Lu He: Hi! Everyone. I am Lu, a PhD. Candidate in informatics at the University of California, Irvine
Lu He: today i'll be presenting our CSCW Paper with my co-author, Changyang He from the Department of Computer Science and Engineering at Hong Kong University of Science and Technology
Lu He: Our paper is entitled, Help me debunk this: unpacking the individual and community work and information credibility assessments
Lu He: In the United States, Information consumers view misinformation as a critical issue
Lu He: that causes confusion and threats their trust in authoritative institutions and each other.
Lu He: Especially during the pandemic, online information misinformation about vaccination, personal protective equipment, such as masks is spreading fast and affecting the public's everyday life and behaviors.
Lu He: Information consumers need to actively participate in information credibility assessment to make sure that they're not misled by inaccurate information.
Lu He: While such tasks are commonly carried out by domain experts. The amount of online information and the speed of information travels is beyond their capacity.
Lu He: Therefore, online users are also actively contributing their opinions to check if the information is accurate.
Lu He: Existing literature on laypeople's debunking efforts
Lu He: focuses on their accuracy in detecting misinformation and treats debunking mostly as an individual effort.
Lu He: Literature from other community-driven practices, such as the Wikipedia project suggests that the process is complex.
Lu He: So what about the community-based debunking process?
Lu He: How do they look like?
Lu He: How complex they are?
Lu He: in this study. We aim to understand and uncover how community debunkers individually and collaboratively assess information credibility.
Lu He: To do this, we focus on an active subreddit that hosts a large number of volunteer community debunkers which check information credibility for others.
Lu He: Users can post information that they would like others to check.
Lu He: Community members, who then reply the post to provide their opinions on the accuracy of the information.
Lu He: We collected more than four thousand threads
Lu He: and their associate replies from the subreddit in March, 2021.
Lu He: Two authors qualitatively coded a random sample of 108 unique threads, containing more than two two thousand replies contributed by more than four hundred unique users. More details about how we filtered and sampled the data
Lu He: and the qualitative coding and codebook development can be found in the full paper.
Lu He: We found that individual users'
Lu He: work in information credibility assessment serves as the foundation of debunking in the online community.
Lu He: They adopt various strategies, such as
Lu He: debunking based on the specific characteristic of the information by checking if the information was accompanied by relevant supporting material, or if the content seemed to be edited intentionally, or cherrypicked, which are considered as red flags by the community debunkers.
Lu He: The Debunkers, also debunk with the support of external information that may come from authoritative sources, such as CDC, research papers, or other debunks of the same claims.
Lu He: They may also debunk utilizing their personal knowledge, which can be situated, that is, the debunker has personal experiences related to the claim presented, or they could debunk, using their personal knowledge obtained through professional trainings in the particular field.
Lu He: Oftentimes they debunk with multiple strategies
Lu He: by presenting both external information and leveraging their personal knowledge.
Lu He: Then I would present the community work of information credibility assessment.
Lu He: The community work is defined by replies that reference each other debunkers' point and initiate rich discussions and social interactions which collaboratively contributed to debunking information or improving the quality of existing debunks.
Lu He: For example
Lu He: the first debunker provided external information,
Lu He: and the second debunker, while focusing on the same points supplemented with personal knowledge.
Lu He: Sometimes debunkers may also based on other debunkers' replies, request, additional information, or clarifications and justifications. In this example, the first debunker said "I think a chart in the post to extract, at least to me".
Lu He: The second debunker requested justification about the first debunker's reply.
Lu He: The debunkers may also correct each other's
Lu He: debunking replies because the
Lu He: answers may not necessarily be completely correct. For example, the first debunker claimed that autism is a rare and poorly understood disorder. The second debunker corrected the false claim that autism is rare by presenting statistics from the CDC.
Lu He: The debunkers may also involve in the back-and-forth debate to reach consensus with each other.
Lu He: Sometimes when the number of replies grow and get overwhelming for new readers, some debunkers, instead of producing their own debunking replies, they synthesize the key take-away from other debunkers' replies.
Lu He: Besides deciding if the information presented is correct or not, the debunkers may also
Lu He: interact with each other socially. For example, when debunkers attack the original posters or other debunkers, some debunkers may remind community rules and norms, such as attacking the claim, not the person.
Lu He: In summary, we found that the community-based debunking effort is rarely a linear and individual process. Actually among the one hundred and eight threads that we analyzed, more than seventeen percent involve multiple community members inn collaboratively assessing information credibility.
Lu He: Based on our analysis, we derived implications for future research and better platform design.
Lu He: First, we know that debunking is a negotiated and collaborative effort. Future platforms should design functions to support easier collaborative efforts, such as requests, summarizations and collection.
Lu He: Future platforms can consider design features to support community development.
Lu He: Debunking is also a learning and educational process.
Lu He: Platforms can consider designing features to support community knowledge accumulation which can be re-used in future debunking.
Lu He: Lastly, we note that
Lu He: community debunking is a complex, tedious, and highly collaborative effort, and therefore researchers and the platforms should recognize, value, and even compensate the community debunkers' labor.
Lu He: Thank you for listening to our presentation. For more questions about our paper, you can reach out to us.