Abusive Language Detection and Characterization of Twitter Behavior

09/26/2020
by   Dincy Davis, et al.
0

In this work, abusive language detection in online content is performed using Bidirectional Recurrent Neural Network (BiRNN) method. Here the main objective is to focus on various forms of abusive behaviors on Twitter and to detect whether a speech is abusive or not. The results are compared for various abusive behaviors in social media, with Convolutional Neural Netwrok (CNN) and Recurrent Neural Network (RNN) methods and proved that the proposed BiRNN is a better deep learning model for automatic abusive speech detection.

READ FULL TEXT

page 1

page 5

page 7

research
06/08/2022

Improved two-stage hate speech classification for twitter based on Deep Neural Networks

Hate speech is a form of online harassment that involves the use of abus...
research
02/01/2018

A Unified Deep Learning Architecture for Abuse Detection

Hate speech, offensive language, sexism, racism and other types of abusi...
research
04/07/2021

Three-class Overlapped Speech Detection using a Convolutional Recurrent Neural Network

In this work, we propose an overlapped speech detection system trained a...
research
08/30/2018

Comparative Studies of Detecting Abusive Language on Twitter

The context-dependent nature of online aggression makes annotating large...
research
09/15/2020

Improving Joint Layer RNN based Keyphrase Extraction by Using Syntactical Features

Keyphrase extraction as a task to identify important words or phrases fr...
research
12/04/2021

Unraveling Social Perceptions Behaviors towards Migrants on Twitter

We draw insights from the social psychology literature to identify two f...
research
05/03/2021

Towards A Multi-agent System for Online Hate Speech Detection

This paper envisions a multi-agent system for detecting the presence of ...

Please sign up or login with your details

Forgot password? Click here to reset