Fake Twitter followers detection by denoising autoencoder

By Jacopo Castellini, Valentina Poggioni, Giulia Sorbi. 2017

32 citations recorded by the source corpus.

Gaining followers on the Twitter platform has become a rapid way to increase one's credibility on this social network, that in the last few years has become a launch pad for new trends and to influence people opinions. So, many people have begun to buy fake followers on underground markets appositely created to sold them. Therefore, identifying fake followers profiles is useful to maintain the balance between real influential people on the network and people who simply exploited this mechanism. This work presents a model based on artificial neural networks able to detect fake Twitter profiles. In particular, a denoising autoencoder has been implemented as anomaly detector trained with a semi-supervised learning approach. The model has been tested on a benchmark already used in literature and results are presented.

Subfields: Computer Networks and Communications, Information Systems, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Francisco Herrera
2. Salvador Garcia

In Information Systems

1. Salvador Garcia
2. Francisco Herrera
3. M. J. Jesús
4. Francisco Charte

In Signal Processing

1. M. J. Jesús
2. Francisco Herrera
3. Francisco Charte
4. David Charte
5. Salvador Garcia
6. Enrique Herrera-Viedma
7. Francisco Chiclana

Citations in the corpus

• A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines (2018, background): “A practical application of abnormal pattern detection with AEs is the one proposed in [129].”