Xuanjun (Jason) Gong

Media researchers aim to accurately explain and predict people’s media selection. Explanation is an accurate description of the causal mechanisms that govern media selection whereas prediction is focused on making accurate inferences about unobserved data. However, meta-analyses demonstrate that existing media selection theories and models have limited explanatory accuracy. The predictive accuracy of these theories and models is unknown. Our project bridges this gap by empirically specifying how predictable, in principle, media selection is. To achieve this ambition, we articulate key conceptual distinctions between explanation and prediction. Subsequently, we report two empirical studies that estimate the theoretical maximum predictability of people’s music-listening and web-browsing behaviors. Approximately 80% of music selection and 60% of web-browsing behaviors are predictable. Moreover, a simple Markov Chain model that uses information about people’s prior media selection can achieve about 20% prediction accuracy for music selection and 10% accuracy in predicting web-browsing. By estimating the maximum predictability of people’s media selection behavior, we gain a first-ever benchmark by which media selection theories and models can be compared. More broadly, we show how simple models that account for the sequential dependency in media selection lend new insights and suggest novel directions for future theory development.

Understanding information diffusion is vital to explaining the good, bad, and ugly impacts of social media. Two types of processes govern information diffusion: broadcasting and viral spread. Viral spreading is when a message is diffused by peer-to-peer social connections, whereas broadcasting is characterized by influences that can come from outside of the peer-to-peer social network. How these processes shape public discourse is not well understood. Using a simulation study and real-world Twitter data (10,155 users, 18,000,929 tweets) gathered during 2020, we show that broadcast spreading is associated with more integrated discourse networks compared to viral spreading. Moreover, discourse oscillates between extended periods of segregation and punctuated periods of integration. These results defy simple interpretations of good or bad, and instead suggest that information diffusion dynamics on social media have the capacity to disrupt or amplify both prosocial and antisocial content.

Flow is thought to occur when both task difficulty and individual ability are high. Flow experiences are highly rewarding and are associated with well-being. Importantly, media use can be a source of flow. Communication scholars have a long history of theoretical inquiry into how flow biases media selection, how different media content results in flow, and how flow influences media processing and effects. However, the neurobiological basis of flow during media use is not well understood, limiting our explanatory capacity to specify how media contribute to flow or well-being. Here, we show that flow is associated with a flexible and modular brain-network topology, which may offer an explanation for why flow is simultaneously perceived as high-control and effortless, even when the task difficulty is high. Our study tests core predictions derived from synchronization theory, and our results provide qualified support for the theory while also suggesting important theoretical updates.