Left or Right? : Awareness of Social Media Consumption

This dissertation contributes to a better understanding of how consciously users of social media perceive their online environment. A special focus of two of my projects is on the perception of the political orientation of the online environment, its determinants, and possible distortion factors. This work is based on the assumption that people’s awareness of their online environment is dependent on a correct retrieval of their network on the one hand, and on an accurate assessment of these resources in terms of their political orientation on the other. Therefore, I first examine these basic assumptions in two projects, while a third project finally measures the awareness of one’s environment.All presented projects use online surveys to collect empirical data and are based on the analysis of digital trace data. This dissertation, therefore, combines classical elements of empirical social science with novel methods of computer science in the new research field that is Computational Social Science. This work contributes substantially to theoretical considerations from social psychology, political science, and methodological social research. The present study shows to what extent people can recapitulate their behavior on social media, how the political orientation of their network and that of third parties is evaluated, and which cognitive mechanisms cause a distorted perception.In the first project, I lay the grounds for the other projects by comparing the reported values of a reliably measurable variable (Twitter activity in the form of the number of most recently posted messages on Twitter, as well as the number of friends on Twitter) to their observed values. The aim of this work is to gain a basic intuition for the accuracy of self-reported values regarding one’s own online behavior and thus to contribute to research regarding survey methodology. In general, the results indicate a low accuracy of the Twitter users’ perception of their Twitter activity. Furthermore, there seem to be differences in response behavior according to social group membership and recruitment context. This project contributes to recently published research on distorted response behavior when reporting online activity (Guess et al., 2019; Henderson et al., 2019) and sets the framework for the validity of the results of the other projects.Awareness of online media environments, however, not only depends on remembering one’s own social network as accurately as possible but also on correctly assessing the political orientation of the individual sources. The second project contributes to the literature on the perception of political news distortions (e.g., Vallone, Ross and Leeper, 1985; Gunther, 1992) by evaluating in a conjoint experiment (Hainmueller, Hopkins and Yamamoto, 2014) the general ability of Internet users to recognize the ideological direction of hypothetical Twitter networks. The results suggest that conservative Internet users estimate the correct political alignment of liberal Twitter networks less accurately than congenial Twitter networks. This project also contributes to research on media literacy (e.g., van der Meer and Hameleers, 2020) by measuring normative views on the diversity of subscribed media content. Overall, participants in my study generally prefer a political diversification of Twitter networks.Finally, the last project measures the awareness of Twitter users for the political attitudes of their own Twitter environment and is thus at the heart of the topic of this dissertation. By comparing perceptions of their own Twitter network with estimates obtained with the method developed by Barberá (2015), I show that there is a basic intuition for the political attitudes of their own environment. However, the political attitudes of social media users distort the accuracy of this awareness by overestimating the percentage of Twitter friends who have the same political attitude as the user herself, while political knowledge helps to determine the correct political orientation of one’s own Twitter environment more accurately.This dissertation investigates online media consumption within the framework of social science hypotheses using a variety of modern survey and recruitment methods as well as innovative tools from the field of computer science. The results of the second and third projects suggest that Internet users have a basic sense of the political inclination of online actors. However, the results of the first and third projects also clearly show that there is a general uncertainty regarding their own online activities. With the analyses of cognitive mechanisms in the second and third projects and the collection of normative views on online news consumption in the second project, my results contribute to explaining this discrepancy between presumed and actual behavior.References:Barberá, Pablo. 2015. “Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data.” Political Analysis 23:76–91.Guess, Andrew, Kevin Munger, Jonathan Nagler and Joshua Tucker. 2019. “How Accurate Are Survey Responses on Social Media and Politics.” Political Communication 36(2):241–258.Gunther, Albert C. 1992. “Biased Press or Biased Public? Attitudes Toward Media Coverage of Social Groups.” Public Opinion Quarterly 56(2):147–167.Hainmueller, Jens, Daniel J. Hopkins and Teppei Yamamoto. 2014. “Causal Inference in Conjoint Analysis: Understanding Multidimensional Choices via Stated Preference Experiments.” Political Analysis 22(1):1–30.Henderson, Michael, Ke Jiang, Martin Johnson and Lance Porter. 2019. “Measuring Twitter Use: Validating Survey-Based Measures.” Social Science Computer Review TBA:1–21.Vallone, Robert P., Lee Ross and Mark R. Leeper. 1985. “The Hostile Media Phenomenon: Biased Perception and Perceptions of Media Bias in Coverage of the Beirut Massacre.” Journal of Personality and Social Psychology 49(3):577–585.van der Meer, Toni G.L.A. and Michael Hameleers. 2020. “Fighting Biased News Diets: Using News Media Literacy Interventions to Stimulate Online Cross-Cutting Media Exposure Patterns.” New Media & Society TBA:1–23.publishe