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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.

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Topics
Details

Details

003
Publications

2020

Studying How Health Literacy Influences Attention during Online Information Seeking

Authors
Lopes, CT; Ramos, E;

Publication
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

Authors
Santos, PM; Lopes, CT;

Publication
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.

2019

A classification scheme for analyses of messages exchanged in online health forums

Authors
Lopes, CT; Da Silva, BG;

Publication
INFORMATION RESEARCH-AN INTERNATIONAL ELECTRONIC JOURNAL

Abstract
Introduction. Online health forums help to surface and organize patients' knowledge and make it useful for many. They are used by many to seek for advice or to share what they know about health subjects. Being an important communication medium, it's important to understand why and how it is used. Method. In this work we examine and categorize messages of an online health forum, with the purpose of providing a classification scheme that can be used by the research community in future analyses. The definition of the classification scheme was iterative and its inter-rater reliability was assessed twice using Cohen's Kappa statistic. Analysis. The classification scheme arose from a content analysis of 3,399 messages from several communities of an online health forum. Findings. The scheme is divided into four sections of categories and each section has several subcategories, in total there are 23 subcategories. The inter-rater agreement assessment of the scheme showed a good consistency between coders. The majority of the categories has a Cohen's Kappa agreement above 0.4. Conclusion. The proposed classification scheme facilitates the analysis of messages exchanged in online health forums for several purposes, including studies of information seeking.

2019

Interplay of Documents' Readability, Comprehension and Consumer Health Search Performance Across Query Terminology

Authors
Lopes, CT; Ribeiro, C;

Publication
Proceedings of the 2019 Conference on Human Information Interaction and Retrieval, CHIIR 2019, Glasgow, Scotland, UK, March 10-14, 2019

Abstract
Because of terminology mismatches, health consumers frequently face difficulties while searching the Web for health information. Difficulties arise in query formulation but also in understanding the retrieved documents. In this work we analyze how documents' readability affects users' comprehension and how both affect the retrieval performance, measured in different ways. In addition, we analyze how performance measures relate with each other. For this purpose we have conducted a laboratory user study with 40 participants. We found that readability is essential for a document to be at least partially relevant and that it becomes even more important if the document has medico-scientific terminology. Moreover, the relevance of a document to a specific user highly depends on its comprehension. In lay queries we found the medical accuracy of users' answers is related to the session's relevance assessments. This shows that users can, at least in part, relate their relevance assessments with the medical accuracy of the documents. On the other hand, this relationship does not exist with medico-scientific queries. © 2019 Copyright held by the owner/author(s). Publication rights licensed to Association for Computing Machinery.

2019

Assisting Health Consumers While Searching the Web through Medical Annotations

Authors
Lopes, CT; Sousa, H;

Publication
Proceedings of the 2019 Conference on Human Information Interaction and Retrieval, CHIIR 2019, Glasgow, Scotland, UK, March 10-14, 2019

Abstract

Supervised
thesis

2019

HealthSuggestions: moving beyond the beta version

Author
Paulo Miguel Pereira dos Santos

Institution
UP-FEUP

2019

Social Media and Chatbots use for Chronic Disease Patients Support: Case Study from an Online Community Regarding Therapeutic use of Cannabis

Author
Alice Rangel Teixeira

Institution
UP-FEUP

2019

Ferramentas para o Planeamento e Avaliação de Risco de Dados de Investigação: o caso do i3S no âmbito do RGPD

Author
Maria do Rosário Ferreira Nunes

Institution
UP-FEUP

2019

Automatic Prediction of Health Literacy through an eye tracker

Author
Edgar Duarte Ramos

Institution
UP-FEUP

2019

Automatic Assessment of Health Information Readability

Author
Hélder Manuel Mouro Antunes

Institution
UP-FEUP