Rechat: Ferramenta para Estudo do Comportamento de Usuários em Sistemas de Bate-papo do Estilo WhatsApp
Resumo
The spread of misinformation, hate speech, or sexist discourse had become a significant problem, especially on chat platforms. This article describes a data collection and processing tool to support research on users’ behavior exposed to these contents. Our motivation is the recent need to study profiles that propagate this type of information and the need to understand these new dissemination mechanisms. This tool provides a web configuration panel and a mobile application for data collection. Using a chatbot mechanism, the panel allows researchers to perform personalized experiments to analyze specific issues. The application collects the user’s basic profile, allows interaction with the chatbots sent by the researcher, and runs on the volunteers’ devices, similar to a real messaging application such as WhatsApp or Telegram. The data generated regarding the user’s interactions with the application can be exported for analysis through the panel. Also, we describe the insertion of optional codes that can be executed in parallel to the conversations, helping in sophisticated personalizations. We believe that the proposed open-source tool will help researchers from different areas, even without computer programming skills, to understand fundamental mechanisms of user behavior in mobile chat systems.
Referências
Trushar Barot and Eytan Oren. 2015. Guide to chat apps. Tow Center for Digital Journalism (2015).
Elizabeth Berndt-Morris and Samantha M Minnis. 2014. The chat is coming from inside the house: an analysis of perceived chat behavior and reality. Journal of library & information services in distance learning 8, 3-4 (2014), 168–180.
Walter W Borden, Alexander Abrams, and Dana S Spiegel. 2019. System and method for online monitoring of and interaction with chat and instant messaging participants. US Patent 10,298,700.
Daniel Buschek, Sarah Völkel, Clemens Stachl, Lukas Mecke, Sarah Prange, and Ken Pfeuffer. 2018. Experience Sampling as Information Transmission: Perspective and Implications. In Proc. of Ubicomp. ACM, New York, NY, USA,606–611.
AT Campbell and ND Lane. 2013. Smartphone sensing: A game changer for behavioral science. In Workshop held at the Summer Institute for Social and Personality Psychology. Harvard University, USA.
Douglas C Derrick, Thomas O Meservy, Jeffrey L Jenkins, Judee K Burgoon, and Jay F Nunamaker Jr. 2013. Detecting deceptive chat-based communication using typing behavior and message cues. ACM Transactions on Management Information Systems (TMIS) 4, 2 (2013), 1–21.
Nikolaos Dimotakis and Remus Ilies. 2013. Experience-sampling and event-sampling research. A Day in the Life of a Happy Worker (2013), 85–99.
Andrea Gaggioli, Giovanni Pioggia, Gennaro Tartarisco, Giovanni Baldus, Dani-ele Corda, Pietro Cipresso, and Giuseppe Riva. 2013. A mobile data collectionplatform for mental health research. Personal and Ubiquitous Computing 17, 2(2013), 241–251.
Leila Posenato Garcia and Elisete Duarte. 2020. Infodemia: excesso de quantidadeem detrimento da qualidade das informações sobre a COVID-19.
Deborah A Kerr, Christina M Pollard, Peter Howat, Edward J Delp, Mark Picke-ring, Katherine R Kerr, Satvinder S Dhaliwal, Iain S Pratt, Janine Wright, andCarol J Boushey. 2012. Connecting Health and Technology (CHAT): protocol of a randomized controlled trial to improve nutrition behaviours using mobile devices and tailored text messaging in young adults. BMC public health 12, 1(2012), 477.
Sunyoung Kim, Jennifer Mankoff, and Eric Paulos. 2013. Sensr: evaluating a flexible framework for authoring mobile data-collection tools for citizen science. In Proceedings of the 2013 conference on Computer supported cooperative work.1453–1462.
Juha K Laurila, Daniel Gatica-Perez, Imad Aad, Olivier Bornet, Trinh-Minh-TriDo, Olivier Dousse, Julien Eberle, Markus Miettinen, et al. 2012. The mobile data challenge: Big data for mobile computing research. Technical Report. Newcastle, UK.
Alireza Mogharrab and Carman Neustaedter. 2020. Family Group Chat: Family Needs to Manage Contact and Conflict. In Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems. 1–7.
Mika Raento, Antti Oulasvirta, and Nathan Eagle. 2009. Smartphones: An emerging tool for social scientists. Sociological methods & research 37, 3 (2009), 426–454.
Trevor W Rife and Jesse A Poland. 2014. Field book: an open-source application for field data collection on android. Crop Science 54, 4 (2014), 1624–1627.
Johannes Schobel, Rüdiger Pryss, Marc Schickler, and Manfred Reichert. 2016. A configurator component for end-user defined mobile data collection processes. In pROC. OF ICSOC. Springer, Springer, Cham, 216–219.
Cristina Tardáguila, Fabrício Benevenuto, and Pablo Ortellado. 2018. Fake News Is Poisoning Brazilian Politics. WhatsApp Can Stop It. New York Times (2018).
Harveen Kaur Ubhi, Susan Michie, Daniel Kotz, Wai Chi Wong, and Robert West. 2015. A mobile app to aid smoking cessation: preliminary evaluation of Smoke Free 28. Journal of medical Internet research 17, 1 (2015), e17.
Li Zhou, Jianfeng Gao, Di Li, and Heung-Yeung Shum. 2020. The design and implementation of xiaoice, an empathetic social chatbot. Computational Linguistics46, 1 (2020), 53–93.
