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

Author(s)

Cotacallapa, Moshe ; Berton, Lilian ; Ferreira, Leonardo N. ; Quiles, Marcos G. ; Zhao, Liang ; Macau, Elbert E. N. ; Vega-Oliveros, Didier A. ; IEEE

• Document type: Journal article
• Source: 2020 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN); v. N/A, p. 8-pg., 2020-01-01.

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 asses 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. (AU)

• FAPESP's process: 17/05831-9 - Analysis of climate indexes influence on wildfires using complex networks and data mining
• Grantee: Leonardo Nascimento Ferreira
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• FAPESP's process: 16/23698-1 - Dynamical Processes in Complex Network based on Machine Learning
• Grantee: Didier Augusto Vega Oliveros
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• FAPESP's process: 16/16291-2 - Characterizing time-varying networks: methods and applications
• Grantee: Marcos Gonçalves Quiles
• Support Opportunities: Scholarships abroad - Research
• FAPESP's process: 18/24260-5 - Spatiotemporal Data Analytics based on Complex Networks
• Grantee: Didier Augusto Vega Oliveros
• Support Opportunities: Scholarships abroad - Research Internship - Post-doctor
• FAPESP's process: 15/50122-0 - Dynamic phenomena in complex networks: basics and applications
• Grantee: Elbert Einstein Nehrer Macau
• Support Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 18/01722-3 - Semi-supervised learning via complex networks: network construction, selection and propagation of labels and applications
• Grantee: Lilian Berton
• Support Opportunities: Regular Research Grants
• FAPESP's process: 19/00157-3 - Association and causality analyses between climate and wildfires using network science
• Grantee: Leonardo Nascimento Ferreira
• Support Opportunities: Scholarships abroad - Research Internship - Post-doctor