34 citations · 69 across the 12 of their papers we have counts for
12 papers
DIVERSE: A Dataset of YouTube Video Comment Stances with a Data Programming Model
Iain J. Cruickshank, Amir Soofi, Lynnette Hui Xian Ng
Public opinion of military organizations significantly influences their ability to recruit talented individuals. As recruitment efforts increasingly extend into digital spaces like…
Developing a Natural Language Understanding Model to Characterize Cable News Bias
Seth P. Benson, Iain J. Cruickshank
Media bias has been extensively studied by both social and computational sciences. However, current work still has a large reliance on human input and subjective assessment to labe…
Prompting and Fine-Tuning Open-Sourced Large Language Models for Stance Classification
Iain J. Cruickshank, Lynnette Hui Xian Ng
Stance classification, the task of predicting the viewpoint of an author on a subject of interest, has long been a focal point of research in domains ranging from social science to…
Analysis of Media Writing Style Bias through Text-Embedding Networks
Iain J. Cruickshank, Jessica Zhu, Nathaniel D. Bastian
With the rise of phenomena like `fake news' and the growth of heavily-biased media ecosystems, there has been increased attention on understanding and evaluating media bias. Of par…
Measuring Classification Decision Certainty and Doubt
Alexander M. Berenbeim, Iain J. Cruickshank, Susmit Jha +2
Quantitative characterizations and estimations of uncertainty are of fundamental importance in optimization and decision-making processes. Herein, we propose intuitive scores, whic…
MLTEing Models: Negotiating, Evaluating, and Documenting Model and System Qualities
Katherine R. Maffey, Kyle Dotterrer, Jennifer Niemann +3
Many organizations seek to ensure that machine learning (ML) and artificial intelligence (AI) systems work as intended in production but currently do not have a cohesive methodolog…
Multi-modal Networks Reveal Patterns of Operational Similarity of Terrorist Organizations
Gian Maria Campedelli, Iain J. Cruickshank, Kathleen M. Carley
Capturing dynamics of operational similarity among terrorist groups is critical to provide actionable insights for counter-terrorism and intelligence monitoring. Yet, in spite of i…
Influencing the Influencers: Evaluating Person-to-Person Influence on Social Networks Using Granger Causality
Richard Kuzma, Iain J. Cruickshank, Kathleen M. Carley
We introduce a novel method for analyzing person-to-person content influence on Twitter. Using an Ego-Alter framework and Granger Causality, we examine President Donald Trump (the…
Coordinating Narratives and the Capitol Riots on Parler
Lynnette Hui Xian Ng, Iain Cruickshank, Kathleen M. Carley
Coordinated disinformation campaigns are used to influence social media users, potentially leading to offline violence. In this study, we introduce a general methodology to uncover…
Analysis of External Content in the Vaccination Discussion on Twitter
Richard Kuzma, Iain J. Cruickshank, Kathleen M. Carley
The spread of coronavirus and anti-vaccine conspiracies online hindered public health responses to the pandemic. We examined the content of external articles shared on Twitter from…
Characterizing Communities of Hashtag Usage on Twitter During the 2020 COVID-19 Pandemic by Multi-view Clustering
Iain J. Cruickshank, Kathleen M. Carley
The COVID-19 pandemic has produced a flurry of online activity on social media sites. As such, analysis of social media data during the COVID-19 pandemic can produce unique insight…
A Complex Networks Approach to Find Latent Clusters of Terrorist Groups
Gian Maria Campedelli, Iain Cruickshank, Kathleen M. Carley
Given the extreme heterogeneity of actors and groups participating in terrorist actions, investigating and assessing their characteristics can be important to extract relevant info…