arXiv subjects

Melody Yu

Publications and source records attributed to Melody Yu.

5 recordsLinked to original sources

arXiv2026

Detecting and Enhancing Intellectual Humility in Online Political Discourse

Intellectual humility (IH)-a recognition of one's own intellectual limitations-can reduce polarization and foster more understanding across lines of difference. Yet little work explores how IH can be systematically defined, measured, evaluated, and enhanced in spaces that often lack it the most: online political discussions. In this paper, we seek to bridge these gaps by exploring two questions: 1) how might preexisting levels of IH influence future expressions of IH during online political discourse? and 2) can online interventions enhance IH across different political topics and conversational environments? To pursue these questions, we define a codebook characterizing different dimensions of IH and intellectual arrogance (IA) and have researchers use it to annotate several hundred Reddit posts, which we then use to develop and validate a classifier to support IH analysis at scale. These tools subsequently enable two key contributions: i) an observational data analysis of how IH varies across different political discussions on Reddit, which reveals that more/less IH environments tend to contain future posts of a similar nature, and ii) a randomized control trial evaluating strategies for nudging discussion participants to demonstrate more IH in their posts, which reveals the possibility of enhancing IH in online discussions across a range of contentious topics. Our findings highlight the possibility of measuring and increasing IH online without necessarily reducing engagement.

cs.CY↗

arXiv2024

The Computational Anatomy of Humility: Modeling Intellectual Humility in Online Public Discourse

The ability for individuals to constructively engage with one another across lines of difference is a critical feature of a healthy pluralistic society. This is also true in online discussion spaces like social media platforms. To date, much social media research has focused on preventing ills -- like political polarization and the spread of misinformation. While this is important, enhancing the quality of online public discourse requires not just reducing ills but also promoting foundational human virtues. In this study, we focus on one particular virtue: ``intellectual humility'' (IH), or acknowledging the potential limitations in one's own beliefs. Specifically, we explore the development of computational methods for measuring IH at scale. We manually curate and validate an IH codebook on 350 posts about religion drawn from subreddits and use them to develop LLM-based models for automating this measurement. Our best model achieves a Macro-F1 score of 0.64 across labels (and 0.70 when predicting IH/IA/Neutral at the coarse level), higher than an expected naive baseline of 0.51 (0.32 for IH/IA/Neutral) but lower than a human annotator-informed upper bound of 0.85 (0.83 for IH/IA/Neutral). Our results both highlight the challenging nature of detecting IH online -- opening the door to new directions in NLP research -- and also lay a foundation for computational social science researchers interested in analyzing and fostering more IH in online public discourse.

cs.CY↗

arXiv2024

SeeSay: An Assistive Device for the Visually Impaired Using Retrieval Augmented Generation

In this paper, we present SeeSay, an assistive device designed for individuals with visual impairments. This system leverages large language models (LLMs) for speech recognition and visual querying. It effectively identifies, records, and responds to the user's environment by providing audio guidance using retrieval-augmented generation (RAG). Our experiments demonstrate the system's capability to recognize its surroundings and respond to queries with audio feedback in diverse settings. We hope that the SeeSay system will facilitate users' comprehension and recollection of their surroundings, thereby enhancing their environmental perception, improving navigational capabilities, and boosting overall independence.

cs.HC↗

arXiv2024

Seagull: Privacy preserving network verification system

The Internet relies on routing protocols to direct traffic efficiently across interconnected networks, with the Border Gateway Protocol (BGP) serving as the core mechanism managing routing between autonomous systems. However, BGP configurations are largely manual, making them susceptible to human errors that can lead to outages or security vulnerabilities. Verifying the correctness and convergence of BGP configurations is therefore essential for maintaining a stable and secure Internet. Yet, this verification process faces two key challenges: preserving the privacy of proprietary routing information and ensuring scalability across large, distributed networks. This paper introduces a privacy-preserving verification framework that leverages multiparty computation (MPC) to validate BGP configurations without exposing sensitive routing data. Our approach overcomes both privacy and scalability challenges by ensuring that no information beyond the verification outcome is revealed. Through formal analysis, we show that the proposed method achieves strong privacy guarantees and practical scalability, providing a secure and efficient foundation for verifying BGP-based routing in the Internet backbone.

cs.CR↗

arXiv2023

Decoding the Popularity of TV Series: A Network Analysis Perspective

In this paper, we analyze the character networks extracted from three popular television series and explore the relationship between a TV show episode's character network metrics and its review from IMDB. Character networks are graphs created from the plot of a TV show that represents the interactions of characters in scenes, indicating the presence of a connection between them. We calculate various network metrics for each episode, such as node degree and graph density, and use these metrics to explore the potential relationship between network metrics and TV series reviews from IMDB. Our results show that certain network metrics of character interactions in episodes have a strong correlation with the review score of TV series. Our research aims to provide more quantitative information that can help TV producers understand how to adjust the character dynamics of future episodes to appeal to their audience. By understanding the impact of character interactions on audience engagement and enjoyment, producers can make informed decisions about the development of their shows.

cs.SI↗