Cookies Policy
The website need some cookies and similar means to function. If you permit us, we will use those means to collect data on your visits for aggregated statistics to improve our service. Find out More
Accept Reject
  • Menu
Publications

Publications by CRACS

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, A;

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

Abstract

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

2020

A METHODOLOGY TO ASSESS LEARNING PATTERNS IN ONLINE COURSES MEDIATED BY AN LMS

Authors
Figueira, A;

Publication
EDULEARN20 Proceedings

Abstract

2020

REPORT ON THE SELF-STUDY BEHAVIOR IN LEARNING FROM VIDEO LECTURES DURING A CONFINEMENT PERIOD

Authors
Figueira, A;

Publication
EDULEARN20 Proceedings

Abstract

2020

FOCAS: Penalising friendly citations to improve author ranking

Authors
Silva, JMB; Aparício, D; Ribeiro, P; Silva, FMA;

Publication
Proceedings of the ACM Symposium on Applied Computing

Abstract
Scientific impact is commonly associated with the number of citations received. However, an author can easily boost his own citation count by (i) publishing articles that cite his own previous work (self-citations), (ii) having co-authors citing his work (co-author citations), or (iii) exchanging citations with authors from other research groups (reciprocated citations). Even though these friendly citations inflate an author's perceived scientific impact, author ranking algorithms do not normally address them. They, at most, remove self-citations. Here we present Friends-Only Citations AnalySer (FOCAS), a method that identifies friendly citations and reduces their negative effect in author ranking algorithms. FOCAS combines the author citation network with the co-authorship network in order to measure author proximity and penalises citations between friendly authors. FOCAS is general and can be regarded as an independent module applied while running (any) PageRank-like author ranking algorithm. FOCAS can be tuned to use three different criteria, namely authors' distance, citation frequency, and citation recency, or combinations of these. We evaluate and compare FOCAS against eight state-of-the-art author ranking algorithms. We compare their rankings with a ground-truth of best paper awards. We test our hypothesis on a citation and co-authorship network comprised of seven Information Retrieval top-conferences. We observed that FOCAS improved author rankings by 25% on average and, in one case, leads to a gain of 46%. © 2020 ACM.

  • 1
  • 89