieved by addressing these research questions: who reacts the most? Which emotion is the most expressed