Zobrazeno 1 - 10
of 33
pro vyhledávání: '"Rezapour, Rezvaneh"'
Social interactions promote well-being, yet challenges like geographic distance and mental health conditions can limit in-person engagement. Advances in AI agents are transferring communication, particularly in mental health, where AI chatbots provid
Externí odkaz:
http://arxiv.org/abs/2409.15550
Words Matter: Reducing Stigma in Online Conversations about Substance Use with Large Language Models
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 discour
Externí odkaz:
http://arxiv.org/abs/2408.07873
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 insi
Externí odkaz:
http://arxiv.org/abs/2406.12117
Structural balance theory predicts that triads in networks gravitate towards stable configurations. The theory has been verified for undirected graphs. Since real-world networks are often directed, we introduce a novel method for considering both tra
Externí odkaz:
http://arxiv.org/abs/2405.02798
In the rapidly evolving landscape of computing disciplines, substantial efforts are being dedicated to unraveling the sociotechnical implications of generative AI (Gen AI). While existing research has manifested in various forms, there remains a nota
Externí odkaz:
http://arxiv.org/abs/2405.00995
Large language models (LLMs) have brought breakthroughs in tasks including translation, summarization, information retrieval, and language generation, gaining growing interest in the CHI community. Meanwhile, the literature shows researchers' controv
Externí odkaz:
http://arxiv.org/abs/2309.14504
To foster collaboration and inclusivity in Open Source Software (OSS) projects, it is crucial to understand and detect patterns of toxic language that may drive contributors away, especially those from underrepresented communities. Although machine l
Externí odkaz:
http://arxiv.org/abs/2307.15631
The media's representation of illicit substance use can lead to harmful stereotypes and stigmatization for individuals struggling with addiction, ultimately influencing public perception, policy, and public health outcomes. To explore how the discour
Externí odkaz:
http://arxiv.org/abs/2307.01299
This paper contains the description of our submissions to the summarization task of the Podcast Track in TREC (the Text REtrieval Conference) 2020. The goal of this challenge was to generate short, informative summaries that contain the key informati
Externí odkaz:
http://arxiv.org/abs/2104.03343
Autor:
Reddy, Sravana, Yu, Yongze, Pappu, Aasish, Sivaraman, Aswin, Rezapour, Rezvaneh, Jones, Rosie
Podcast episodes often contain material extraneous to the main content, such as advertisements, interleaved within the audio and the written descriptions. We present classifiers that leverage both textual and listening patterns in order to detect suc
Externí odkaz:
http://arxiv.org/abs/2103.02585