Syeda Mahwish


2024

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Using Daily Language to Understand Drinking: Multi-Level Longitudinal Differential Language Analysis
Matthew Matero | Huy Vu | August Nilsson | Syeda Mahwish | Young Min Cho | James McKay | Johannes Eichstaedt | Richard Rosenthal | Lyle Ungar | H. Andrew Schwartz
Proceedings of the 9th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2024)

Analyses for linking language with psychological factors or behaviors predominately treat linguistic features as a static set, working with a single document per person or aggregating across multiple posts (e.g. on social media) into a single set of features. This limits language to mostly shed light on between-person differences rather than changes in behavior within-person. Here, we collected a novel dataset of daily surveys where participants were asked to describe their experienced well-being and report the number of alcoholic beverages they had within the past 24 hours. Through this data, we first build a multi-level forecasting model that is able to capture within-person change and leverage both the psychological features of the person and daily well-being responses. Then, we propose a longitudinal version of differential language analysis that finds patterns associated with drinking more (e.g. social events) and less (e.g. task-oriented), as well as distinguishing patterns of heavy drinks versus light drinkers.

2023

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Transfer and Active Learning for Dissonance Detection: Addressing the Rare-Class Challenge
Vasudha Varadarajan | Swanie Juhng | Syeda Mahwish | Xiaoran Liu | Jonah Luby | Christian Luhmann | H. Andrew Schwartz
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

While transformer-based systems have enabled greater accuracies with fewer training examples, data acquisition obstacles still persist for rare-class tasks – when the class label is very infrequent (e.g. < 5% of samples). Active learning has in general been proposed to alleviate such challenges, but choice of selection strategy, the criteria by which rare-class examples are chosen, has not been systematically evaluated. Further, transformers enable iterative transfer-learning approaches. We propose and investigate transfer- and active learning solutions to the rare class problem of dissonance detection through utilizing models trained on closely related tasks and the evaluation of acquisition strategies, including a proposed probability-of-rare-class (PRC) approach. We perform these experiments for a specific rare-class problem: collecting language samples of cognitive dissonance from social media. We find that PRC is a simple and effective strategy to guide annotations and ultimately improve model accuracy while transfer-learning in a specific order can improve the cold-start performance of the learner but does not benefit iterations of active learning.