Current Projects

Politicization of Science:  Partisan cues vs scientific literacy

The politicization of science issues poses a critical challenge to scientist communities and policy decisions.  Climate change is a well-known example.  But how much the partisan endorsement contributes to opinion polarization about controversial and even non-controversial science issues?  For example, if Trump criticizes a scientific findings that have been rigorously established, would this sway public opinion among Republicans? Also, what if we show an article that Trump firmly endorse the link between vaccines and autism or harmfulness of e-cigarettes?

Principle Investigator:  Mo Jang

Co-investigators: Chris Noland, Joon Kim (U South Carolina), Minji Kim (U C S F)

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Artificial Intelligence and Attribution

While it is expected that AI is replacing more and more of human-decisions, there has been an increasing concern about who is responsible for the failure (or negative consequences) of AI (e.g., accident caused by self-driving cars). We want to think about AI judges, AI doctors, and AI immigration control officers, and how people will attribute negative decisions from AIs. Eventually, we’re comparing the degree of frustration based on such negative decisions from AI and humans.

Principle Investigator:  Mo Jang

Co-investigator: Yong Jin Park (Howard U)

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How much can we trust self-reported measures of social media use?

Although the discipline has heavily relied on self-reported measure of social media use, criticisms have prevailed about the validity of such measures. Particularly in this high-choice media environment it’s extremely difficult to reasonably expect survey respondents to accurately recall their social media use.  This project ambitiously creates an app to crawl users’ Facebook activities and compared the digital footprint data against self-reports of the correspondent Facebook account owners.  This project will assess the bias inherent self-reports and revisit a number of important social questions with news kinds of data.

Principle Investigator:  Mo Jang

Co-investigator: Hongrui Zhang (U South Carolina, app developer) 

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Frame contagion of climate change frames

Are social media a rumor mill which generates and spreads misinformation about science topics such as hoax frames of climate change? OR are social media simply reflecting and reinforcing the partisan divide manifest on partisan news media? Using a time-series, we investigate the flow of information, including climate change agenda, and various climate change frames.

Principle Investigator:  Mo Jang

Co-investigator: Sol Hart (U Michigan) Lauren Feldman (Rutgers U) Wonki Moon (UT Austin)

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Fake news literacy

This project focuses on the ability to identify fake news, so-called ‘fake news literacy,’ and examines how it is related to demographics characteristics, various media literacy scales, and other key social variables. First, this study investigates how the characteristics of socio-demographics leads to disparities in fake news literacy. The theoretical motivation of this investigation is to import insights from the digital divide literature into fake news phenomenon. Second, this research explores whether existing literacy measures (e.g., media literacy, digital literacy, information literacy) are related to fake news identification. The outcome of this examination will inform us what kinds of knowledge or skill sets are most required for competent news consumers in an era of post-truth. Finally, this study further identifies social variables that lead to the achievement of fake news literacy. These variables include social capital, information seeking behavior, political interest, and online and offline discussion about fake news.

Principle Investigator:  Mo Jang

Co-investigator: Tara Mortensen, Jingjing Liu (U South Carolina)

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Fake news effects

Despite many concerns about the negative effects of fake news, little empirical evidence was provided to demonstrate such effects. This project collected 2-week long panel data to show lagged effects of fake news exposure (prior to the election in 2018) on subsequent cynicism.

Principle Investigator:  Mo Jang

Co-investigator: Dam Hee Kim (Arizona U) Kate Kenski (Arizona U)

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Assessing the bias from self-reported smartphone use

Despite long-standing concerns over inaccurate self-reported measures of media use, media research has relied heavily on self-reported data. This study not only examines discrepancies between survey and logged smartphone data, but also assesses whether correlational outcomes using self-reported measures inflate or attenuate effect sizes compared to outcomes using logged measures.

Principle Investigator:  Mo Jang

Co-investigator: Yujin Heo, Robert McKeever, Leigh Moscowitz, David Moscowitz (U South Carolina), Jung Hyun Kim (Sogang U, South Korea)

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