Authors - Ayushi Chapate, Reena S. Satpute Abstract - Natural language helps us to interact with the computer through human language. This article investigates how Natural Language Processing (NLP) can enhance our understanding of social media changes. To its audience, social media provides a large - arguably unlimited - and otherwise untapped linguistic re-source, revealing information about government behavior, civic participation, in-dividual mental well-being, and consumption behavior, among many other things. Using machine learning analytical methods such as sentiment analysis, topic modeling, stance detection, and misinformation tracking, researchers can begin to study the social, psychological, and economic implications of web-based inter-action. In terms of civic and political implications, to analyze user-generated con-tent, discourse networks, and hashtags using NLP applications can produced new insights into online mobilization and collective action. For example, researchers studying the political movement’s #MeToo and #BlackLivesMatter, based on analysis of Twitter data, have employed topic modeling techniques to reveal their influence and significance in innovative ways. From a psychological perspective, NLP methods make it possible to examine prevalent mental health indicators across separated populations, through the analysis of emotional tone, pronoun use, and distress markers. In studies conducted between 2020–2025, the application of BERT based embedding models were found to detect online indicators of depression, anxiety, and social comparison leverage's based on word meaning. Further, understanding the depth of these psychological consequences remains nebulous and limited to a range of social categories in the digital landscape, similar to previous notions of 'self-checking' across the digital commons exploring citizen engagement.
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