A Statistical Analysis of Sentiment over Different Social Platforms on Drug Usage across High, Middle and Low-Income Countries

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Anureet Chhabra
Akash Sharma
Kunal Chhabra
Amit Chhabra


Social media serves as a platform for sharing information and connecting with others on various subjects, including healthcare and drugs. Analyzing drug sentiment from various social media like Twitter, reddit, quora etc are crucial for monitoring drug safety, identifying adverse reactions, and providing an early warning system for potential safety concerns, benefiting healthcare organizations and governments. This paper aims to study the opinions of people from all over the world by analyzing their messages, posts on social media. For this study 39,069 drug related message corpus were fetched making a comparison between the high income countries, and middle income and low income countries on the basis of drug consumption from 2021 to 2023. The dataset used for the study consisted of 41.63% text-corpus from high income countries out of which on an average from 2021 to 2023, 40.2% was found to have a positive sentiment. Whereas 34.65% of text-corpus are from middle-income countries out of which on an average 26.4% of was of a positive sentiment and 23.70% text-corpus are from low income countries with 23.6% having a positive sentiment. Furthermore, the primary factor for having such differences from people’s sentiment on drug consumption from high to low income countries includes Cultural and Social Norms, Legalization, availability etc therefore, In high-income countries, drug use is more socially accepted than in other regions. This proposed study gives an insight into people’s opinion on various drugs from different countries and regions. The results of this study attempted to understand how the public is responding to different types of information and to identify potential misinformation which can be used to formulate policies for existing and future drug prevention campaigns in order to improve public health and promote public education.

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Special Issue - Sentiment Analysis and Affective computing in Multimedia Data on Social Network