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

• Type: Article in International Conference Proceedings Book
• Date: 2018

Authors

Guimarães, N (Author) Other; Figueira, A (Author) FCUP; Torgo, L (Author) FCUP

Conference proceedings International

Title: 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management: Proceedings

Pages: 90-99

Indexing

Scopus - 5 Citations

Other information

Authenticus ID: P-00P-MH9

DOI: 10.5220/0006932800920101

Abstract (EN): 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 focus 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. Copyright 2018 by SCITEPRESS ¿ Science and Technology Publications, Lda. All rights reserved

Language: English

Type (Professor's evaluation): Scientific