  Chat groups are well-known for their capacity to promote viral political and
marketing campaigns, spread fake news, and create rallies by hundreds of
thousands on the streets. Also, with the increasing public awareness regarding
privacy and surveillance, many platforms have started to deploy end-to-end
encrypted protocols. In this context, the group's conversations are not
accessible in plain text or readable format by third-party organizations or
even the platform owner. Then, the main challenge that emerges is related to
getting insights from users' activity of those groups, but without accessing
the messages. Previous approaches evaluated the user engagement by assessing
user's activity, however, on limited conditions where the data is encrypted,
they cannot be applied. In this work, we present a framework for measuring the
level of engagement of group conversations and users, without reading the
messages. Our framework creates an ensemble of interaction networks that
represent the temporal evolution of the conversation, then, we apply the
proposed Engagement Index (EI) for each interval of conversations to assess
users' participation. Our results in five datasets from real-world WhatsApp
Groups indicate that, based on the EI, it is possible to identify the most
engaged users within a time interval, create rankings, and group users
according to their engagement and monitor their performance over time.
