The Spatial Distribution of Sexual Harassment Sentiment in Twitter

Abstract

In a cultural landscape where discussions about sexual harassment are often considered taboo, this study leverages social media as a platform for the anonymous sharing of experiences related to sexual harassment in Asia. By focusing on Kuala Lumpur, we applied grid-based data mining to analyze the spatial distribution of tweets related to sexual harassment, revealing significant distribution patterns. Moreover, this study utilized two advanced sentiment analysis methods: the Natural Language Toolkit (NLTK) and Azure Machine Learning (AML), to evaluate the polarity of sentiments expressed in these tweets. The findings demonstrate a notable variance in sentiment analysis results between NLTK and AML, with NLTK classifying a majority of tweets as neutral (63.7%), while AML identified a predominant positive sentiment (70%). This discrepancy highlights the complexities of sentiment analysis and the importance of selecting appropriate tools for specific research contexts. In examining the spatial distribution, it becomes apparent that tweets collected through the identified keywords are dispersed within the boundaries of Kuala Lumpur. However, there is a notable concentration in specific areas, particularly evident in the hotspot encompassing Taman Desa and Pantai Dalam. Understanding the sentiment's location enables us to delve deeper into the pronounced cluster of tweets, examining potential correlations with surrounding social and moral issues. This insight empowers us to address the issue through various means, including the implementation of Crime Prevention through Environmental Design and fostering community-based initiatives.