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1. How using various platforms shapes awareness of algorithms.

Author

Espinoza-Rojas, Johan, Siles, Ignacio, and Castelain, Thomas

Subjects
*COMPUTER software, *PROFESSIONS, *MASS media, *AROUSAL (Physiology), *RESEARCH methodology, *GROUNDED theory, *ATTITUDE (Psychology), *ECOLOGY, *INTERVIEWING, *VIDEOCONFERENCING, *INTERNET access, *SURVEYS, *STATISTICAL sampling, *EMOTIONS, *ALGORITHMS
Abstract

This paper examines how the use of multiple platforms is tied to awareness of algorithms. It builds on the premise that users interact with ecologies or environments of technologies rather than single platforms. The study also supplements work on algorithmic awareness by implementing a mixed-method study to account for how Costa Rican users of Netflix and Spotify understood and related to the algorithms of these platforms. This study combined a survey of 258 participants and 21 in-depth semistructured interviews. Findings demonstrate that multi-platform users were more aware of algorithms and carried out more practical actions to obtain algorithmic recommendations than single-platform users. Although user type did not predict participants' attitudes towards algorithmic recommendations, higher levels of awareness were associated with more positive attitudes towards algorithms. The study also shows that differences in levels of awareness explained users' emotional arousal derived from algorithms. [ABSTRACT FROM AUTHOR]

Published
2023
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2. Mathematical and statistical models for the control of mosquito-borne diseases: the experience of Costa Rica.

Author

Vásquez, Paola, Sanchez, Fabio, Barboza, Luis A., García, Yury E., Calvo, Juan G., Shu-Wei Chou-Chen, and Mery, Gustavo

Subjects
*RELATIVE medical risk, *MOSQUITO vectors, *DENGUE, *CHIKUNGUNYA, *MACHINE learning, *MOSQUITO-borne diseases, *RISK assessment, *DECISION making, *STATISTICAL models, *ZIKA virus infections, *PROBABILITY theory, *ALGORITHMS, *DISEASE risk factors, *INFECTIOUS disease transmission
Abstract

Objective. To summarize the results of research conducted in Costa Rica in which mathematical and statistical methods were implemented to study the transmission dynamics of mosquito-borne diseases. Methods. Three articles with mathematical and statistical analysis on vector-borne diseases in Costa Rica were selected and reviewed. These papers show the value and relevance of using different quantitative methods to understand disease dynamics and support decision-making. Results. The results of these investigations: 1) show the impact on dengue case reports when a second pathogen emerges, such as chikungunya; 2) recover key parameters in Zika dynamics using Bayesian inference; and 3) show the use of machine learning algorithms and climatic variables to forecast the dengue relative risk in five different locations. Conclusions. Mathematical and statistical modeling enables the description of mosquito-borne disease transmission dynamics, providing quantitative information to support prevention/control methods and resource allocation planning. [ABSTRACT FROM AUTHOR]

Published
2022
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