Automatic Identification of Political Hate Articles from Social Media using Recurrent Neural Networks

TL;DR

This paper presents a deep learning approach using recurrent neural networks to automatically identify political hate articles from social media texts, achieving high accuracy in classifying political ideologies.

Contribution

It introduces a combined feature extraction method and demonstrates that LSTM with word embeddings outperforms other models in political text classification.

Findings

LSTM with word embeddings achieves 88.28% accuracy.

Combining stylometric, word-embedding, and TF-IDF features improves classification.

Deep learning models outperform traditional classifiers in this task.

Abstract

The increasing growth of social media provides us with an instant opportunity to be informed of the opinions of a large number of politically active individuals in real-time. We can get an overall idea of the ideologies of these individuals on governmental issues by analyzing the social media texts. Nowadays, different kinds of news websites and popular social media such as Facebook, YouTube, Instagram, etc. are the most popular means of communication for the mass population. So the political perception of the users toward different parties in the country is reflected in the data collected from these social sites. In this work, we have extracted three types of features, such as the stylometric feature, the word-embedding feature, and the TF-IDF feature. Traditional machine learning classifiers and deep learning models are employed to identify political ideology from the text. We have compared our methodology with the research work in different languages. Among them, the word embedding feature with LSTM outperforms all other models with 88.28% accuracy.