LaTeX: Language Pattern-aware Triggering Event Detection for Adverse
  Experience during Pandemics
Kaiqun Fu Yangxiao Bai Weiwei Zhang Deepthi Kolady
The COVID-19 pandemic has accentuated socioeconomic disparities across
various racial and ethnic groups in the United States. While previous studies
have utilized traditional survey methods like the Household Pulse Survey (HPS)
to elucidate these disparities, this paper explores the role of social media
platforms in both highlighting and addressing these challenges. Drawing from
real-time data sourced from Twitter, we analyzed language patterns related to
four major types of adverse experiences: loss of employment income (LI), food
scarcity (FS), housing insecurity (HI), and unmet needs for mental health
services (UM). We first formulate a sparsity optimization problem that extracts
low-level language features from social media data sources. Second, we propose
novel constraints on feature similarity exploiting prior knowledge about the
similarity of the language patterns among the adverse experiences. The proposed
problem is challenging to solve due to the non-convexity objective and
non-smoothness penalties. We develop an algorithm based on the alternating
direction method of multipliers (ADMM) framework to solve the proposed
formulation. Extensive experiments and comparisons to other models on
real-world social media and the detection of adverse experiences justify the
efficacy of our model.