LaTeX: Language Pattern-aware Triggering Event Detection for Adverse Experience during Pandemics

TL;DR

This paper presents a novel language pattern-aware method for detecting adverse experiences during pandemics using social media data, specifically Twitter, to address limitations of traditional surveys.

Contribution

It introduces a sparsity optimization framework with novel constraints and an ADMM-based algorithm for real-time adverse experience detection from social media.

Findings

Effective detection of adverse experiences from Twitter data

Outperforms existing models in accuracy and robustness

Demonstrates real-time applicability during pandemics

Abstract

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.