Michael Yankoski

CL
h-index5
3papers
19citations
Novelty48%
AI Score25

3 Papers

CVMay 11, 2022
MEWS: Real-time Social Media Manipulation Detection and Analysis

Trenton W. Ford, William Theisen, Michael Yankoski et al.

This article presents a beta-version of MEWS (Misinformation Early Warning System). It describes the various aspects of the ingestion, manipulation detection, and graphing algorithms employed to determine--in near real-time--the relationships between social media images as they emerge and spread on social media platforms. By combining these various technologies into a single processing pipeline, MEWS can identify manipulated media items as they arise and identify when these particular items begin trending on individual social media platforms or even across multiple platforms. The emergence of a novel manipulation followed by rapid diffusion of the manipulated content suggests a disinformation campaign.

CLMar 18, 2024
Span-Oriented Information Extraction -- A Unifying Perspective on Information Extraction

Yifan Ding, Michael Yankoski, Tim Weninger

Information Extraction refers to a collection of tasks within Natural Language Processing (NLP) that identifies sub-sequences within text and their labels. These tasks have been used for many years to link extract relevant information and to link free text to structured data. However, the heterogeneity among information extraction tasks impedes progress in this area. We therefore offer a unifying perspective centered on what we define to be spans in text. We then re-orient these seemingly incongruous tasks into this unified perspective and then re-present the wide assortment of information extraction tasks as variants of the same basic Span-Oriented Information Extraction task.

CYJul 16, 2021
Pilot Study Suggests Online Media Literacy Programming Reduces Belief in False News in Indonesia

Pamela Bilo Thomas, Clark Hogan-Taylor, Michael Yankoski et al.

Amidst the threat of digital misinformation, we offer a pilot study regarding the efficacy of an online social media literacy campaign aimed at empowering individuals in Indonesia with skills to help them identify misinformation. We found that users who engaged with our online training materials and educational videos were more likely to identify misinformation than those in our control group (total $N$=1000). Given the promising results of our preliminary study, we plan to expand efforts in this area, and build upon lessons learned from this pilot study.