arXiv subjects

Mengyi Sun

Publications and source records attributed to Mengyi Sun.

6 recordsLinked to original sources

arXiv2026

DataParasite Enables Scalable and Repurposable Online Data Curation

Many questions in computational social science rely on datasets assembled from heterogeneous online sources, a process that is often labor-intensive, costly, and difficult to reproduce. Recent advances in large language models enable agentic search and structured extraction from the web, but existing systems are frequently opaque, inflexible, or poorly suited to scientific data curation. Here we introduce DataParasite, an open-source, modular pipeline for scalable online data collection. DataParasite decomposes tabular curation tasks into independent, entity-level searches defined through lightweight configuration files and executed through a shared, task-agnostic python script. Crucially, the same pipeline can be repurposed to new tasks, including those without predefined entity lists, using only natural-language instructions. We evaluate the pipeline on multiple canonical tasks in computational social science, including faculty hiring histories, elite death events, and political career trajectories. Across tasks, DataParasite achieves high accuracy while reducing data-collection costs by an order of magnitude relative to manual curation. By lowering the technical and labor barriers to online data assembly, DataParasite provides a practical foundation for scalable, transparent, and reusable data curation in computational social science and beyond.

cs.CL↗

arXiv2024

Evaluating authorship disambiguation quality through anomaly analysis on researchers' career transition

Authorship disambiguation is crucial for advancing studies in science of science. However, assessing the quality of authorship disambiguation in large-scale databases remains challenging since it is difficult to manually curate a gold-standard dataset that contains disambiguated authors. Through estimating the timing of when 5.8 million biomedical researchers became independent Principal Investigators (PIs) with authorship metadata extracted from the OpenAlex -- the largest open-source bibliometric database -- we unexpectedly discovered an anomaly: over 60% of researchers appeared as the last authors in their first career year. We hypothesized that this improbable finding results from poor name disambiguation, suggesting that such an anomaly may serve as an indicator of low-quality authorship disambiguation. Our findings indicated that authors who lack affiliation information, which makes it more difficult to disambiguate, were far more likely to exhibit this anomaly compared to those who included their affiliation information. In contrast, authors with Open Researcher and Contributor ID (ORCID) -- expected to have higher quality disambiguation -- showed significantly lower anomaly rates. We further applied this approach to examine the authorship disambiguation quality by gender over time, and we found that the quality of disambiguation for female authors was lower than that for male authors before 2010, suggesting that gender disparity findings based on pre-2010 data may require careful reexamination. Our results provide a framework for systematically evaluating authorship disambiguation quality in various contexts, facilitating future improvements in efforts to authorship disambiguation.

cs.DL↗

arXiv2024

Where there's a will there's a way: ChatGPT is used more for science in countries where it is prohibited

Regulating AI is a key societal challenge, but which regulation methods are effective is unclear. This study measures the effectiveness of restricting AI services geographically, focusing on ChatGPT. OpenAI restricts ChatGPT access in several countries, including China and Russia. If restrictions are effective, ChatGPT use should be minimal in these countries. We measured use with a classifier based on distinctive word usage found in early versions of ChatGPT, e.g. "delve." We trained the classifier on pre- and post-ChatGPT "polished" abstracts and found it outperformed GPTZero and ZeroGPT on validation sets, including papers with self-reported AI use. Applying the classifier to preprints from Arxiv, BioRxiv, and MedRxiv showed ChatGPT was used in about 12.6% of preprints by August 2023, with 7.7% higher usage in restricted countries. The gap appeared before China's first major legal LLM became widely available. To test the possibility that, due to high demand, use in restricted countries would have been even higher without restrictions, we compared Asian countries with high expected demand (where English is not an official language) and found that use was higher in those with restrictions. ChatGPT use was correlated with higher views and downloads, but not citations or journal placement. Overall, restricting ChatGPT geographically has proven ineffective in science and possibly other domains, likely due to widespread workarounds.

cs.DL↗

arXiv2023

CDSD: Chinese Dysarthria Speech Database

Dysarthric speech poses significant challenges for individuals with dysarthria, impacting their ability to communicate socially. Despite the widespread use of Automatic Speech Recognition (ASR), accurately recognizing dysarthric speech remains a formidable task, largely due to the limited availability of dysarthric speech data. To address this gap, we developed the Chinese Dysarthria Speech Database (CDSD), the most extensive collection of Chinese dysarthria data to date, featuring 133 hours of recordings from 44 speakers. Our benchmarks reveal a best Character Error Rate (CER) of 16.4\%. Compared to the CER of 20.45\% from our additional human experiments, Dysarthric Speech Recognition (DSR) demonstrates its potential in significant improvement of communication for individuals with dysarthria. The CDSD database will be made publicly available at http://melab.psych.ac.cn/CDSD.html.

cs.SD↗

arXiv2022

Team Power Dynamics and Team Impact: New Perspectives on Scientific Collaboration using Career Age as a Proxy for Team Power

Power dynamics influence every aspect of scientific collaboration. Team power dynamics can be measured by team power level and team power hierarchy. Team power level is conceptualized as the average level of the possession of resources, expertise, or decision-making authorities of a team. Team power hierarchy represents the vertical differences of the possessions of resources in a team. In Science of Science, few studies have looked at scientific collaboration from the perspective of team power dynamics. This research examines how team power dynamics affect team impact to fill the research gap. In this research, all co-authors of one publication are treated as one team. Team power level and team power hierarchy of one team are measured by the mean and Gini index of career age of co-authors in this team. Team impact is quantified by citations of a paper authored by this team. By analyzing over 7.7 million teams from Science (e.g., Computer Science, Physics), Social Sciences (e.g., Sociology, Library & Information Science), and Arts & Humanities (e.g., Art), we find that flat team structure is associated with higher team impact, especially when teams have high team power level. These findings have been repeated in all five disciplines except Art, and are consistent in various types of teams from Computer Science including teams from industry or academia, teams with different gender groups, teams with geographical contrast, and teams with distinct size.

physics.soc-ph↗

arXiv2021

Does double-blind peer-review reduce bias? Evidence from a top computer science conference

Peer review is widely regarded as essential for advancing scientific research. However, reviewers may be biased by authors' prestige or other characteristics. Double-blind peer review, in which the authors' identities are masked from the reviewers, has been proposed as a way to reduce reviewer bias. Although intuitive, evidence for the effectiveness of double-blind peer review in reducing bias is limited and mixed. Here, we examine the effects of double-blind peer review on prestige bias by analyzing the peer review files of 5027 papers submitted to the International Conference on Learning Representations (ICLR), a top computer science conference that changed its reviewing policy from single-blind peer review to double-blind peer review in 2018. We find that after switching to double-blind review, the scores given to the most prestigious authors significantly decreased. However, because many of these papers were above the threshold for acceptance, the change did not affect paper acceptance decisions significantly. Nevertheless, we show that double-blind peer review may have improved the quality of the selections by limiting other (non-author-prestige) biases. Specifically, papers rejected in the single-blind format are cited more than those rejected under the double-blind format, suggesting that double-blind review better identifies poorer quality papers. Interestingly, an apparently unrelated change - the change of rating scale from 10 to 4 points - likely reduced prestige bias significantly, to an extent that affected papers' acceptance. These results provide some support for the effectiveness of double-blind review in reducing prestige bias, while opening new research directions on the impact of peer review formats.

cs.CY↗