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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

2018

Human vs. Automatic Annotation Regarding the Task of Relevance Detection in Social Networks

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

Publication
Advances in Internet, Data & Web Technologies - Lecture Notes on Data Engineering and Communications Technologies

Abstract

2017

Detecting Journalistic Relevance on Social Media: A two-case study using automatic surrogate features

Authors
Figueira, A; Guimarães, N;

Publication
Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, Sydney, Australia, July 31 - August 03, 2017

Abstract

2017

Journalistic Relevance Classification in Social Network Messages: an Exploratory Approach

Authors
Sandim, M; Fortuna, P; Figueira, A; Oliveira, L;

Publication
COMPLEX NETWORKS & THEIR APPLICATIONS V

Abstract
Social networks are becoming a wide repository of information, some of which may be of interest for general audiences. In this study we investigate which features may be extracted from single posts propagated throughout a social network, and that are indicative of its relevance, from a journalistic perspective. We then test these features with a set of supervised learning algorithms in order to evaluate our hypothesis. The main results indicate that if a text fragment is pointed out as being interesting, meaningful for the majority of people, reliable and with a wide scope, then it is more likely to be considered as relevant. This approach also presents promising results when validated with several well-known learning algorithms.

2017

Predicting the Relevance of Social Media Posts Based on Linguistic Features and Journalistic Criteria

Authors
Pinto, A; Oliveira, HG; Figueira, A; Alves, AO;

Publication
NEW GENERATION COMPUTING

Abstract
An overwhelming quantity of messages is posted in social networks every minute. To make the utilization of these platforms more productive, it is imperative to filter out information that is irrelevant to the general audience, such as private messages, personal opinions or well-known facts. This work is focused on the automatic classification of public social text according to its potential relevance, from a journalistic point of view, hopefully improving the overall experience of using a social network. Our experiments were based on a set of posts with several criteria, including the journalistic relevance, assessed by human judges. To predict the latter, we rely exclusively on linguistic features, extracted by Natural Language Processing tools, regardless the author of the message and its profile information. In our first approach, different classifiers and feature engineering methods were used to predict relevance directly from the selected features. In a second approach, relevance was predicted indirectly, based on an ensemble of classifiers for other key criteria when defining relevance-controversy, interestingness, meaningfulness, novelty, reliability and scope-also in the dataset. The first approach achieved a F (1)-score of 0.76 and an Area under the ROC curve (AUC) of 0.63. But the best results were achieved by the second approach, with the best learned model achieving a F (1)-score of 0.84 with an AUC of 0.78. This confirmed that journalistic relevance can indeed be predicted by the combination of the selected criteria, and that linguistic features can be exploited to classify the latter.

2017

WHATSAPPENING OUTSIDE YOUR LMS? ANALYZING A SOCIAL MEDIA INSTANT MESSAGING POWERED LEARNING COMMUNITY

Authors
Oliveira, L; Figueira, A;

Publication
INTED2017 Proceedings

Abstract

Supervised
thesis

2016

Desenho e desenvolvimento de módulo para análise de redes socias online

Author
Rui Emanuel da Silva Pereira

Institution
UP-FCUP

2016

Lexicon Expansion System for Domain and Time Oriented Sentiment Analysis

Author
Nuno Ricardo Pinheiro da Silva Guimarães

Institution
UP-FCUP

2015

Social Media Content Strategies in Higher Public Polytechnic Education Institutions: an editorial model and analytics for sector monitoring

Author
Luciana Gomes de Oliveira

Institution
Outra