Publications

You can also find my articles on my Google Scholar profile.

Journal Articles

Digital Epidemiology: Social Media Analysis for Insights into Epilepsy and Mental Health

Published in Journal of Computation Social Science (pre-print available), 2024

Social media platforms, particularly Reddits r/Epilepsy community, offer a unique perspective into the experiences of individuals with epilepsy (PWE) and their caregivers. This study analyzes 57k posts and 533k comments to explore key themes across demographics such as age, gender, and relationships. Our findings highlight significant discussions on epilepsy-related challenges, including depression (with 39.75% of posts indicating severe symptoms), driving restrictions, workplace concerns, and pregnancy-related issues in women with epilepsy. We introduce a novel engagement metric, F(P), which incorporates post length, sentiment scores, and readability to quantify community interaction. This analysis underscores the importance of integrated care addressing both neurological and mental health challenges faced by PWE. The insights from this study inform strategies for targeted support and awareness interventions.

Recommended citation: Your Name, You. (2010). "Paper Title Number 2." Journal 1. 1(2).
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Conference Papers

A Cognitive Analysis of CEO Speeches and Their Effects on Stock Markets

Published in Proceedings of 5th International Conference on Financial Technology, 2024

The cognitive state of a CEO can have a great impact on the company’s operational results and stock market performance. Conventional cognitive analysis often relies on interviews with cognitive scientists or psychologists, which are not readily scalable for big data applications in finance. In this work, we leverage a novel method to analyze the cognitive states of top-tier managers of 14 well-known companies. We analyze the concept mappings from their speeches and metaphorical expressions over 15 years. We also conduct breakdown analysis for the concept mappings, according to the trends of stock prices. We identify four distinct types of stock market performance and illustrate the featured concept mappings associated with each category. These representative concept mappings reflect the cognitive states of CEOs and provide insights into which cognitive states are most likely to correlate with positive stock market performance.

Recommended citation: Manro, R., Mao, R., Dahiya, L., Ma, Y., & Cambria, E. (2024). A cognitive analysis of CEO speeches and their effects on stock markets. In Proceedings of the 5th International Conference on Financial Technology, ICFT, Singapore.
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CogAI@ SMM4H 2024: Leveraging BERT-based Ensemble Models for Classifying Tweets on Developmental Disorders

Published in Proceedings of The 9th Social Media Mining for Health Research and Applications (SMM4H 2024) Workshop and Shared Tasks, 2024

This paper presents our work for the Task 5 of the Social Media Mining for Health Applications 2024 Shared Task-Binary classification of English tweets reporting children’s medical disorders. In this paper, we present and compare multiple approaches for automatically classifying tweets from parents based on whether they mention having a child with attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorders (ASD), delayed speech, or asthma. We use ensemble of various BERT-based models trained on provided dataset that yields an F1 score of 0.901 on the test data.

Recommended citation: Dahiya, L., & Bagga, R. (2024, August). CogAI@ SMM4H 2024: Leveraging BERT-based Ensemble Models for Classifying Tweets on Developmental Disorders. In Proceedings of The 9th Social Media Mining for Health Research and Applications (SMM4H 2024) Workshop and Shared Tasks (pp. 114-116).
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Robust Depth-Aided Segmentation for Drivable Region Detection in Challenging Environments

Published in ICRA 2024 Workshop on Resilient Off-road Autonomy, 2024

This paper proposes a method for detecting drivable regions in challenging terrains using RGB-D data. By integrating depth information with semantic segmentation, our approach significantly improves detection accuracy across diverse landscapes. Leveraging the SegFormer architecture, we effectively distinguish drivable from non-drivable areas. Additionally, we introduce a depth-based refinement mechanism to ensure reliable performance in real-world scenarios. Extensive evaluation in both off-road and on-road environments confirms the effectiveness of our approach. Using the SA-1B dataset with grounded SAM, our method achieves precise delineation of road classes during training. Overall, this work advances autonomous navigation systems by providing a comprehensive solution for drivable region detection in complex terrains in real time, even on edge computing devices.

Recommended citation: Ramtekkar, V. V., Dahiya, L., Shah, N., Nishimiya, K., Kuroki, T., Song, C., ... & Jeon, M. H. Robust Depth-Aided Segmentation for Drivable Region Detection in Challenging Environments. In ICRA 2024 Workshop on Resilient Off-road Autonomy.
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Liza Dahiya