Authors:
Nuno Guimarães
1
;
Álvaro Figueira
1
and
Luís Torgo
2
Affiliations:
1
CRACS / INESCTEC & University of Porto, Rua do Campo Alegre 1021/1055, Porto and Portugal
;
2
CRACS / INESCTEC & University of Porto, Rua do Campo Alegre 1021/1055, Porto, Portugal, Faculty of Computer Science, Dalhousie University, Halifax and Canada
Keyword(s):
Automatic Detection, Unreliable Twitter Accounts, Text Mining, Sentiment Analysis, Machine Learning.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Business Analytics
;
Computational Intelligence
;
Data Analytics
;
Data Engineering
;
Evolutionary Computing
;
Information Extraction
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Mining Text and Semi-Structured Data
;
Soft Computing
;
Symbolic Systems
Abstract:
Misinformation propagation on social media has been significantly growing, reaching a major exposition in the 2016 United States Presidential Election. Since then, the scientific community and major tech companies have been working on the problem to avoid the propagation of misinformation. For this matter, research has been focused on three major sub-fields: the identification of fake news through the analysis of unreliable posts, the propagation patterns of posts in social media, and the detection of bots and spammers. However, few works have tried to identify the characteristics of a post that shares unreliable content and the associated behaviour of its account. This work presents four main contributions for this problem. First, we provide a methodology to build a large knowledge database with tweets who disseminate misinformation links. Then, we answer research questions on the data with the goal of bridging these problems to similar problem explored in the literature. Next, we f
ocus on accounts which are constantly propagating misinformation links. Finally, based on the analysis conducted, we develop a model to detect social media accounts that spread unreliable content. Using Decision Trees, we achieved 96% in the F1-score metric, which provides reliability on our approach.
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