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About

About

I graduated in Mathematics Applied to Computer Science, from Faculty of Sciences (UP) in 1995, and took my MSc in Foundations of Advanced Information Technology, from Imperial College, London, in 1997. In 2004 I concluded my PhD in Computer Science in concurrent and distributed programming.

I am currently an Assistant Professor, with tenure, at Faculty of Sciences in University of Porto. My research interests are in the areas of text and web mining, community detection, e-learning and web-based learning and standards in education.

I'm also a researcher in the CRACS Research Unit where I have been leading international projects involving University of University of Porto, Texas at Austin, University of Coimbra and University of Aveiro, regarding the automatic detection of relevance in social networks.

Interest
Topics
Details

Details

002
Publications

2020

Identifying journalistically relevant social media texts using human and automatic methodologies

Authors
Figueira, Á; Guimar?ães, N; Miranda, F;

Publication
International Journal of Grid and Utility Computing

Abstract

2020

Identifying journalistically relevant social media texts using human and automatic methodologies

Authors
Guimaraes, N; Miranda, F; Figueira, A;

Publication
INTERNATIONAL JOURNAL OF GRID AND UTILITY COMPUTING

Abstract
Social networks have provided the means for constant connectivity and fast information dissemination. In addition, real-time posting allows a new form of citizen journalism, where users can report events from a witness perspective. Therefore, information propagates through the network at a faster pace than traditional media reports it. However, relevant information is a small percentage of all the content shared. Our goal is to develop and evaluate models that can automatically detect journalistic relevance. To do it, we need solid and reliable ground truth data with a significantly large quantity of annotated posts, so that the models can learn to detect relevance over all the spectrum. In this article, we present and confront two different methodologies: an automatic and a human approach. Results on a test data set labelled by experts' show that the models trained with automatic methodology tend to perform better in contrast to the ones trained using human annotated data.

2020

Contribution of Social Tagging to Clustering Effectiveness Using as Interpretant the User’s Community

Authors
Cunha, E; Figueira, Á;

Publication
Trends and Innovations in Information Systems and Technologies - Advances in Intelligent Systems and Computing

Abstract

2020

Identifying journalistically relevant social media texts using human and automatic methodologies

Authors
Guimarães, N; Miranda, F; Figueira, A;

Publication
IJGUC

Abstract

2019

A Brief Overview on the Strategies to Fight Back the Spread of False Information

Authors
Figueira, A; Guirnaraes, N; Torgo, L;

Publication
JOURNAL OF WEB ENGINEERING

Abstract
The proliferation of false information on social networks is one of the hardest challenges in today's society, with implications capable of changing users perception on what is a fact or rumor. Due to its complexity, there has been an overwhelming number of contributions from the research community like the analysis of specific events where rumors are spread, analysis of the propagation of false content on the network, or machine learning algorithms to distinguish what is a fact and what is "fake news". In this paper, we identify and summarize some of the most prevalent works on the different categories studied. Finally, we also discuss the methods applied to deceive users and what are the next main challenges of this area.

Supervised
thesis

2019

Análise da utilização dos recursos do Moodle para prever classificações

Author
Bruno Miguel Ribeiro Cabral

Institution
UP-FCUP

2019

Analyzing and Developing Indicators for Building an Automatic Detector of Unreliable Information in Social Media

Author
Nuno Ricardo Pinheiro da Silva Guimarães

Institution
UP-FCUP

2018

Social Media Governance in the Public Portuguese Polytechnical

Author
Luciana Gomes de Oliveira

Institution
Outra

2018

Analyzing and Developing Indicators for Building an Automatic Detector of Unreliable Information in Social Media

Author
Nuno Ricardo Pinheiro da Silva Guimarães

Institution
UP-FCUP

2017

Computing the accuracy of an automatic system for relevance detection in social networks

Author
Filipe Fernandes Miranda

Institution
UP-FCUP