  The COVID-19 pandemic has been accompanied by an `infodemic' -- of accurate
and inaccurate health information across social media. Detecting misinformation
amidst dynamically changing information landscape is challenging; identifying
relevant keywords and posts is arduous due to the large amount of human effort
required to inspect the content and sources of posts. We aim to reduce the
resource cost of this process by introducing a weakly-supervised iterative
graph-based approach to detect keywords, topics, and themes related to
misinformation, with a focus on COVID-19. Our approach can successfully detect
specific topics from general misinformation-related seed words in a few seed
texts. Our approach utilizes the BERT-based Word Graph Search (BWGS) algorithm
that builds on context-based neural network embeddings for retrieving
misinformation-related posts. We utilize Latent Dirichlet Allocation (LDA)
topic modeling for obtaining misinformation-related themes from the texts
returned by BWGS. Furthermore, we propose the BERT-based Multi-directional Word
Graph Search (BMDWGS) algorithm that utilizes greater starting context
information for misinformation extraction. In addition to a qualitative
analysis of our approach, our quantitative analyses show that BWGS and BMDWGS
are effective in extracting misinformation-related content compared to common
baselines in low data resource settings. Extracting such content is useful for
uncovering prevalent misconceptions and concerns and for facilitating precision
public health messaging campaigns to improve health behaviors.
