arXiv subjects

Libby Hemphill

Publications and source records attributed to Libby Hemphill.

At least 19 recordsLinked to original sources

arXiv2026

Reducing the rate of personal insults in social media with bystander bots

Prompted by previous research on strategies for reducing interpersonal conflict and addressing problematic behaviors in online communities, a randomized controlled trial on Reddit compared various responses for reducing the rate of personal insults users post to the site. We generated replies from five deescalation strategies and used an automated procedure for posting them as replies to insulting comments. The findings reveal that automated replies to insults can effectively reduce their rate. Appreciation performed best. Not all strategies performed well, though. We conclude that automated responses are a viable tool for addressing some problematic behaviors. We discuss their potential utility and limitations.

cs.SI↗

arXiv2025

The Effects of Moral Framing on Online Fundraising Outcomes: Evidence from GoFundMe Campaigns

This study examines the impact of moral framing on fundraising outcomes, including both monetary and social support, by analyzing a dataset of 14,088 campaigns posted on GoFundMe. We focused on three moral frames: care, fairness, and (ingroup) loyalty, and measured their presence in campaign appeals. Our results show that campaigns in the Emergency category are most influenced by moral framing. Generally, negatively framing appeals by emphasizing harm and unfairness effectively attracts more donations and comments from supporters. However, this approach can have a downside, as it may lead to a decrease in the average donation amount per donor. Additionally, we found that loyalty framing was positively associated with receiving more donations and messages across all fundraising categories. This research extends existing literature on framing and communication strategies related to fundraising and their impact. We also propose practical implications for designing features of online fundraising platforms to better support both fundraisers and supporters.

cs.CY↗

arXiv2025

Evaluating how LLM annotations represent diverse views on contentious topics

Researchers have proposed the use of generative large language models (LLMs) to label data for research and applied settings. This literature emphasizes the improved performance of these models relative to other natural language models, noting that generative LLMs typically outperform other models and even humans across several metrics. Previous literature has examined bias across many applications and contexts, but less work has focused specifically on bias in generative LLMs' responses to subjective annotation tasks. This bias could result in labels applied by LLMs that disproportionately align with majority groups over a more diverse set of viewpoints. In this paper, we evaluate how LLMs represent diverse viewpoints on these contentious tasks. Across four annotation tasks on four datasets, we show that LLMs do not show systematic substantial disagreement with annotators on the basis of demographics. Rather, we find that multiple LLMs tend to be biased in the same directions on the same demographic categories within the same datasets. Moreover, the disagreement between human annotators on the labeling task -- a measure of item difficulty -- is far more predictive of LLM agreement with human annotators. We conclude with a discussion of the implications for researchers and practitioners using LLMs for automated data annotation tasks. Specifically, we emphasize that fairness evaluations must be contextual, model choice alone will not solve potential issues of bias, and item difficulty must be integrated into bias assessments.

cs.CL↗

arXiv2025

The Spread of Virtual Gifting in Live Streaming: The Case of Twitch

This paper examines how gifting spreads among viewers on Twitch, one of the largest live streaming platforms worldwide. Twitch users can give gift subscriptions to other viewers in the chat room, with the majority of gifters opting for community gifting, which is gifting to randomly selected viewers. We identify the random nature of gift-receiving in our data as a natural experiment setting. We investigate whether gift recipients pay it forward, considering various gift types that may either promote or deter the spread of gifting. Our findings reveal that Twitch viewers who receive gift subscriptions are generally more likely to pay it forward than non-recipients, and the positive impact of gift-receiving becomes stronger when the recipient is the sole beneficiary of the giver's gifting behavior. However, we found that gifts from frequent gifters discourage recipients from paying it forward, and gifts from anonymous gifters do not influence the likelihood of viewers becoming future gifters. This research contributes to the existing literature on the spread of online prosocial behavior by providing robust evidence and suggests practical strategies for promoting online gifting.

cs.CY↗

arXiv2024

Toward satisfactory public accessibility: A crowdsourcing approach through online reviews to inclusive urban design

As urban populations grow, the need for accessible urban design has become urgent. Traditional survey methods for assessing public perceptions of accessibility are often limited in scope. Crowdsourcing via online reviews offers a valuable alternative to understanding public perceptions, and advancements in large language models can facilitate their use. This study uses Google Maps reviews across the United States and fine-tunes Llama 3 model with the Low-Rank Adaptation technique to analyze public sentiment on accessibility. At the POI level, most categories -- restaurants, retail, hotels, and healthcare -- show negative sentiments. Socio-spatial analysis reveals that areas with higher proportions of white residents and greater socioeconomic status report more positive sentiment, while areas with more elderly, highly-educated residents exhibit more negative sentiment. Interestingly, no clear link is found between the presence of disabilities and public sentiments. Overall, this study highlights the potential of crowdsourcing for identifying accessibility challenges and providing insights for urban planners.

cs.SI↗

arXiv2024

Characterizing Online Toxicity During the 2022 Mpox Outbreak: A Computational Analysis of Topical and Network Dynamics

Background: Online toxicity, encompassing behaviors such as harassment, bullying, hate speech, and the dissemination of misinformation, has become a pressing social concern in the digital age. The 2022 Mpox outbreak, initially termed "Monkeypox" but subsequently renamed to mitigate associated stigmas and societal concerns, serves as a poignant backdrop to this issue. Objective: In this research, we undertake a comprehensive analysis of the toxic online discourse surrounding the 2022 Mpox outbreak. Our objective is to dissect its origins, characterize its nature and content, trace its dissemination patterns, and assess its broader societal implications, with the goal of providing insights that can inform strategies to mitigate such toxicity in future crises. Methods: We collected more than 1.6 million unique tweets and analyzed them from five dimensions, including context, extent, content, speaker, and intent. Utilizing BERT-based topic modeling and social network community clustering, we delineated the toxic dynamics on Twitter. Results: We identified five high-level topic categories in the toxic online discourse on Twitter, including disease (46.6%), health policy and healthcare (19.3%), homophobia (23.9%), politics (6.0%), and racism (4.1%). Through the toxicity diffusion networks of mentions, retweets, and the top users, we found that retweets of toxic content were widespread, while influential users rarely engaged with or countered this toxicity through retweets. Conclusions: By tracking topical dynamics, we can track the changing popularity of toxic content online, providing a better understanding of societal challenges. Network dynamics spotlight key social media influencers and their intents, indicating that addressing these central figures in toxic discourse can enhance crisis communication and inform policy-making.

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

Prompt Design Matters for Computational Social Science Tasks but in Unpredictable Ways

Manually annotating data for computational social science tasks can be costly, time-consuming, and emotionally draining. While recent work suggests that LLMs can perform such annotation tasks in zero-shot settings, little is known about how prompt design impacts LLMs' compliance and accuracy. We conduct a large-scale multi-prompt experiment to test how model selection (ChatGPT, PaLM2, and Falcon7b) and prompt design features (definition inclusion, output type, explanation, and prompt length) impact the compliance and accuracy of LLM-generated annotations on four CSS tasks (toxicity, sentiment, rumor stance, and news frames). Our results show that LLM compliance and accuracy are highly prompt-dependent. For instance, prompting for numerical scores instead of labels reduces all LLMs' compliance and accuracy. The overall best prompting setup is task-dependent, and minor prompt changes can cause large changes in the distribution of generated labels. By showing that prompt design significantly impacts the quality and distribution of LLM-generated annotations, this work serves as both a warning and practical guide for researchers and practitioners.

cs.AI↗

arXiv2024

A scoping review of using Large Language Models (LLMs) to investigate Electronic Health Records (EHRs)

Electronic Health Records (EHRs) play an important role in the healthcare system. However, their complexity and vast volume pose significant challenges to data interpretation and analysis. Recent advancements in Artificial Intelligence (AI), particularly the development of Large Language Models (LLMs), open up new opportunities for researchers in this domain. Although prior studies have demonstrated their potential in language understanding and processing in the context of EHRs, a comprehensive scoping review is lacking. This study aims to bridge this research gap by conducting a scoping review based on 329 related papers collected from OpenAlex. We first performed a bibliometric analysis to examine paper trends, model applications, and collaboration networks. Next, we manually reviewed and categorized each paper into one of the seven identified topics: named entity recognition, information extraction, text similarity, text summarization, text classification, dialogue system, and diagnosis and prediction. For each topic, we discussed the unique capabilities of LLMs, such as their ability to understand context, capture semantic relations, and generate human-like text. Finally, we highlighted several implications for researchers from the perspectives of data resources, prompt engineering, fine-tuning, performance measures, and ethical concerns. In conclusion, this study provides valuable insights into the potential of LLMs to transform EHR research and discusses their applications and ethical considerations.

cs.ET↗

arXiv2024

Crowdsourcing public attitudes toward local services through the lens of Google Maps reviews: An urban density-based perspective

Understanding how urban density impacts public perceptions of urban service is important for informing livable, accessible, and equitable urban planning. Conventional methods such as surveys are limited by their sampling scope, time efficiency, and expense. On the other hand, crowdsourcing through online platforms presents an opportunity for decision-makers to tap into a user-generated source of information that is widely available and cost-effective. To demonstrate such potential, we collect Google Maps reviews for 23,906 points of interest (POIs) in Atlanta, Georgia. Next, we use the Bidirectional Encoder Representations from Transformers (BERT) model to classify reviewers' attitudes toward urban density and the Robustly Optimized BERT approach (RoBERTa) to compute sentiment. Finally, a partial least squares regression is fitted to examine the relationships between average sentiment and socio-spatial factors. The findings reveal areas in Atlanta with predominantly negative sentiments toward urban density and highlight the variation in sentiment distribution across different POIs. Further, the regression analysis reveals that minority and low-income communities often express more negative sentiments, and higher land use density exacerbates such negativity. This study introduces a novel data source and methodological framework that can be easily adapted to different regions, offering useful insights into public sentiment toward the built environment and shedding light on how planning policies can be designed to handle related challenges.

cs.SI↗

arXiv2024

War and Peace (WarAgent): Large Language Model-based Multi-Agent Simulation of World Wars

Can we avoid wars at the crossroads of history? This question has been pursued by individuals, scholars, policymakers, and organizations throughout human history. In this research, we attempt to answer the question based on the recent advances of Artificial Intelligence (AI) and Large Language Models (LLMs). We propose \textbf{WarAgent}, an LLM-powered multi-agent AI system, to simulate the participating countries, their decisions, and the consequences, in historical international conflicts, including the World War I (WWI), the World War II (WWII), and the Warring States Period (WSP) in Ancient China. By evaluating the simulation effectiveness, we examine the advancements and limitations of cutting-edge AI systems' abilities in studying complex collective human behaviors such as international conflicts under diverse settings. In these simulations, the emergent interactions among agents also offer a novel perspective for examining the triggers and conditions that lead to war. Our findings offer data-driven and AI-augmented insights that can redefine how we approach conflict resolution and peacekeeping strategies. The implications stretch beyond historical analysis, offering a blueprint for using AI to understand human history and possibly prevent future international conflicts. Code and data are available at \url{https://github.com/agiresearch/WarAgent}.

cs.AI↗

arXiv2024

Landscape of Generative AI in Global News: Topics, Sentiments, and Spatiotemporal Analysis

Generative AI has exhibited considerable potential to transform various industries and public life. The role of news media coverage of generative AI is pivotal in shaping public perceptions and judgments about this significant technological innovation. This paper provides in-depth analysis and rich insights into the temporal and spatial distribution of topics, sentiment, and substantive themes within global news coverage focusing on the latest emerging technology --generative AI. We collected a comprehensive dataset of news articles (January 2018 to November 2023, N = 24,827). For topic modeling, we employed the BERTopic technique and combined it with qualitative coding to identify semantic themes. Subsequently, sentiment analysis was conducted using the RoBERTa-base model. Analysis of temporal patterns in the data reveals notable variability in coverage across key topics--business, corporate technological development, regulation and security, and education--with spikes in articles coinciding with major AI developments and policy discussions. Sentiment analysis shows a predominantly neutral to positive media stance, with the business-related articles exhibiting more positive sentiment, while regulation and security articles receive a reserved, neutral to negative sentiment. Our study offers a valuable framework to investigate global news discourse and evaluate news attitudes and themes related to emerging technologies.

cs.CY↗

arXiv2024

Investigating disaster response through social media data and the Susceptible-Infected-Recovered (SIR) model: A case study of 2020 Western U.S. wildfire season

Effective disaster response is critical for affected communities. Responders and decision-makers would benefit from reliable, timely measures of the issues impacting their communities during a disaster, and social media offers a potentially rich data source. Social media can reflect public concerns and demands during a disaster, offering valuable insights for decision-makers to understand evolving situations and optimize resource allocation. We used Bidirectional Encoder Representations from Transformers (BERT) topic modeling to cluster topics from Twitter data. Then, we conducted a temporal-spatial analysis to examine the distribution of these topics across different regions during the 2020 western U.S. wildfire season. Our results show that Twitter users mainly focused on three topics:"health impact," "damage," and "evacuation." We used the Susceptible-Infected-Recovered (SIR) theory to explore the magnitude and velocity of topic diffusion on Twitter. The results displayed a clear relationship between topic trends and wildfire propagation patterns. The estimated parameters obtained from the SIR model in selected cities revealed that residents exhibited a high level of several concerns during the wildfire. Our study details how the SIR model and topic modeling using social media data can provide decision-makers with a quantitative approach to measure disaster response and support their decision-making processes.

cs.SI↗

arXiv2024

AppealMod: Inducing Friction to Reduce Moderator Workload of Handling User Appeals

As content moderation becomes a central aspect of all social media platforms and online communities, interest has grown in how to make moderation decisions contestable. On social media platforms where individual communities moderate their own activities, the responsibility to address user appeals falls on volunteers from within the community. While there is a growing body of work devoted to understanding and supporting the volunteer moderators' workload, little is known about their practice of handling user appeals. Through a collaborative and iterative design process with Reddit moderators, we found that moderators spend considerable effort in investigating user ban appeals and desired to directly engage with users and retain their agency over each decision. To fulfill their needs, we designed and built AppealMod, a system that induces friction in the appeals process by asking users to provide additional information before their appeals are reviewed by human moderators. In addition to giving moderators more information, we expected the friction in the appeal process would lead to a selection effect among users, with many insincere and toxic appeals being abandoned before getting any attention from human moderators. To evaluate our system, we conducted a randomized field experiment in a Reddit community of over 29 million users that lasted for four months. As a result of the selection effect, moderators viewed only 30% of initial appeals and less than 10% of the toxically worded appeals; yet they granted roughly the same number of appeals when compared with the control group. Overall, our system is effective at reducing moderator workload and minimizing their exposure to toxic content while honoring their preference for direct engagement and agency in appeals.

cs.CY↗

arXiv2023

How We Define Harm Impacts Data Annotations: Explaining How Annotators Distinguish Hateful, Offensive, and Toxic Comments

Computational social science research has made advances in machine learning and natural language processing that support content moderators in detecting harmful content. These advances often rely on training datasets annotated by crowdworkers for harmful content. In designing instructions for annotation tasks to generate training data for these algorithms, researchers often treat the harm concepts that we train algorithms to detect - 'hateful', 'offensive', 'toxic', 'racist', 'sexist', etc. - as interchangeable. In this work, we studied whether the way that researchers define 'harm' affects annotation outcomes. Using Venn diagrams, information gain comparisons, and content analyses, we reveal that annotators do not use the concepts 'hateful', 'offensive', and 'toxic' interchangeably. We identify that features of harm definitions and annotators' individual characteristics explain much of how annotators use these terms differently. Our results offer empirical evidence discouraging the common practice of using harm concepts interchangeably in content moderation research. Instead, researchers should make specific choices about which harm concepts to analyze based on their research goals. Recognizing that researchers are often resource constrained, we also encourage researchers to provide information to bound their findings when their concepts of interest differ from concepts that off-the-shelf harmful content detection algorithms identify. Finally, we encourage algorithm providers to ensure their instruments can adapt to contextually-specific content detection goals (e.g., soliciting instrument users' feedback).

cs.CL↗

arXiv2023

Remove, Reduce, Inform: What Actions do People Want Social Media Platforms to Take on Potentially Misleading Content?

To reduce the spread of misinformation, social media platforms may take enforcement actions against offending content, such as adding informational warning labels, reducing distribution, or removing content entirely. However, both their actions and their inactions have been controversial and plagued by allegations of partisan bias. When it comes to specific content items, surprisingly little is known about what ordinary people want the platforms to do. We provide empirical evidence about a politically balanced panel of lay raters' preferences for three potential platform actions on 368 news articles. Our results confirm that on many articles there is a lack of consensus on which actions to take. We find a clear hierarchy of perceived severity of actions with a majority of raters wanting informational labels on the most articles and removal on the fewest. There was no partisan difference in terms of how many articles deserve platform actions but conservatives did prefer somewhat more action on content from liberal sources, and vice versa. We also find that judgments about two holistic properties, misleadingness and harm, could serve as an effective proxy to determine what actions would be approved by a majority of raters.

cs.CY↗

arXiv2023

DataChat: Prototyping a Conversational Agent for Dataset Search and Visualization

Data users need relevant context and research expertise to effectively search for and identify relevant datasets. Leading data providers, such as the Inter-university Consortium for Political and Social Research (ICPSR), offer standardized metadata and search tools to support data search. Metadata standards emphasize the machine-readability of data and its documentation. There are opportunities to enhance dataset search by improving users' ability to learn about, and make sense of, information about data. Prior research has shown that context and expertise are two main barriers users face in effectively searching for, evaluating, and deciding whether to reuse data. In this paper, we propose a novel chatbot-based search system, DataChat, that leverages a graph database and a large language model to provide novel ways for users to interact with and search for research data. DataChat complements data archives' and institutional repositories' ongoing efforts to curate, preserve, and share research data for reuse by making it easier for users to explore and learn about available research data.

cs.IR↗