Zobrazeno 1 - 10
of 404
pro vyhledávání: '"GUO, Siyi"'
Quantifying the effect of textual interventions in social systems, such as reducing anger in social media posts to see its impact on engagement, poses significant challenges. Direct interventions on real-world systems are often infeasible, necessitat
Externí odkaz:
http://arxiv.org/abs/2410.21474
Social scientists use surveys to probe the opinions and beliefs of populations, but these methods are slow, costly, and prone to biases. Recent advances in large language models (LLMs) enable the creating of computational representations or "digital
Externí odkaz:
http://arxiv.org/abs/2406.12074
Effective communication during health crises is critical, with social media serving as a key platform for public health experts (PHEs) to engage with the public. However, it also amplifies pseudo-experts promoting contrarian views. Despite its import
Externí odkaz:
http://arxiv.org/abs/2406.01866
User representation learning aims to capture user preferences, interests, and behaviors in low-dimensional vector representations. These representations have widespread applications in recommendation systems and advertising; however, existing methods
Externí odkaz:
http://arxiv.org/abs/2405.05275
Publikováno v:
JMIR Medical Informatics, Vol 9, Iss 3, p e25704 (2021)
BackgroundPressure injury (PI) is a common and preventable problem, yet it is a challenge for at least two reasons. First, the nurse shortage is a worldwide phenomenon. Second, the majority of nurses have insufficient PI-related knowledge. Machine le
Externí odkaz:
https://doaj.org/article/bbe22ddfbb034248b4911076b858f235
Language models (LMs) are known to represent the perspectives of some social groups better than others, which may impact their performance, especially on subjective tasks such as content moderation and hate speech detection. To explore how LMs repres
Externí odkaz:
http://arxiv.org/abs/2402.11114
Recent advances in NLP have improved our ability to understand the nuanced worldviews of online communities. Existing research focused on probing ideological stances treats liberals and conservatives as separate groups. However, this fails to account
Externí odkaz:
http://arxiv.org/abs/2402.01091
The rich and dynamic information environment of social media provides researchers, policy makers, and entrepreneurs with opportunities to learn about social phenomena in a timely manner. However, using these data to understand social behavior is diff
Externí odkaz:
http://arxiv.org/abs/2401.06275
Social media platforms are rife with politically charged discussions. Therefore, accurately deciphering and predicting partisan biases using Large Language Models (LLMs) is increasingly critical. In this study, we address the challenge of understandi
Externí odkaz:
http://arxiv.org/abs/2311.09687
Narrative is a foundation of human cognition and decision making. Because narratives play a crucial role in societal discourses and spread of misinformation and because of the pervasive use of social media, the narrative dynamics on social media can
Externí odkaz:
http://arxiv.org/abs/2307.08541