38 citations · 107 across the 19 of their papers we have counts for
24 papers
Exploring the Benefits of Domain-Pretraining of Generative Large Language Models for Chemistry
Anurag Acharya, Shivam Sharma, Robin Cosbey +3
A proliferation of Large Language Models (the GPT series, BLOOM, LLaMA, and more) are driving forward novel development of multipurpose AI for a variety of tasks, particularly natu…
EXPERT: Public Benchmarks for Dynamic Heterogeneous Academic Graphs
Sameera Horawalavithana, Ellyn Ayton, Anastasiya Usenko +5
Machine learning models that learn from dynamic graphs face nontrivial challenges in learning and inference as both nodes and edges change over time. The existing large-scale graph…
Unsupervised Keyphrase Extraction via Interpretable Neural Networks
Rishabh Joshi, Vidhisha Balachandran, Emily Saldanha +3
Keyphrase extraction aims at automatically extracting a list of "important" phrases representing the key concepts in a document. Prior approaches for unsupervised keyphrase extract…
Identifying Causal Influences on Publication Trends and Behavior: A Case Study of the Computational Linguistics Community
Maria Glenski, Svitlana Volkova
Drawing causal conclusions from observational real-world data is a very much desired but challenging task. In this paper we present mixed-method analyses to investigate causal infl…
VAINE: Visualization and AI for Natural Experiments
Grace Guo, Maria Glenski, ZhuanYi Shaw +4
Natural experiments are observational studies where the assignment of treatment conditions to different populations occurs by chance "in the wild". Researchers from fields such as…
Leveraging Community and Author Context to Explain the Performance and Bias of Text-Based Deception Detection Models
Galen Weld, Ellyn Ayton, Tim Althoff +1
Deceptive news posts shared in online communities can be detected with NLP models, and much recent research has focused on the development of such models. In this work, we use char…
Towards Trustworthy Deception Detection: Benchmarking Model Robustness across Domains, Modalities, and Languages
Maria Glenski, Ellyn Ayton, Robin Cosbey +2
Evaluating model robustness is critical when developing trustworthy models not only to gain deeper understanding of model behavior, strengths, and weaknesses, but also to develop f…
Evaluating Deception Detection Model Robustness To Linguistic Variation
Maria Glenski, Ellyn Ayton, Robin Cosbey +2
With the increasing use of machine-learning driven algorithmic judgements, it is critical to develop models that are robust to evolving or manipulated inputs. We propose an extensi…
Political Bias and Factualness in News Sharing across more than 100,000 Online Communities
Galen Weld, Maria Glenski, Tim Althoff
As civil discourse increasingly takes place online, misinformation and the polarization of news shared in online communities have become ever more relevant concerns with real world…
Behavior Change in Response to Subreddit Bans and External Events
Pamela Bilo Thomas, Daniel Riehm, Maria Glenski +1
As more people flock to social media to connect with others and form virtual communities, it is important to research how members of these groups interact to understand human behav…
Measure Utility, Gain Trust: Practical Advice for XAI Researcher
Brittany Davis, Maria Glenski, William Sealy +1
Research into the explanation of machine learning models, i.e., explainable AI (XAI), has seen a commensurate exponential growth alongside deep artificial neural networks throughou…
Adjusting for Confounders with Text: Challenges and an Empirical Evaluation Framework for Causal Inference
Galen Weld, Peter West, Maria Glenski +3
Causal inference studies using textual social media data can provide actionable insights on human behavior. Making accurate causal inferences with text requires controlling for con…
CrossCheck: Rapid, Reproducible, and Interpretable Model Evaluation
Dustin Arendt, Zhuanyi Huang, Prasha Shrestha +3
Evaluation beyond aggregate performance metrics, e.g. F1-score, is crucial to both establish an appropriate level of trust in machine learning models and identify future model impr…
Multilingual Multimodal Digital Deception Detection and Disinformation Spread across Social Platforms
Maria Glenski, Ellyn Ayton, Josh Mendoza +1
Our main contribution in this work is novel results of multilingual models that go beyond typical applications of rumor or misinformation detection in English social news content t…
Improved Forecasting of Cryptocurrency Price using Social Signals
Maria Glenski, Tim Weninger, Svitlana Volkova
Social media signals have been successfully used to develop large-scale predictive and anticipatory analytics. For example, forecasting stock market prices and influenza outbreaks.…
Propagation from Deceptive News Sources: Who Shares, How Much, How Evenly, and How Quickly?
Maria Glenski, Tim Weninger, Svitlana Volkova
As people rely on social media as their primary sources of news, the spread of misinformation has become a significant concern. In this large-scale study of news in social media we…
GuessTheKarma: A Game to Assess Social Rating Systems
Maria Glenski, Greg Stoddard, Paul Resnick +1
Popularity systems, like Twitter retweets, Reddit upvotes, and Pinterest pins have the potential to guide people toward posts that others liked. That, however, creates a feedback l…
How Humans versus Bots React to Deceptive and Trusted News Sources: A Case Study of Active Users
Maria Glenski, Tim Weninger, Svitlana Volkova
Society's reliance on social media as a primary source of news has spawned a renewed focus on the spread of misinformation. In this work, we identify the differences in how social…
Identifying and Understanding User Reactions to Deceptive and Trusted Social News Sources
Maria Glenski, Tim Weninger, Svitlana Volkova
In the age of social news, it is important to understand the types of reactions that are evoked from news sources with various levels of credibility. In the present work we seek to…
Fishing for Clickbaits in Social Images and Texts with Linguistically-Infused Neural Network Models
Maria Glenski, Ellyn Ayton, Dustin Arendt +1
This paper presents the results and conclusions of our participation in the Clickbait Challenge 2017 on automatic clickbait detection in social media. We first describe linguistica…
Predicting User-Interactions on Reddit
Maria Glenski, Tim Weninger
In order to keep up with the demand of curating the deluge of crowd-sourced content, social media platforms leverage user interaction feedback to make decisions about which content…
Consumers and Curators: Browsing and Voting Patterns on Reddit
Maria Glenski, Corey Pennycuff, Tim Weninger
As crowd-sourced curation of news and information become the norm, it is important to understand not only how individuals consume information through social news Web sites, but als…
Rating Effects on Social News Posts and Comments
Maria Glenski, Tim Weninger
At a time when information seekers first turn to digital sources for news and opinion, it is critical that we understand the role that social media plays in human behavior. This is…
Random Voting Effects in Social-Digital Spaces: A case study of Reddit Post Submissions
Maria Glenski, Thomas J. Johnston, Tim Weninger
At a time when information seekers first turn to digital sources for news and opinion, it is critical that we understand the role that social media plays in human behavior. This is…