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Identification of Political Hate Speech Using Machine Learning-Based Text Toxicity Analysis

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ICT Systems and Sustainability

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 516))

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Abstract

The maintenance of civility during political campaigns has taken a huge blow during recent times with ever increasing toxicity in speeches targeting polarization of people for electoral gains. This is however creating a big divide in the society by the proliferation of cyber-hate speech, which is threatening the integrity and harmony of societies. The term “hate speech” refers to the use of words or phrases that are threatening, derogatory, or insulting to a specific individual or group. Users of social media in India are increasing rapidly, and this is coupled with an increase in the frequency with which cyber-hate speech targets specific segments of society or individuals based on their caste, color, or creed. An online environment free of hostility and bigotry has remained a key focus area of academics’ attention. In the present study, some contemporary political data from the Twitter platform is being used to identify hate speech with the help of machine training and learning methods including the text-based natural language processing (NLP). To achieve the identified goal, several political tweets across different ideologies have been taken into consideration. There are many different ways to collect and organize emotions and personality traits. Analysis of the processed dataset has been done using an ensemble of popular machine learning algorithms, and the results indicate the comparative performance of the methods.

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Authors and Affiliations

  1. MVN University, Aurangabad, India

    Priya & Sachin Gupta

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  1. Priya
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  2. Sachin Gupta
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Correspondence to Priya .

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Editors and Affiliations

  1. Singidunum University, Belgrade, Serbia

    Milan Tuba

  2. ITM University, Gwalior, Madhya Pradesh, India

    Shyam Akashe

  3. Global Knowledge Research Foundation, Ahmedabad, India

    Amit Joshi

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Priya, Gupta, S. (2023). Identification of Political Hate Speech Using Machine Learning-Based Text Toxicity Analysis. In: Tuba, M., Akashe, S., Joshi, A. (eds) ICT Systems and Sustainability. Lecture Notes in Networks and Systems, vol 516. Springer, Singapore. https://doi.org/10.1007/978-981-19-5221-0_22

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