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About

I am part of REMINDS - RElevance MINing and Detection System project and my focus is on Sentiment Analysis on Social Networks.

I completed my Master's Degree in Computer Science in the Faculty of Science at the University of Porto

I graduated in Computer Science (Bsc) in the Faculty of Science at the University of Porto

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001
Publications

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

Analysis and Detection of Unreliable Users in Twitter: Two Case Studies

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

Publication
Communications in Computer and Information Science - Knowledge Discovery, Knowledge Engineering and Knowledge Management

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.

2019

A System to Automatically Predict Relevance in Social Media

Authors
Figueira, A; Guimaraes, N; Pinto, J;

Publication
Procedia Computer Science

Abstract

2018

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

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

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

Abstract