Search Results for author:
Found 16 papers, 5 papers with code
Characterizing Online Toxicity During the 2022 Mpox Outbreak: A Computational Analysis of Topical and Network Dynamics
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.
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.
War and Peace (WarAgent): Large Language Model-based Multi-Agent Simulation of World Wars
Can we avoid wars at the crossroads of history?
How We Define Harm Impacts Data Annotations: Explaining How Annotators Distinguish Hateful, Offensive, and Toxic Comments
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.
Investigating disaster response through social media data and the Susceptible-Infected-Recovered (SIR) model: A case study of 2020 Western U.S. wildfire season
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.
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.
"HOT" ChatGPT: The promise of ChatGPT in detecting and discriminating hateful, offensive, and toxic comments on social media
To investigate this potential, we used ChatGPT and compared its performance with MTurker annotations for three frequently discussed concepts related to harmful content: Hateful, Offensive, and Toxic (HOT).
A Bibliometric Review of Large Language Models Research from 2017 to 2023
Large language models (LLMs) are a class of language models that have demonstrated outstanding performance across a range of natural language processing (NLP) tasks and have become a highly sought-after research area, because of their ability to generate human-like language and their potential to revolutionize science and technology.
A Natural Language Processing Pipeline for Detecting Informal Data References in Academic Literature
The pipeline increases recall for literature to review for inclusion in data-related collections of publications and makes it possible to detect informal data references at scale.
Leaders or Followers? A Temporal Analysis of Tweets from IRA Trolls
The Internet Research Agency (IRA) influences online political conversations in the United States, exacerbating existing partisan divides and sowing discord.
Librarian-in-the-Loop: A Natural Language Processing Paradigm for Detecting Informal Mentions of Research Data in Academic Literature
The librarian-in-the-loop paradigm is centered in the data work performed by ICPSR librarians, supporting broader efforts to build a more comprehensive bibliography of data-related literature that reflects the scholarly communities of research data users.
Leveraging Machine Learning to Detect Data Curation Activities
This paper contributes: 1) a schema of data curation activities; 2) a computational model for identifying curation actions in work log descriptions; and 3) an analysis of frequent data curation activities at ICPSR over time.
Two Computational Models for Analyzing Political Attention in Social Media
Understanding how political attention is divided and over what subjects is crucial for research on areas such as agenda setting, framing, and political rhetoric.
A Just and Comprehensive Strategy for Using NLP to Address Online Abuse
Online abusive behavior affects millions and the NLP community has attempted to mitigate this problem by developing technologies to detect abuse.
Still out there: Modeling and Identifying Russian Troll Accounts on Twitter
In this work, we: 1) develop machine learning models that predict whether a Twitter account is a Russian troll within a set of 170K control accounts; and, 2) demonstrate that it is possible to use this model to find active accounts on Twitter still likely acting on behalf of the Russian state.
Social and Information Networks Computers and Society
Forecasting the presence and intensity of hostility on Instagram using linguistic and social features
Online antisocial behavior, such as cyberbullying, harassment, and trolling, is a widespread problem that threatens free discussion and has negative physical and mental health consequences for victims and communities.
