arXiv subjects

Ly Dinh

Publications and source records attributed to Ly Dinh.

8 recordsLinked to original sources

arXiv2025

Instilling Doubts About Truth: Measuring the Impact of Tucker Carlson's Interview with Vladimir Putin Using Machine Learning and Natural Language Processing

On February 7, 2024, Russian President Vladimir Putin gave a two-hour interview with conservative political commentator, Tucker Carlson. This study investigated the impact of the Carlson- Putin interview on the US X audience. We proposed a framework of social media impact using machine learning (ML) and natural language processing (NLP) by measuring changes in audience, structure, and content. Triangulation methods were used to validate the process and results. The interview had a considerable impact among segments of the American public: 1) the reach and engagement of far-right influencers increased after the interview, suggesting Kremlin narratives gained traction within these circles, 2) the communication structure became more vulnerable to disinformation spread after the interview, and 3) the public discourse changed from support for Ukraine funding to conversations about Putin, Russia, and the issue of "truth" or the veracity of Putin's claims. This research contributes to methods development for social media studies and aids scholars in analyzing how public opinion shapes policy debates. The Carlson-Putin interview sparked a broader discussion about truth-telling. Far from being muted, the broad impact of the interview appears considerable and poses challenges for foreign affairs leaders who depend on public support and buy-in when formulating national policy.

cs.SI↗

arXiv2024

Academic collaboration on large language model studies increases overall but varies across disciplines

Interdisciplinary collaboration is crucial for addressing complex scientific challenges. Recent advancements in large language models (LLMs) have shown significant potential in benefiting researchers across various fields. To explore their potential for interdisciplinary collaboration, we collect and analyze data from OpenAlex, an open-source academic database. Our dataset comprises 59,293 LLM-related papers, along with 70,945 machine learning (ML) papers and 73,110 papers from non-LLM/ML fields as control groups. We first employ Shannon Entropy to assess the diversity of collaboration. Our results reveal that many fields have exhibited a more significant increasing trend following the release of ChatGPT as compared to the control groups. In particular, Computer Science and Social Science display a consistent increase in both institution and department entropy. Other fields such as Decision Science, Psychology, and Health Professions have shown minor to significant increases. Our difference-in-difference analysis also indicates that the release of ChatGPT leads to a statistically significant increase in collaboration in several fields, such as Computer Science and Social Science. In addition, we analyze the author networks and find that Computer Science, Medicine, and other Computer Science-related departments are the most prominent. Regarding authors' institutions, our analysis reveals that entities such as Stanford University, Harvard University, and University College London are key players, either dominating centrality or playing crucial roles in connecting research networks. Overall, this study provides valuable information on the current landscape and evolving dynamics of collaboration networks in LLM research. It also suggests potential areas for fostering more diverse collaborations and highlights the need for continued research on the impact of LLMs on scientific practices.

cs.SI↗

arXiv2024

Crowdsourced reviews reveal substantial disparities in public perceptions of parking

Due to increased reliance on private vehicles and growing travel demand, parking remains a longstanding urban challenge globally. Quantifying parking perceptions is paramount as it enables decision-makers to identify problematic areas and make informed decisions on parking management. This study introduces a cost-effective and widely accessible data source, crowdsourced online reviews, to investigate public perceptions of parking across the U.S. Specifically, we examine 4,987,483 parking-related reviews for 1,129,460 points of interest (POIs) across 911 core-based statistical areas (CBSAs) sourced from Google Maps. We employ the Bidirectional Encoder Representations from Transformers (BERT) model to classify the parking sentiment and conduct regression analyses to explore its relationships with socio-spatial factors. Findings reveal significant variations in parking sentiment across POI types and CBSAs, with Restaurant POIs showing the most negative. Regression results further indicate that denser urban areas with higher proportions of African Americans and Hispanics and lower socioeconomic status are more likely to exhibit negative parking sentiment. Interestingly, an opposite relationship between parking supply and sentiment is observed, indicating increasing supply does not necessarily improve parking experiences. Finally, our textual analysis identifies keywords associated with positive or negative sentiments and highlights disparities between urban and rural areas. Overall, this study demonstrates the potential of a novel data source and methodological framework in measuring parking sentiment, offering valuable insights that help identify hyperlocal parking issues and guide targeted parking management strategies.

cs.CY↗

arXiv2024

Structural Balance in Real-World Social Networks: Incorporating Direction and Transitivity in Measuring Partial Balance

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 transitivity and sign consistency for evaluating partial balance in signed digraphs. We test our approach on graphs constructed by using different methods for identifying edge signs: natural language processing to infer signs from underlying text data, and self-reported survey data. Our results show that for various social contexts and edge sign detection methods, partial balance of these digraphs are moderately high, ranging from 61% to 96%. Our approach not only enhances the theoretical framework of structural balance but also provides practical insights into the stability of social networks, enabling a deeper understanding of interpersonal and group dynamics across different communication platforms.

cs.SI↗

arXiv2023

Hyperauthored papers disproportionately amplify important egocentric network metrics

Hyperauthorship, a phenomenon whereby there are a disproportionately large number of authors on a single paper, is increasingly common in several scientific disciplines, but with unknown consequences for network metrics used to study scientific collaboration. The validity of co-authorship as a proxy for scientific collaboration is affected by this. Using bibliometric data from publications in the field of genomics, we examine the impact of hyperauthorship on metrics of scientific collaboration, and propose a method to determine a suitable cutoff threshold for hyperauthored papers and compare co-authorship networks with and without hyperauthored works. Our analysis reveals that including hyperauthored papers dramatically impacts the structural positioning of central authors and the topological characteristics of the network, while producing small influences on whole-network cohesion measures. We present two solutions to minimize the impact of hyperauthorship: using a mathematically grounded and reproducible calculation of threshold cutoff to exclude hyperauthored papers or fractional counting to weight network results. Our findings affirm the structural influences of hyperauthored papers and suggest that scholars should be mindful when using co-authorship networks to study scientific collaboration.

cs.DL↗

arXiv2020

Multilevel Structural Evaluation of Signed Directed Social Networks based on Balance Theory

Balance theory explains the forces behind the structure of social systems, which are commonly modeled as static undirected signed networks. We expand this modeling approach to incorporate directionality of edges, and consider three levels of analysis: triads, subgroups, and the whole network. For triad-level balance, we operationalize a new measure by utilizing semicycles that satisfy the condition of transitivity. For subgroup-level balance, we propose measures of cohesiveness (intra-group solidarity) and divisiveness (inter-group antagonism) to capture balance within and among subgroups of the network using the most fitting partition of nodes into two groups. For network-level balance, we re-purpose the normalized line index to incorporate directionality, and provide the proportion of edges whose position suits balance. Through extensive computational analysis, we quantify and analyze patterns of social structure in triads, subgroups, and the whole network across a range of social settings from college students and Wikipedia editors to philosophers and Bitcoin traders. We then apply our multilevel framework of analysis to examine balance in temporal and multilayer networks, which demonstrates the generalizability of our approach to evaluating balance, and leads to new observations on balance with respect to time and layer dimensions. Our complementary findings on a variety of social networks highlight the need to evaluate balance at different levels. We propose a comprehensive yet parsimonious approach to address this need.

cs.SI↗

arXiv2020

Structural balance in signed digraphs: considering transitivity to measure balance in graphs constructed by using different link signing methods

Structural balance theory assumes triads in networks to 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 transitivity and sign consistency for calculating balance in signed digraphs. We test our approach on graphs that we constructed by using different methods for identifying edge signs: natural language processing to infer signs from underlying text data, and self-reported survey data. Our results show that for various social contexts and edge sign detection methods, balance is moderately high, ranging from 67.5% to 92.4%.

cs.SI↗

arXiv2020

An Empirical Methodology for Detecting and Prioritizing Needs during Crisis Events

In times of crisis, identifying the essential needs is a crucial step to providing appropriate resources and services to affected entities. Social media platforms such as Twitter contain vast amount of information about the general public's needs. However, the sparsity of the information as well as the amount of noisy content present a challenge to practitioners to effectively identify shared information on these platforms. In this study, we propose two novel methods for two distinct but related needs detection tasks: the identification of 1) a list of resources needed ranked by priority, and 2) sentences that specify who-needs-what resources. We evaluated our methods on a set of tweets about the COVID-19 crisis. For task 1 (detecting top needs), we compared our results against two given lists of resources and achieved 64% precision. For task 2 (detecting who-needs-what), we compared our results on a set of 1,000 annotated tweets and achieved a 68% F1-score.

cs.CL↗