Motivating COVID-19 Misinformation Acknowledgement on Instagram
DFP Student Team: Abiramy Kuganesan (MSc, Computer Science, UBC); Hannah Elbaggari (MSc, Computer Science, UBC); Oloff Biermann (MSc, Computer Science, UBC); Eleanor Ren (MLIS, iSchool, UBC); Jin Wen (PhD, Psychology, UBC)
Faculty Consultants: Luanne Sinnamon (iSchool, UBC)
Misinformation is information that has been demonstrated to be inaccurate, misleading or detrimental. It can cause negative effects on individual and societal beliefs, invoke feelings of fear and suspicion, and lead to declines in mental and physical well-being. The COVID-19 pandemic has brought new meaning to these dangers. However, misinformation does not only come from insidious misinformation-spreaders, it is also fueled by the engagement of users unknowingly spreading false information. There exists a need to create user-interfaces that not only address the spread of misinformation, but also encourage misinformation reporting behaviours. This study is interested in misinformation systems on Instagram given the platform’s rising usage among younger adults. Studies have found that the content on Instagram frequently contains misleading information and is a major contributor to misinformation spread between communities. We set out to encourage users to think critically about information consumption and provide them with a method of flagging misinformation that aligns more closely with their goals. We assess user motivations for engaging with misinformation and categorize users by similarities in engagement behaviours. We find that users differ considerably in their motivations and that a “one-size-fits-all” approach for misinformation reporting would be difficult to capture. We focus on the user archetype with the least misinformation engagement. A prototype that draws from digital nudge theory and the reader to leader framework is developed. Unlike Instagram’s current approach, our prototype features an option that allows users to label a post as true, unsure, or false in a quick and non-intrusive manner. Generally, users express that the design is a lightweight and quick method of labeling misinformation. Ultimately, our results provide a basis for further investigation that can potentially refine design designs for misinformation reporting on social media.
Beautiful presentation! I really enjoyed your flow and description of the problem and solution.
I’d be curious to see what would happen if that function were available sociologically speaking – as there is great division on what the “truth” is, and what is or isn’t fact, would both “sides” of a polarized issue equally report the other side as “fake news”, and thus overwhelm the moderators who would be responsible for evaluating and removing the posts? Or, are there studies showing that more actual misinformation is tagged as such, as opposed to “real” information?
Great job!