publications | Xuanjun (Jason) Gong

2024

Computational Modeling Entertainment Media Choice and Decision Making in Communication Science

Xuanjun (Jason) Gong, and Richard Huskey

In DeGruyter Handbook of Entertainment, 2024

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Media researchers and industry practitioners aim to predict and explain what content people select on entertainment platforms. However, the high complexity of this stochastic media selection process and low explanatory power of current approaches presents an obstacle for theory development. Emerging behavioral experimentation methods drawn from decision science, coupled with the increasing availability of logged media selection data from streaming entertainment platforms, present new opportunities to verify existing theories and develop new theories with greater explainability and predictability. To exploit this emerging opportunity, media researchers need to transition from verbal theory to formal computational models, with novel parameter estimation, data simulation, and model verification methods. Here, we will introduce the basic concepts and analytical knowledge for formal modeling methods, as well as a theoretical framework rooted in decision science for interpreting and forecasting humans’ entertainment behaviors.

2023

Media selection is highly predictable, In principle

Xuanjun (Jason) Gong, and Richard Huskey

Computational Communication Research, Dec 2023

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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.
Consider the time dimension: theorizing and formalizing sequential media selection

Xuanjun (Jason) Gong, and Richard Huskey

Human Communication Research, Dec 2023

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Existing media-selection theories predominantly consider media selection at a static moment-in-time. However, such theorizing is out-of-step with today’s media landscape, which is dominated by sequential media consumption where future media selection is dependent on previously selected media. Ignoring the dependencies among sequential media selection leads to a failure to theorize and model the time-evolving nature of media selection. To bridge this gap, we review computational modeling methods and offer an integrative theoretical framework for studying sequential media selection. In doing so, we lay the theoretical and methodological foundation necessary for state-of-the-art research focused on understanding the underlying mechanisms of, and sequential dependencies among, media selection. Our sequential media-selection framework helps media researchers by theorizing and formalizing processes related to learning, exploration vs. exploitation, and foraging. The outcome is a manuscript that builds on existing theory and research to offer a roadmap for next-generation media-selection inquiry.
Moving behavioral experimentation online: A tutorial and some recommendations for drift diffusion modeling

Xuanjun (Jason) Gong, and Richard Huskey

American Behavioral Scientist, Nov 2023

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Behavioral science demands skillful experimentation and high-quality data that are typically gathered in-person. However, the COVID-19 pandemic forced many behavioral research labs to close. Thankfully, new tools for conducting online experiments allow researchers to elicit psychological responses and gather behavioral data with unprecedented precision. It is now possible to quickly conduct large-scale high-quality behavioral experiments online, even for studies designed to generate data necessary for complex computational models. However, these techniques require new skills that might be unfamiliar to behavioral researchers who are more familiar with lab-based experimentation. We present a detailed tutorial introducing an end-to-end build of an online experimental pipeline and corresponding data analysis. We provide an example study investigating people’s media preferences using drift diffusion modeling (DDM), paying particular attention to potential issues that come with online behavioral experimentation. This tutorial includes sample data and code for conducting and analyzing DDM data gathered in an online experiment, thereby mitigating the extent to which researchers must reinvent the wheel.
Computationally modeling mood management theory: a drift-diffusion model of people’s preferential choice for valence and arousal in media

Xuanjun (Jason) Gong, Richard Huskey, Allison Eden, and Ezgi Ulusoy

Journal of Communication, Jun 2023

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Mood management theory (MMT) hypothesizes that people select entertainment content to maintain affective homeostasis. However, this hypothesis lacks a formal quantification of each affective attributes’ separate impact on an individual’s media content selection, as well as an integrated cognitive mechanism explaining media selection. Here we present a computational decision-making model that mathematically formalizes this affective media decision-making process. We empirically tested this formalization with the drift-diffusion model using three decision-making experiments. Contrary to MMT, all three studies showed that people prefer negatively valenced and high-arousal media content and that prevailing mood does not shape media selection as predicted by MMT. We also discovered that people are less cautious when choices have larger valence differences. Our results support the proposed mathematical formalization of affective attributes’ influence on media selection, challenge core predictions drawn from MMT, and introduce a new mechanism (response caution) for media selection.
Broadcast information diffusion processes on social media networks: exogenous events lead to more integrated public discourse

Xuanjun (Jason) Gong, Richard Huskey, Haoning Xue, Cuihua Shen, and Seth Frey

Journal of Communication, Apr 2023

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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.

2022

COVID-19 vaccine fact-checking posts on Facebook: Observational study.

Haoning Xue, Xuanjun (Jason) Gong, and Hannah Stevens

Journal of medical Internet research, Apr 2022

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Background: Effective interventions aimed at correcting COVID-19 vaccine misinformation, known as fact-checking messages, are needed to combat the mounting antivaccine infodemic and alleviate vaccine hesitancy. Objective: This work investigates (1) the changes in the public’s attitude toward COVID-19 vaccines over time, (2) the effectiveness of COVID-19 vaccine fact-checking information on social media engagement and attitude change, and (3) the emotional and linguistic features of the COVID-19 vaccine fact-checking information ecosystem. Methods: We collected a data set of 12,553 COVID-19 vaccine fact-checking Facebook posts and their associated comments (N=122,362) from January 2020 to March 2022 and conducted a series of natural language processing and statistical analyses to investigate trends in public attitude toward the vaccine in COVID-19 vaccine fact-checking posts and comments, and emotional and linguistic features of the COVID-19 fact-checking information ecosystem. Results: The percentage of fact-checking posts relative to all COVID-19 vaccine posts peaked in May 2020 and then steadily decreased as the pandemic progressed (r=–0.92, df=21, t=–10.94, 95% CI –0.97 to –0.82, P<.001). The salience of COVID-19 vaccine entities was significantly lower in comments (mean 0.03, SD 0.03, t=39.28, P<.001) than in posts (mean 0.09, SD 0.11). Third-party fact checkers have been playing a more important role in more fact-checking over time (r=0.63, df=25, t=4.06, 95% CI 0.33-0.82, P<.001). COVID-19 vaccine fact-checking posts continued to be more analytical (r=0.81, df=25, t=6.88, 95% CI 0.62-0.91, P<.001) and more confident (r=0.59, df=25, t=3.68, 95% CI 0.27-0.79, P=.001) over time. Although comments did not exhibit a significant increase in confidence over time, tentativeness in comments significantly decreased (r=–0.62, df=25, t=–3.94, 95% CI –0.81 to –0.31, P=.001). In addition, although hospitals receive less engagement than other information sources, the comments expressed more positive attitudinal valence in comments compared to other information sources (b=0.06, 95% CI 0.00-0.12, t=2.03, P=.04). Conclusions: The percentage of fact-checking posts relative to all posts about the vaccine steadily decreased after May 2020. As the pandemic progressed, third-party fact checkers played a larger role in posting fact-checking COVID-19 vaccine posts. COVID-19 vaccine fact-checking posts continued to be more analytical and more confident over time, reflecting increased confidence in posts. Similarly, tentativeness in comments decreased; this likewise suggests that public uncertainty diminished over time. COVID-19 fact-checking vaccine posts from hospitals yielded more positive attitudes toward vaccination than other information sources. At the same time, hospitals received less engagement than other information sources. This suggests that hospitals should invest more in generating engaging public health campaigns on social media.

2021

Flexible and Modular Brain Network Dynamics Characterize Flow Experiences During Media Use: A Functional Magnetic Resonance Imaging Study

Richard Huskey, Justin Robert Keene, Shelby Wilcox, Xuanjun (Jason) Gong, Robyn Adams, and 1 more author

Journal of Communication, Nov 2021

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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.