A Federated Approach for Hate Speech Detection

02/18/2023
by   Jay Gala, et al.
0

Hate speech detection has been the subject of high research attention, due to the scale of content created on social media. In spite of the attention and the sensitive nature of the task, privacy preservation in hate speech detection has remained under-studied. The majority of research has focused on centralised machine learning infrastructures which risk leaking data. In this paper, we show that using federated machine learning can help address privacy the concerns that are inherent to hate speech detection while obtaining up to 6.81 improvement in terms of F1-score.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
07/28/2021

Detecting Abusive Albanian

The ever growing usage of social media in the recent years has had a dir...
research
12/01/2022

Early prediction of the risk of ICU mortality with Deep Federated Learning

Intensive Care Units usually carry patients with a serious risk of morta...
research
07/08/2020

Automatic Detection of Sexist Statements Commonly Used at the Workplace

Detecting hate speech in the workplace is a unique classification task, ...
research
03/31/2023

Porównanie metod detekcji zajętości widma radiowego z wykorzystaniem uczenia federacyjnego z oraz bez węzła centralnego

Dynamic spectrum access systems typically require information about the ...
research
02/05/2022

PrivPAS: A real time Privacy-Preserving AI System and applied ethics

With 3.78 billion social media users worldwide in 2021 (48 population), ...
research
07/12/2019

Spearphone: A Speech Privacy Exploit via Accelerometer-Sensed Reverberations from Smartphone Loudspeakers

In this paper, we build a speech privacy attack that exploits speech rev...
research
07/28/2020

Detecting and analysing spontaneous oral cancer speech in the wild

Oral cancer speech is a disease which impacts more than half a million p...

Please sign up or login with your details

Forgot password? Click here to reset