Deep Learning Models to Detect Online False Information: A Systematic Literature Review

2021 ◽  
Author(s):  
Asmaa Seyam ◽  
Ali Bou Nassif ◽  
Manar Abu Talib ◽  
Qassim Nasir ◽  
Bushra Al Blooshi
2021 ◽  
Vol 21 (2) ◽  
pp. 1-31
Author(s):  
Bjarne Pfitzner ◽  
Nico Steckhan ◽  
Bert Arnrich

Data privacy is a very important issue. Especially in fields like medicine, it is paramount to abide by the existing privacy regulations to preserve patients’ anonymity. However, data is required for research and training machine learning models that could help gain insight into complex correlations or personalised treatments that may otherwise stay undiscovered. Those models generally scale with the amount of data available, but the current situation often prohibits building large databases across sites. So it would be beneficial to be able to combine similar or related data from different sites all over the world while still preserving data privacy. Federated learning has been proposed as a solution for this, because it relies on the sharing of machine learning models, instead of the raw data itself. That means private data never leaves the site or device it was collected on. Federated learning is an emerging research area, and many domains have been identified for the application of those methods. This systematic literature review provides an extensive look at the concept of and research into federated learning and its applicability for confidential healthcare datasets.


2021 ◽  
Author(s):  
Ghita Amrani ◽  
Amina Adadi ◽  
Mohammed Berrada ◽  
Zouhayr Souirti ◽  
Said Boujraf

2021 ◽  
Author(s):  
Andrea Camille Garcia ◽  
Jealine Eleanor Gorre ◽  
Joshua Angelo Karl Perez ◽  
Mary Jane Samonte

2020 ◽  
pp. 146144482095929 ◽  
Author(s):  
Eleni Kapantai ◽  
Androniki Christopoulou ◽  
Christos Berberidis ◽  
Vassilios Peristeras

The scale, volume, and distribution speed of disinformation raise concerns in governments, businesses, and citizens. To respond effectively to this problem, we first need to disambiguate, understand, and clearly define the phenomenon. Our online information landscape is characterized by a variety of different types of false information. There is no commonly agreed typology framework, specific categorization criteria, and explicit definitions as a basis to assist the further investigation of the area. Our work is focused on filling this need. Our contribution is twofold. First, we collect the various implicit and explicit disinformation typologies proposed by scholars. We consolidate the findings following certain design principles to articulate an all-inclusive disinformation typology. Second, we propose three independent dimensions with controlled values per dimension as categorization criteria for all types of disinformation. The taxonomy can promote and support further multidisciplinary research to analyze the special characteristics of the identified disinformation types.


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