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Taking the High Road: A Big Data Investigation of Natural Discourse in the Emerging U.S. Consensus about Marijuana Legalization
Abstract and Figures
U.S. support for marijuana legalization grew from 38% to 65% in 2008-2019. To find the discourse features that preceded and followed the shift, I curated a comprehensive corpus of Reddit comments from the same period. Neural networks trained on human annotations of attitude and persuasion attempts separated strategic use of narratives from non-argumentative discourse. Two narrative frames considered important to persuasion in past research were studied: anecdotal vs. generalized content. I operationalized anecdotal frames based on three linguistic clause-level features: Whether the clause is about a generic kind, if it represents a reliable state or an event, and whether any events are bounded in time. A corpus of Reddit and news was annotated for these features and more, neural networks based on which estimated anecdotal properties in the broader Reddit dataset. Anecdotal themes were less prevalent but present in most comments, particularly in arguments favoring legalization. Nationally, a surge in anecdotes within non-argumentative discourse happened over time as a consequence of attitude shifts. Generalized discourse was a potential cause with major surges around the 2012 and 2016 legal milestones. Attempts to associate generalized discourse with legal changes were complicated by marijuana’s varied status across the U.S. I therefore inferred user locations and compared the rate of anecdotal themes before and after legalization in comments from pioneering states. More generalized frames set the stage for each successful legalization bid. The particular content, however, varied between the two milestones. Character judgments were prominent in 2012, while crimes and politics took center-stage in 2016. The generalized precedents of legalization in the two periods shared argumentative and moralistic focus but had distinctive clause-level profiles. Meanwhile, legal and medical arguments were sidelined, meaning the novel consensus was not informed by much of the relevant information. Together, my findings present generalized argument framing as a harbinger of attitude shift toward hot-button topics, and anecdotal non-argumentative framing as a consequence of it. The machine learning pipeline that made this insight possible is novel for social media research but general-purpose, allowing similar abstract narrative frames to be broken down into theory-driven constituents, and studied in quantitative detail.
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