(Mis)measurement of Political Content Exposure within the Smartphone Ecosystem
Investigating Common Assumptions
DOI:
https://doi.org/10.51685/jqd.2024.015Keywords:
Political Communication, computational social science, Screenomics, partisan polarization, news consumptionAbstract
The affordances of the smartphone are shifting individuals toward ever smaller and more fragmented units of political experience. In this piece, we make use of a novel approach to granular assessment of political exposure on smartphones, revealing an incredible level of complexity in modern political content diets, somewhat at odds with simplifying assumptions commonly made by political communication research. Based on five million screen-recording frames taken from 119 smartphones over two weeks, we find clear challenges to three common assumptions in the literature, with clear impacts on new theories about fragmented political media use: Assumption (1) unique encounters with political content can be aggregated by tabulation as though they are equivalent experiences; (2) durations of exposure to political content can be aggregated (e.g., at a monthly or daily level) without regard for how those time units are clustered at smaller timescales; and (3) singular political formats or sources (particularly the news formats and sources) are sufficient proxies for measuring and manipulating overall political content exposure. Regarding the first and second assumptions, our findings suggest that the majority of political content exposure occurs in bursts of only a few potentially-forgettable seconds, and that the remainder follow a power-law curve that reemerges across apps and individuals, with extreme variability within and across individuals. Regarding the third assumption, we find the vast majority of political content is encountered from formats and sources other than news and social media. We articulate how these results fit within and augment literature focused on political content exposure.
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Copyright (c) 2024 Daniel Muise, David M. Markowitz, Byron Reeves, Nilam Ram, Thomas N. Robinson
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.