@InProceedings{CotacallapaChoqueBeFeQuZhMaVe:2020:MeEnLe,
               author = "Cotacallapa Choque, Frank Mosh{\'e} and Berton, Lilian and 
                         Ferreira, Leonardo Nascimento and Quiles, Marcos G. and Zhao, 
                         Liang and Macau, Elbert Einstein Nehrer and Vega-Oliveros, Didier 
                         A.",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Universidade Federal de S{\~a}o Paulo (UNIFESP)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Universidade Federal 
                         de S{\~a}o Paulo (UNIFESP)} and {Universidade de S{\~a}o Paulo 
                         (USP)} and {Universidade Federal de S{\~a}o Paulo (UNIFESP)} and 
                         {Universidade de S{\~a}o Paulo (USP)}",
                title = "Measuring the engagement level in encrypted group conversations by 
                         using temporal networks",
            booktitle = "Proceedings...",
                 year = "2020",
         organization = "International Joint Conference on Neural Networks",
            publisher = "IEEE",
             keywords = "User characterization, Network analysis, Temporal Networks, 
                         Encrypted group messages, Engagement index.",
             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 groups conversations are not 
                         accessible in plain text or readable format by thirdparty 
                         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 users 
                         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.",
  conference-location = "Glasglow, United Kingdom",
      conference-year = "19-24 July",
                 isbn = "978-172816926-2",
             language = "en",
           targetfile = "cotacallapa_measuring.pdf",
               volume = "2020",
        urlaccessdate = "28 abr. 2024"
}