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

By Swati Agarwal, Ashish Sureka. 2017

61 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Communication, Information Systems

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Citations in the corpus

Towards Open Intent Discovery for Conversational Text (2019, methodology): “Supervised and semi-supervised learning models based on linguistic and sentiment features have been used to model racial intent on Tumblr [1], and purchase intent in social posts [14, 47] and in discussion forums [5].”
A Survey on Automatic Detection of Hate Speech in Text (2018, methodology): “Characteristics of the message were also considered such as hashtags, mentions, retweets, URLs, number of tags, terms used in the tags, number of notes (reblog and like count), and link to multimedia content, such as image, video, or audio attached to the post [3].”
Benchmarking Aggression Identification in Social Media (2018, background): “, 2015; Malmasi and Zampieri, 2018; Waseem and Hovy, 2016; Schmidt and Wiegand, 2017), radicalization (Agarwal and Sureka, 2015; Agarwal and Sureka, 2017), racism (Greevy and Smeaton, 2004; Greevy, 2004) and others.”