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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.

Interest
Topics
Details

Details

004
Publications

2021

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

Authors
Couto, L; Lopes, CT;

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

Abstract

2021

How Can an Archive Be Characterized?

Authors
Araújo, MF; Lopes, CT;

Publication
Linking Theory and Practice of Digital Libraries - 25th International Conference on Theory and Practice of Digital Libraries, TPDL 2021, Virtual Event, September 13-17, 2021, Proceedings

Abstract
Archives are evolving. Analog archives are becoming increasingly digitized and linked with other cultural heritage institutions and information sources. Diverse forms of born-digital archives are appearing. This diversity asks for systematic ways to characterize existing archives managing physical or digital records. We conducted a systematic review to identify and understand how archives are characterized. From the 885 identified articles, only 15 were focused on archives’ characterization and, therefore, included in the study. We found several characterization features organized in three main groups: archival materials, provided services, and internal processes. © 2021, Springer Nature Switzerland AG.

2021

Equal opportunities in the access to quality online health information? A multi-lingual study on Wikipedia

Authors
Couto, L; Lopes, CT;

Publication
OpenSym 2021: 17th International Symposium on Open Collaboration, Virtual Event, Spain, September 15-17, 2021

Abstract
Wikipedia is a free, multilingual, and collaborative online encyclopedia. Nowadays, it is one of the largest sources of online knowledge, often appearing at the top of the results of the major search engines, being one of the most sought-after resources by the public searching for health information. The collaborative nature of Wikipedia raises security concerns since this information is used for decision-making, especially in the health area. Despite being available in hundreds of idioms, there are asymmetries between idioms, namely regarding their quality. In this work, we compare the quality of health information on Wikipedia between idioms with 100 million native speakers or more, and also in Greek, Italian, Korean, Turkish, Persian, Catalan and Hebrew, for historical tradition. Quality metrics are applied to health and medical articles in English, maintained by WikiProject Medicine, and their versions in the above idioms. With this, we contribute to a clarification of the role of Wikipedia in the access to health information. We demonstrate differences in both the quantity and quality of information available between idioms. English is the idiom with the highest quality in general. Urdu, Greek, Indonesian, and Hindi achieved lower values of quality. © 2021 ACM.

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.

Supervised
thesis

2021

Mutability of Web Search Engines Results - Data Collection and Brief Analysis

Author
Bruno Miguel Faustino Moreno

Institution
UP-FEUP

2021

Avaliação da qualidade da Wikipédia enquanto fonte de informação em saúde

Author
Luís Pedro da Silva Couto

Institution
UP-FEUP

2021

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

Author
Joana Patrícia de Sousa Rodrigues

Institution
UP-FEUP

2021

Web Search Engines - A study on the evolution of user interfaces

Author
Bruno Edgar Évora Rebelo Oliveira

Institution
UP-FEUP

2020

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

Author
Mariana Cláudia Medeiros de Henriques

Institution
UP-FEUP