International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 05 | May 2024
p-ISSN: 2395-0072
www.irjet.net
“Exploring Gender Prediction and Hate Speech Detection in the Twitter: A Machine Learning Approach” Juluru V Y H Lakshmi Narsitha1 1UG Student, Department of Computer Science, R.V.R.&J.C College of Engineering, Guntur, Andhra Pradesh, India
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Abstract - In the dynamic world of social media, this
descriptions, we aim to uncover patterns and features that contribute to accurate gender prediction.
study embarks on a journey to explore the realms of gender prediction and hate speech detection using Twitter data. For gender prediction, GloVe embeddings are employed for text preprocessing, while logistic regression, support vector machines, and random forests etc. serve as classification algorithms. Twitter data encompassing user tweets and descriptions are scrutinized both individually and combined to predict gender based on textual features. In parallel, hate speech detection focuses exclusively on tweets, employing a bag-of-words representation and decision tree classifiers. The research evaluates the efficacy of these diverse algorithms in accurately predicting gender and detecting hate speech within Twitter data, illuminating the intricate challenges and promising avenues inherent in such tasks. By navigating the amalgamation of social media and machine learning methodologies, this study aims to offer valuable insights into the realms of gender prediction and hate speech detection, particularly within the context of online discourse.
Simultaneously, our focus extends to the vital task of hate speech detection, a pressing issue in contemporary online discourse. Hate speech, characterized by its harmful and discriminatory nature, poses serious challenges to fostering respectful interactions and upholding community standards. Employing a bag-of-words representation and decision tree classifiers, we aim to identify and mitigate instances of hate speech within the Twitter data corpus. Through rigorous evaluation, we seek to assess the efficacy of diverse machine learning algorithms in accurately predicting gender and detecting hate speech. By navigating the intricate interplay between social media dynamics and machine learning methodologies, this study aims to offer valuable insights into gender prediction and hate speech detection within the context of online discourse. Through our exploration, we aspire to contribute to a deeper understanding of these phenomena and inform strategies for creating safer and more inclusive digital spaces.
Key Words: GloVe (Global Vectors) embeddings, Bag of Words, Gender Prediction, Hate Speech Detection, Twitter Data Analysis, Machine Learning, Textual Features, User Profile Analysis
1.1 Research Significance
1.INTRODUCTION
The significance of this research lies in its potential to address critical issues within the realm of social media and online discourse. Gender prediction and hate speech detection are two areas of considerable importance, impacting various aspects of digital interactions and societal dynamics.
In the ever-evolving landscape of social media, platforms like Twitter have emerged as rich sources of data, offering unique insights into human behavior and communication patterns. This study embarks on an exploration of two pivotal facets of social media analysis: gender prediction and hate speech detection. With the pervasive influence of online interactions, understanding how individuals represent themselves and identifying harmful speech is crucial for fostering inclusive and respectful digital environments.
Firstly, gender prediction holds implications for understanding user behavior, preferences, and interactions within online communities. By accurately predicting gender based on textual data, researchers and practitioners can gain insights into gender representation and its influence on communication patterns. This knowledge can inform targeted marketing strategies, personalized user experiences, and sociological studies examining gender dynamics in digital spaces.
Gender prediction, a complex yet intriguing task, holds significant implications for various domains, including marketing, sociology, and psychology. Leveraging advanced text preprocessing techniques such as GloVe embeddings, coupled with robust classification algorithms like logistic regression, support vector machines, and random forests, we endeavor to decipher the textual cues indicative of gender identity. By examining user-generated content, encompassing both tweets and profile
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Impact Factor value: 8.226
Secondly, hate speech detection is crucial for promoting respectful and inclusive online environments. Hate speech, characterized by its harmful and discriminatory nature, undermines the principles of free expression and contributes to the proliferation of online toxicity. By
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