Cookies Policy
We use cookies to improve our site and your experience. By continuing to browse our site you accept our cookie policy. Find out More
Close
  • Menu
About

About

Carla Teixeira Lopes is an assistant Professor in the Department of Informatics Engineering, University of Porto, Portugal. She is also a researcher at INESC TEC since 2014. She received a PhD in Informatics Engineering from the University of Porto in 2013. Her research interests lie at the intersection of information retrieval and human-computer interaction. She is interested in studying information search behaviour and in developing tools that help people search more successfully. Lately, she has been focused in exploring how context can help improve the experience of health consumers searching the Web.

Interest
Topics
Details

Details

Publications

2017

Effects of Language and Terminology of Query Suggestions on Medical Accuracy Considering Different User Characteristics

Authors
Lopes, CT; Paiva, D; Ribeiro, C;

Publication
JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY

Abstract
Searching for health information is one of the most popular activities on the web. In this domain, users often misspell or lack knowledge of the proper medical terms to use in queries. To overcome these difficulties and attempt to retrieve higher-quality content, we developed a query suggestion system that provides alternative queries combining the Portuguese or English language with lay or medico-scientific terminology. Here we evaluate this system's impact on the medical accuracy of the knowledge acquired during the search. Evaluation shows that simply providing these suggestions contributes to reduce the quantity of incorrect content. This indicates that even when suggestions are not clicked, they are useful either for subsequent queries' formulation or for interpreting search results. Clicking on suggestions, regardless of type, leads to answers with more correct content. An analysis by type of suggestion and user characteristics showed that the benefits of certain languages and terminologies are more perceptible in users with certain levels of English proficiency and health literacy. This suggests a personalization of this suggestion system toward these characteristics. Overall, the effect of language is more preponderant than the effect of terminology. Clicks on English suggestions are clearly preferable to clicks on Portuguese ones.

2013

Measuring the value of health query translation: An analysis by user language proficiency

Authors
Lopes, CT; Ribeiro, C;

Publication
JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY

Abstract
English is by far the most used language on the web. In some domains, the existence of less content in the users' native language may not be problematic and even help to cope with the information overload. Yet, in domains such as health, where information quality is critical, a larger quantity of information may mean easier access to higher quality content. Query translation may be a good strategy to access content in other languages, but the presence of medical terms in health queries makes the translation process more difficult, even for users with very good language proficiencies. In this study, we evaluate how translating a health query affects users with different language proficiencies. We chose English as the non-native language because it is a widely spoken language and it is the most used language on the web. Our findings suggest that non-English-speaking users having at least elementary English proficiency can benefit from a system that suggests English alternatives for their queries, or automatically retrieves English content from a non-English query. This awareness of the user profile results in higher precision, more accurate medical knowledge, and better access to high-quality content. Moreover, the suggestions of English-translated queries may also trigger new health search strategies.

Supervised
thesis

2017

HealthTalks - Improving Health Communication and Personal Information Management

Author
João Miguel Lopes Vale Cardoso Monteiro

Institution
UP-FEUP

2016

HealthTranslator: automatic annotation of Web documents in order to assist health consumer’s searches

Author
Hugo Miguel Ribeiro de Sousa

Institution
UP-FEUP

2016

Query expansion strategies for laypeople-centred health information retrieval

Author
Ricardo Daniel Soares da Silva

Institution
UP-FEUP

2016

Análise de conteúdo em fóruns de saúde na Web: proposta de um esquema de classificação

Author
Bárbara Lia Guimarães da Silva

Institution
UP-FEUP