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Found 6 papers, 1 papers with code
Machine unlearning through fine-grained model parameters perturbation
Machine unlearning techniques, which involve retracting data records and reducing influence of said data on trained models, help with the user privacy protection objective but incur significant computational costs.
eCIL-MU: Embedding based Class Incremental Learning and Machine Unlearning
New categories may be introduced over time, or existing categories may need to be reclassified.
AMIR: Automated MisInformation Rebuttal -- A COVID-19 Vaccination Datasets based Recommendation System
While the ideas herein can be generalized and reapplied in the broader context of misinformation mitigation using a multitude of information sources and catering to the spectrum of social media platforms, this work serves as a proof of concept, and as such, it is confined in its scope to only rebuttal of tweets, and in the specific context of misinformation regarding COVID-19.
Misinformation Concierge: A Proof-of-Concept with Curated Twitter Dataset on COVID-19 Vaccination
We demonstrate the Misinformation Concierge, a proof-of-concept that provides actionable intelligence on misinformation prevalent in social media.
A Modular Framework for Centrality and Clustering in Complex Networks
It leads to a novel two-stage graph clustering algorithm.
Misleading the Covid-19 vaccination discourse on Twitter: An exploratory study of infodemic around the pandemic
We build on this to study and contrast the characteristics of tweets in the corpus that are misleading in nature against non-misleading ones.
