| dc.description | Online social networks are a powerful communication and information
dissemination tool, particularly useful in complex
scenarios such as social crises, natural disasters, and pandemics.
However, one of the main problems, especially in
socio-political crises, is the automatic detection of fake news.
This problem is usually addressed with greater or lesser success
using supervised machine learning techniques. In this work,
we propose a mixed approach, using unsupervised learning for
feature extraction, and supervised learning for the prediction
of fake news on microblogging networks. We consider Twitter
news with linguistic and network features. To identify hidden
patterns in the data, we use Principal Component Analysis
and t-Distributed Stochastic Neighbor Embedding. The results
show that the data can be better classified using non-linear
rather than linear separability. Moreover, when using Extreme
Gradient Boosting (XGBoost), an accuracy of 99.26% is obtained,
and the most relevant features are identified. | |
