Publications

2025

1. The Evolving Landscape of Youth Online Safety: Insights from News Media Analysis

   Mohammad (Matt) Namvarpour, Elham Aghakhani, Michael D Ekstrand, Rezvaneh Rezapour, and 1 more author

   In Proceedings of the 17th ACM Web Science Conference 2025 2025

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   There have been various efforts to understand how youth online safety has been reflected in the news, as news play an important role in shaping public opinions. However, these efforts focused on specific contexts, such as individual countries and specific online risk types. Therefore, there is a need for a holistic view of understanding trends in the news regarding stakeholders involved and various ranges of online risks. In this work, we seek to understand how discussions of online safety for youth has evolved in news publications over the last two decades. We applied quantitative media content analysis and sentiment analysis to 3.9K English-language news articles from 2002–2024, documenting shifts in the portrayal of key stakeholders. Our results showed increased media focus on technology companies and government in youth safety discussions, particularly highlighting cyberbullying as a key risk. We found a generally negative trend in the sentiment toward the perceived safety of youth online, which fluctuates based on societal concerns and policy changes. The significance of this work lies in its analysis of how media discourse has illuminated public perceptions and policy directions concerning youth safety in digital spaces. Content Warning: This paper discusses sensitive topics, such as sex and child harassment, which may be triggering.

2. From Conversation to Automation: Leveraging LLMs for Problem-Solving Therapy Analysis

   Elham Aghakhani, Lu Wang, Karla T. Washington, George Demiris, and 2 more authors

   In Findings of the Association for Computational Linguistics: ACL 2025 Jul 2025

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   Problem-Solving Therapy (PST) is a structured psychological approach that helps individuals manage stress and resolve personal issues by guiding them through problem identification, solution brainstorming, decision-making, and outcome evaluation. As mental health care increasingly adopts technologies like chatbots and large language models (LLMs), it is important to thoroughly understand how each session of PST is conducted before attempting to automate it. We developed a comprehensive framework for PST annotation using established PST Core Strategies and a set of novel Facilitative Strategies to analyze a corpus of real-world therapy transcripts to determine which strategies are most prevalent. Using various LLMs and transformer-based models, we found that GPT-4o outperformed all models, achieving the highest accuracy (0.76) in identifying all strategies. To gain deeper insights, we examined how strategies are applied by analyzing Therapeutic Dynamics (autonomy, self-disclosure, and metaphor), and linguistic patterns within our labeled data. Our research highlights LLMs’ potential to automate therapy dialogue analysis, offering a scalable tool for mental health interventions. Our framework enhances PST by improving accessibility, effectiveness, and personalized support for therapists.

2024

1. Words Matter: Reducing Stigma in Online Conversations about Substance Use with Large Language Models

   Layla Bouzoubaa, Elham Aghakhani, and Rezvaneh Rezapour

   In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing Nov 2024

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   Stigma is a barrier to treatment for individuals struggling with substance use disorders (SUD), which leads to significantly lower treatment engagement rates. With only 7% of those affected receiving any form of help, societal stigma not only discourages individuals with SUD from seeking help but isolates them, hindering their recovery journey and perpetuating a cycle of shame and self-doubt. This study investigates how stigma manifests on social media, particularly Reddit, where anonymity can exacerbate discriminatory behaviors. We analyzed over 1.2 million posts, identifying 3,207 that exhibited stigmatizing language related to people who use substances (PWUS). Of these, 1,649 posts were classified as containing directed stigma towards PWUS, which became the focus of our de-stigmatization efforts. Using Informed and Stylized LLMs, we developed a model to transform these instances into more empathetic language.Our paper contributes to the field by proposing a computational framework for analyzing stigma and de-stigmatizing online content, and delving into the linguistic features that propagate stigma towards PWUS. Our work not only enhances understanding of stigma’s manifestations online but also provides practical tools for fostering a more supportive environment for those affected by SUD.

2. Decoding the Narratives: Analyzing Personal Drug Experiences Shared on Reddit

   Layla Bouzoubaa, Elham Aghakhani, Max Song, Quang Trinh, and 1 more author

   In Findings of the Association for Computational Linguistics ACL 2024 Aug 2024

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   Online communities such as drug-related subreddits serve as safe spaces for people who use drugs (PWUD), fostering discussions on substance use experiences, harm reduction, and addiction recovery. Users’ shared narratives on these forums provide insights into the likelihood of developing a substance use disorder (SUD) and recovery potential. Our study aims to develop a multi-level, multi-label classification model to analyze online user-generated texts about substance use experiences. For this purpose, we first introduce a novel taxonomy to assess the nature of posts, including their intended connections (Inquisition or Disclosure), subjects (e.g., Recovery, Dependency), and specific objectives (e.g., Relapse, Quality, Safety). Using various multi-label classification algorithms on a set of annotated data, we show that GPT-4, when prompted with instructions, definitions, and examples, outperformed all other models. We apply this model to label an additional 1,000 posts and analyze the categories of linguistic expression used within posts in each class. Our analysis shows that topics such as Safety, Combination of Substances, and Mental Health see more disclosure, while discussions about physiological Effects focus on harm reduction. Our work enriches the understanding of PWUD’s experiences and informs the broader knowledge base on SUD and drug use.