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Publications

Publications by CRACS

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

2018

Twitter as a Source for Time- and Domain-Dependent Sentiment Lexicons

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

Publication
Lecture Notes in Social Networks - Social Network Based Big Data Analysis and Applications

Abstract

2018

Contributions to the Detection of Unreliable Twitter Accounts through Analysis of Content and Behaviour

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

Publication
Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management

Abstract

2018

Current State of the Art to Detect Fake News in Social Media: Global Trendings and Next Challenges

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

Publication
Proceedings of the 14th International Conference on Web Information Systems and Technologies

Abstract

2018

Uncovering Social Media Content Strategies for Worldwide Top-Ranked Universities

Authors
Figueira, Á;

Publication
Procedia Computer Science

Abstract

2018

A Three-Step Data-Mining Analysis of Top-Ranked Higher Education Institutions' Communication on Facebook

Authors
Figueira, A;

Publication
Proceedings of the Sixth International Conference on Technological Ecosystems for Enhancing Multiculturality, Salamanca, Spain, October 24-26, 2018

Abstract
Organizations are rushing into social media networks following a worldwide trend to create a social presence in multiple media channels. However, a social media strategy needs to be aligned with and framed in the overall organizational strategic management goals. Higher Educational Institutions (HEI) are not different from other organizations in which concerns these problems. Determining the organizational positioning of an organization current strategy will allow to combine monitoring and benchmarking methods to foster the identification of opportunities and threats, which can serve as inputs for the internal evaluation of social media strategies', for the necessary strategic readjustments and a subsequent efficiency measurement. In order to address these challenges, we propose a three-step automatic data-mining procedure to assess the posting behavior and strategy of HEI, understand the editorial policy behind it, and predict the future HEI engagement. We used a sample of the 5-top ranked educational institutions in 2017. We collected the posts from each HEI official Facebook page during an entire school year. Our method showed high degree of accuracy and is also capable of describing which topics are most common in each university's social media content strategy and relate them to the corresponding response from their publics. © 2018 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

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