# Measuring the engagement level in encrypted group conversations by using temporal networks

## TL;DR

This paper introduces a framework that measures user engagement in encrypted group chats by analyzing temporal interaction networks, enabling insights without accessing message content.

## Contribution

It proposes a novel method using temporal networks and an Engagement Index to assess user participation in encrypted conversations, which was not possible with previous approaches.

## Key findings

- Effective identification of most engaged users within time intervals
- Ability to rank and group users based on engagement levels
- Monitoring user engagement trends over time

## Abstract

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.