  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,
especially when a country is facing an unprecedented healthcare crisis, fuels
further deterioration of relations. In this paper, we define a new task of
detecting \emph{supportive} content and demonstrate that existing \emph{NLP for
social impact} tools can be effectively harnessed for such tasks within a quick
turnaround time. We also release the first publicly available data set at the
intersection of geopolitical relations and a raging pandemic in the context of
India and Pakistan.
