Characterizing Linguistic Attributes for Automatic Classification of Intent Based Racist/Radicalized Posts on Tumblr Micro-Blogging Website

TL;DR

This paper develops an ensemble learning classifier to detect racist or radicalized intent in Tumblr posts by analyzing semantic, sentiment, and linguistic features, addressing ambiguity beyond keyword spotting.

Contribution

It introduces a novel approach combining semantic, sentiment, and linguistic features with ensemble learning to identify racist intent in social media posts.

Findings

Effective discrimination of racist posts using emotion tone and social tendencies.

Linguistic cues and personality traits are key features for intent classification.

Proposed method outperforms baseline keyword-based techniques.

Abstract

Research shows that many like-minded people use popular microblogging websites for posting hateful speech against various religions and race. Automatic identification of racist and hate promoting posts is required for building social media intelligence and security informatics based solutions. However, just keyword spotting based techniques cannot be used to accurately identify the intent of a post. In this paper, we address the challenge of the presence of ambiguity in such posts by identifying the intent of author. We conduct our study on Tumblr microblogging website and develop a cascaded ensemble learning classifier for identifying the posts having racist or radicalized intent. We train our model by identifying various semantic, sentiment and linguistic features from free-form text. Our experimental results shows that the proposed approach is effective and the emotion tone, social tendencies, language cues and personality traits of a narrative are discriminatory features for identifying the racist intent behind a post.