A Brief Overview on the Strategies to Fight Back the Spread of False Information

  • Álvaro Figueira CRACS-INESCTEC and University of Porto, Porto, Portugal
  • Nuno Guimaraes CRACS-INESCTEC and University of Porto, Porto, Portugal
  • Luis Torgo Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia, Canada
Keywords: false information, social networks


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.


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Author Biographies

Álvaro Figueira, CRACS-INESCTEC and University of Porto, Porto, Portugal

Álvaro Figueira graduated in “Mathematics Applied to Computer Science” from Faculty of Sciences (UP) in 1995. He got his MSc in “Foundations of Advanced Information Technology” from Imperial College, London, in 1997, and his PhD in Computer Science from UP, in 2004. Prof. Figueira is currently an Assistant Professor with tenure at Faculty of Sciences in University of Porto. His research interests are in the areas of web mining, community detection, web-based learning and social media automated analysis. He is a researcher in the CRACS/INESCTEC research unit where he has been leading international projects involving University of Texas at Austin, University of Porto, University of Coimbra and University of Aveiro, regarding the automatic detection of relevance in social networks.

Nuno Guimaraes, CRACS-INESCTEC and University of Porto, Porto, Portugal

Nuno Guimaraes is currently a PhD student in Computer Science at the Faculty of Sciences University of Porto and a researcher at the Center for Research in Advanced Computing Systems (CRACS – INESCTEC). His PhD is focused on the analysis and detection of unreliable information on social media. He had previously worked as a researcher in REMINDS project whose goal was to detect journalistically relevant information on Social Media. Nuno completed his master’s and bachelor’s degree in Computer Science at the Faculty of Sciences of the University of Porto. In his master’s thesis, he developed a novel way to create time and domain dependent sentiment lexicons in an unsupervised fashion.

Luis Torgo, Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia, Canada

Luis Torgo is a Canada Research Chair (Tier 1) on Spatiotemporal Ocean Data Analytics and a Professor of Computer Science at the Faculty of Computer Science of the Dalhousie University, Canada. He also holds appointments as an Associate Professor of the Department of Computer Science of the Faculty of Sciences of the University of Porto, Portugal, and as an invited professor of the Stern Business School of the New York University where he has been teaching in recent years at the Master of Science in Business Analytics. Dr. Torgo has been doing research in the area of Data Mining and Machine Learning since 1990, and has published over 100 papers in several forums of these areas. Dr. Torgo is the author of the widely acclaimed Data Mining with R book published by CRC Press in 2010 with a strongly revised second edition that appeared in 2017. Dr. Torgo is also the CEO and one of the founding partners of KNOYDA a company devoted to training and consulting within data science.


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