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Sobre

Carla Teixeira Lopes é, atualmente, professora auxiliar no Departamento de Engenharia Informática da Faculdade de Engenharia da Universidade do Porto e investigadora sénior no INESC TEC. É doutorada (2013) em Engenharia Informática pela Faculdade de Engenharia da Universidade do Porto. Tem experiência de investigação e coordenação de trabalhos nas áreas de recuperação de informação, sistemas de gestão de dados, interação pessoa-computador, World Wide Web e análise de dados. A sua investigação atual está relacionada com recuperação de informação em saúde, com especial enfoque no desenvolvimento de ferramentas que apoiem os consumidores de saúde. 

 

Tópicos
de interesse
Detalhes

Detalhes

  • Nome

    Carla Lopes
  • Cluster

    Informática
  • Cargo

    Investigador Sénior
  • Desde

    01 maio 2014
004
Publicações

2021

Assessing the quality of health-related Wikipedia articles with generic and specific metrics

Autores
Couto, L; Lopes, CT;

Publicação
Companion of The Web Conference 2021, Virtual Event / Ljubljana, Slovenia, April 19-23, 2021.

Abstract

2020

Studying How Health Literacy Influences Attention during Online Information Seeking

Autores
Lopes, CT; Ramos, E;

Publicação
CHIIR '20: Conference on Human Information Interaction and Retrieval, Vancouver, BC, Canada, March 14-18, 2020 [the conference was cancelled due to the international COVID-19 health crisis].

Abstract
Health literacy affects how people understand health information and, therefore, should be considered by search engines in health searches. In this work, we analyze how the level of health literacy is related to the eye movements of users searching the web for health information. We performed a user study with 30 participants that were asked to search online in the context of three work task situations defined by the authors. Their eye interactions with the Search Results Page and the Result Pages were logged using an eye-tracker and later analyzed. When searching online for health information, people with adequate health literacy spend more time and have more fixations on Search Result Pages. In this type of page, they also pay more attention to the results' hyperlink and snippet and click in more results too. In Result Pages, adequate health literacy users spend more time analyzing textual content than people with lower health literacy. We found statistical differences in terms of clicks, fixations, and time spent that could be used as a starting point for further research. That we know of, this is the first work to use an eye-tracker to explore how users with different health literacy search online for health-related information. As traditional instruments are too intrusive to be used by search engines, an automatic prediction of health literacy would be very useful for this type of system. © 2020 ACM.

2020

Generating Query Suggestions for Cross-language and Cross-terminology Health Information Retrieval

Autores
Santos, PM; Lopes, CT;

Publicação
Advances in Information Retrieval - 42nd European Conference on IR Research, ECIR 2020, Lisbon, Portugal, April 14-17, 2020, Proceedings, Part II

Abstract
Medico-scientific concepts are not easily understood by laypeople that frequently use lay synonyms. For this reason, strategies that help users formulate health queries are essential. Health Suggestions is an existing extension for Google Chrome that provides suggestions in lay and medico-scientific terminologies, both in English and Portuguese. This work proposes, evaluates, and compares further strategies for generating suggestions based on the initial consumer query, using multi-concept recognition and the Unified Medical Language System (UMLS). The evaluation was done with an English and a Portuguese test collection, considering as baseline the suggestions initially provided by Health Suggestions. Given the importance of understandability, we used measures that combine relevance and understandability, namely, uRBP and uRBPgr. Our best method merges the Consumer Health Vocabulary (CHV)-preferred expression for each concept identified in the initial query for lay suggestions and the UMLS-preferred expressions for medico-scientific suggestions. Multi-concept recognition was critical for this improvement. © Springer Nature Switzerland AG 2020.

2020

ArchOnto, a CIDOC-CRM-Based Linked Data Model for the Portuguese Archives

Autores
Koch, I; Ribeiro, C; Lopes, CT;

Publicação
Digital Libraries for Open Knowledge - Lecture Notes in Computer Science

Abstract

2020

Proposal and Comparison of Health Specific Features for the Automatic Assessment of Readability

Autores
Antunes, H; Lopes, CT;

Publicação
Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval, SIGIR 2020, Virtual Event, China, July 25-30, 2020

Abstract

Teses
supervisionadas

2020

Digital images as data and metadata: description requirements for information retrieval and semantic interoperability

Autor
Joana Patrícia de Sousa Rodrigues

Instituição
UP-FEUP

2020

Integração de modelos para dados ligados no património cultural e contribuições para princípios FAIR

Autor
Inês Dias Koch

Instituição
UP-FEUP

2020

A utilização e produção de imagens em contexto de investigação: contribuições para boas práticas de gestão de dados em formato de imagem

Autor
Miguel Martins Fernandes

Instituição
UP-FEUP

2020

Analysis of web information-seeking behavior of users with different levels of health literacy

Autor
Mariana Cláudia Medeiros de Henriques

Instituição
UP-FEUP

2019

ArchGraph: Desenho e concepção de um protótipo vertical de infraestrutura para arquivos semânticos

Autor
Nuno Miguel Cardoso Lopes de Freitas

Instituição
UP-FEUP