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Christina P. Walker

dblp:371/6230 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2024
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Generative modeling · 100%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Computational social science and digital humanities · 100%
Network and information security
1 paper
Security and privacy of machine learning · 100%

Topics — the 1 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Generative modeling › image manipulation
deepfake
0.812024
Merging AI Incidents Research with Political Misinformation Research: Introducing the Political Deepfakes Incidents Database · AAAI 2024

Methods — techniques the papers use, named apart from their topics

database construction · 2.3content coding · 2.3
YearPublicationVenuePosition
2024 Merging AI Incidents Research with Political Misinformation Research: Introducing the Political Deepfakes Incidents Database
abstract
This article presents the Political Deepfakes Incidents Database (PDID), a collection of politically-salient deepfakes, encompassing synthetically-created videos, images, and less-sophisticated `cheapfakes.' The project is driven by the rise of generative AI in politics, ongoing policy efforts to address harms, and the need to connect AI incidents and political communication research. The database contains political deepfake content, metadata, and researcher-coded descriptors drawn from political science, public policy, communication, and misinformation studies. It aims to help reveal the prevalence, trends, and impact of political deepfakes, such as those featuring major political figures or events. The PDID can benefit policymakers, researchers, journalists, fact-checkers, and the public by providing insights into deepfake usage, aiding in regulation, enabling in-depth analyses, supporting fact-checking and trust-building efforts, and raising awareness of political deepfakes. It is suitable for research and application on media effects, political discourse, AI ethics, technology governance, media literacy, and countermeasures.
Christina P. Walker, Daniel S. Schiff, Kaylyn Jackson Schiff
AAAI1