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ICA Top Paper: Minich, Cotter, Kriss, Lu, Yang, & Cascio

First author Dr. Matt Minich

In our lead up to #ICA24, we are providing information about papers that received Top Paper awards from the Communication Science and Biology (CSaB) Interest Group. Each paper received exceptionally high scores from reviewers. These papers reflect outstanding scholarship in CSaB. Today’s Top Paper features Minich, Cotter, Kriss, Lu, Yang, and Cascio’s paper: “Pictorial warning labels reduce sharing intentions, alter self-relevance processes elicited by social media posts promoting cannabis edibles”. Be sure to check out the paper at #ICA24

CSaB: In a few short sentences, what is your study about?

Matt: This study tested a neural model of information sharing in a new context: the dissemination of cannabis marketing posts on social media. Specifically, we tested whether adding a pictorial warning label (like the kind mandated on some tobacco products) might make these materials less likely to go viral. We found that people were less likely to share posts that had warning labels, and that posts with warning labels elicited less activity in a brain network associated with self-relevance processes.  

CSaB: How did you come up with the idea for this line of research?

Matt: This study builds on some recent findings (Kim & Minich et al., 2023) that warning labels for cannabis products can have an emotional impact on their audiences. Emotions are an important motivator of online information sharing, so we wondered whether that process might also be affected by warning labels. Some of the literature we reviewed suggested that this might be the case, and a parallel line of study identified the sharing of cannabis marketing materials online as a serious problem, particularly for adolescent social media users. Thus, we saw an opportunity to achieve two things at once – improve our understanding of online sharing decisions and potentially address an emerging public health threat. 

CSaB: Anything else you’d like to add?

Matt: I’d only like to highlight co-first author Lynne Cotter, who conducted the first of the two studies that make up this paper. 

CSaB: Tell us more about the team!

Matt: This project was borne from a collaboration between two research labs. Lynne Cotter, Linqi Lu, and Prof. Sijia Yang are all computational methods experts from UW-Madison’s Computational Approaches to Message Effects Research (CAMER) lab. Lauren A. Kriss, Prof. Christopher N. Cascio, and I work primarily in the Communication, Brain, and Behavior (CBB) lab. 

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