TL;DR
This paper introduces a new task of detecting supportive social media content during international crises, demonstrating the effectiveness of NLP tools and releasing a relevant dataset focused on India-Pakistan relations during COVID-19.
Contribution
It defines the task of identifying supportive content in geopolitical social media, applies NLP tools effectively, and releases a novel dataset for this context.
Findings
Existing NLP tools can effectively detect supportive content.
Supportive hashtags like #IndiaNeedsOxygen are prominent in Pakistan.
Divisive hashtags such as #EndiaSaySorryToKashmir also trend.
Abstract
The ongoing COVID-19 pandemic resulted in significant ramifications for international relations ranging from travel restrictions, global ceasefires, and international vaccine production and sharing agreements. Amidst a wave of infections in India that resulted in a systemic breakdown of healthcare infrastructure, a social welfare organization based in Pakistan offered to procure medical-grade oxygen to assist India -- a nation which was involved in four wars with Pakistan in the past few decades. In this paper, we focus on Pakistani Twitter users' response to the ongoing healthcare crisis in India. While #IndiaNeedsOxygen and #PakistanStandsWithIndia featured among the top-trending hashtags in Pakistan, divisive hashtags such as #EndiaSaySorryToKashmir simultaneously started trending. Against the backdrop of a contentious history including four wars, divisive content of this nature,…
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