Story and essential meaning dynamics in Bangladesh's July 2024 Student-People's Uprising

TL;DR

This study analyzes over 50,000 YouTube comments to explore emotional and discourse dynamics during Bangladesh's July 2024 Student-People's Uprising, revealing shifts in sentiment, topics, and public perception over time.

Contribution

It introduces a comprehensive multi-method analysis of social media discourse during a political uprising, combining sentiment, emotion, topic, and lexical analyses with innovative measures like allotaxonometry.

Findings

Negative sentiment dominated during the movement.

Post-blackout happiness increased with positive language.

Discourse shifted from protest to justice themes.

Abstract

News media serves a crucial role in disseminating information and shaping public perception, especially during periods of political unrest. Using over 50,0000 YouTube comments on news coverage from July 16 to August 6, 2024, we investigate the emotional dynamics and evolving discourse of public perception during the July 2024 Student-People's Uprising in Bangladesh. Through integrated analyses of sentiment, emotion, topic, lexical discourse, timeline progression, sentiment shifts, and allotaxonometry, we show how negative sentiment dominated during the movement. We find a negative correlation between comment happiness and number of protest deaths $(r = -0.45,\p = 0.00)$. Using an ousiometer to measure essential meaning, we find public responses reflect a landscape of power, aggression, and danger, alongside persistent expressions of hope, moral conviction, and empowerment through goodnesses. Topic discourse progressed during the movement, with peaks in `Political Conflict', `Media Flow', and `Student Violence' during crisis surges, while topics like `Social Resistance' and `Digital Movement' persisted amid repression. Sentiment shifts reveal that after the second internet blackout, average happiness increased, driven by the more frequent use of positive words such as `victory', `peace' and `freedom' and a decrease in negative terms such as `death' and `lies'. Finally, through allotaxonometric analysis, we observe a clear shift from protest to justice.