Kaiping Chen

• Doctor of Philosophy
• Professor (Assistant) at University of Wisconsin–Madison

Deliberative exercises may reinvigorate civic life by building citizens’ capacity to engage in other types of civic activities. This study examines members of a citizens’ panel ( n = 56) who participated in a 6-day deliberative event on climate change and energy transition in Edmonton, Alberta (Canada), in 2012. We compared panellists’ civic engage...

This paper examines the impact of crowdsourcing on a policymaking process by using a novel data analytics tool called Civic CrowdAnalytics, applying Natural Language Processing (NLP) methods such as concept extraction, word association and sentiment analysis. By drawing on data from a crowdsourced urban planning process in the City of Palo Alto in...

This theoretical viewpoint paper presents a new perspective on urban governance in an information age. Smart city governance is not only about technology but also about re-organizing collaboration between a variety of actors. The introduction of new tools for open collaboration in the public domain is rapidly changing the way collaborative action i...

While crowdsourcing is an increasingly common method of open-government practices to strengthen participatory democracy, its impact on governance is unclear. Using data from a crowdsourced city-plan update by the City of Palo Alto, California, this paper examines the impact of a crowd’s input on policy changes. We used an enacted policy change to q...

This paper examines the impact of an augmented reality (AR) tour guide on users’ art engagement. In a between-subjects experiment in an art museum, users’ art engagement with a novel video see-through augmented reality guide was examined against user behavior with a book guide. The AR users’ liking of art increased more than the book users’, wherea...

While researchers often study message features like moral content in text, such as party manifestos and social media posts, their quantification remains a challenge. Conventional human coding struggles with scalability and intercoder reliability. While dictionary-based methods are cost-effective and computationally efficient, they often lack contex...

BACKGROUND Effective communication is essential for promoting preventive behaviors during infectious disease outbreaks like COVID-19. While consistent news can better inform the public about these health behaviors, the public may not adopt them. OBJECTIVE This study aims to explore the role of different media platforms in shaping public discourse...

Background Effective communication is essential for promoting preventive behaviors during infectious disease outbreaks like COVID-19. While consistent news can better inform the public about these health behaviors, the public may not adopt them. Objective This study aims to explore the role of different media platforms in shaping public discourse...

With the rise of digital media, conspiracy theories infamous for their emotional manipulation have challenged science epistemology and democratic discourse. Despite extensive literature on misinformation and the role of emotion in persuasion, less is understood about how emotion is used in conspiracy and debunking messages on video platforms and th...

In the evolving landscape of computer vision (CV) technologies, the automatic detection and interpretation of gender and emotion in images is a critical area of study. This paper investigates social biases in CV models, emphasizing the limitations of traditional evaluation metrics such as precision, recall, and accuracy. These metrics often fall sh...

Environmental issues already disproportionately impact communities of color in the U.S., where there is a long and well-established history of environmental racism. Despite efforts to diversify voices in deliberation, engaging marginalized populations remains a key challenge. Drawing from environmental communication, civic empowerment, and social i...

This research aims to address gaps in the knowledge and practice of engaging underrepresented communities in public communication and policymaking on environmental justice issues. Through a quasi-field experiment conducted in Madison, Wisconsin, we investigate how Black and Latinx communities perceive environmental justice issues and how these perc...

This study examines how gender stereotypes are reflected in discourses around controversial science issues across two platforms, YouTube and TikTok. Utilizing the Social Identity Model of Deindividuation Effects, we developed hypotheses and research questions about how content creators might use gender-related stereotypes to engage audiences. Our a...

Autoregressive language models, which use deep learning to produce human-like texts, have surged in prevalence. Despite advances in these models, concerns arise about their equity across diverse populations. While AI fairness is discussed widely, metrics to measure equity in dialogue systems are lacking. This paper presents a framework, rooted in d...

Diversity in knowledge production is a core challenge facing science communication. Despite extensive works showing how diversity has been undermined in science communication, little is known about to what extent social media augments or hinders diversity for science communication. This article addresses this gap by examining the profile and networ...

Contention over COVID-19 is only a recent example of increasing social division around science in the U.S. Many blame these divisions on actors who have strategically sowed doubt and distrust around expert supported positions and policies. However, this overlooks how scientists have fueled narratives of social and political conflict around science....

The COVID-19 pandemic unleashed a torrent of conspiracy theories across different social media platforms. Parallel to this conspiracy wave was a heightened sense of nationalism, which manifested through both in-group solidarity and perceived out-group threats. In this study, we examine how individuals’ use of government social media to gather polit...

There are growing concerns about the role of identity narratives in spreading misinformation on social media, which threatens informed citizenship. Drawing on the social identity model of deindividualization effects (SIDE) and social identity theory, we investigate how the use of national identity language is associated with the diffusion and disco...

Social media have become popular channels for sharing and discussing science issues. Drawing from the classic communication theory, Public Arena Model, this paper examines how issue entrepreneurs influenced the Chinese public's cognition of GMO, especially the role of celebrities and scientists in controversial science communication. To answer this...

Autoregressive language models, which use deep learning to produce human-like texts, have become increasingly widespread. Such models are powering popular virtual assistants in areas like smart health, finance, and autonomous driving. While the parameters of these large language models are improving, concerns persist that these models might not wor...

Twitter enables an online public sphere for social movement actors, news organizations, and others to frame climate change and the climate movement. In this paper, we analyze five million English tweets posted from 2018 to 2021 demonstrating how peaks in Twitter activity relate to key events and how the framing of the climate strike discourse has e...

This paper synthesizes the efforts of an interdisciplinary, University-convened communication task force in the U.S. that used science communication theory to develop an effective strategy during the early stages of the COVID-19 pandemic. We outline recommendations for researchers and practitioners who are, or are interested in, implementing theory...

Misinformation and intergroup bias are two pathologies challenging informed citizenship. This article examines how identity language is used in misinformation and debunking messages about controversial science on the Chinese digital public spheres and their impact on how the public engage with science. We collected an 8-year time series dataset of...

Misinformation and intergroup bias are two pathologies challenging informed citizenship. This paper examines how identity language is used in misinformation and debunking messages about controversial science on Chinese digital public sphere, and their impact on how the public engage with science. We collected an eight-year time series dataset of pu...

Climate change is a critical global problem that requires immediate action to mitigate its effects. In recent years, youth climate activists have mobilized worldwide protests to demand action, using social media platforms to communicate and broadcast their message. This study examines Greta Thunberg's rise to global prominence through an analysis o...

We examined initial newspaper coverage of the COVID-19 outbreak (January–May 2020) in the United States and China, countries with contrasting media systems and pandemic experiences. We join the context-rich media systems literature and the longitudinal nature of the issue-attention literature to expand each by providing more system-level context fo...

Gene editing in the environment must consider uncertainty about potential benefits and risks for different populations and under different conditions. There are disagreements about the weight and balance of harms and benefits. Deliberative and community-led approaches offer the opportunity to engage and empower diverse publics to co-create response...

Paralleling very visible debates in psychology, some parts of the communication field have recently pushed for a wholesale endorsement of the principles of open science and its practices, including a particular focus on replicability and reproducibility as quality criteria. Unfortunately, these discussions have been plagued by a set of at least 3 i...

When and how can researchers synthesize survey data with analyses of social media content to study public opinion, and when and how can social media data complement surveys to better inform researchers and policymakers? This paper explores how public opinions might differ between survey and social media platforms in terms of content and audience, f...

A bstract Scholars increasingly use Twitter data to study the life sciences and politics. However, Twitter data collection tools often pose challenges for scholars who are unfamiliar with their operation. Equally important, although many tools indicate that they offer representative samples of the full Twitter archive, little is known about whether...

Significance A human mobility flow-augmented stochastic susceptible–exposed–infectious–removed–style epidemic modeling framework is developed, which combines with data assimilation and machine learning to reconstruct the historical growth trajectories of COVID-19 infection in the two largest counties in Wisconsin. The associations between the sprea...

In a rapidly changing public health crisis such as COVID-19, researchers need innovative approaches that can effectively link qualitative approaches and computational methods. In this article, computational and qualitative methods are used to analyze survey data collected in March 2020 ( n = 2,270) to explore the content of persuasive messages and...

People are increasingly exposed to science and political information from social media. One consequence is that these sites play host to “alternative influencers,” who spread misinformation. However, content posted by alternative influencers on different social media platforms is unlikely to be homogenous. Our study uses computational methods to in...

Recent years have witnessed an explosion of science conspiracy videos on the Internet, challenging science epistemology and public understanding of science. Scholars have started to examine the persuasion techniques used in conspiracy messages such as uncertainty and fear yet, little is understood about the visual narratives, especially how visual...

This paper studies conspiracy and debunking narratives about the origins of COVID-19 on a major Chinese social media platform, Weibo, from January to April 2020. Popular conspiracies about COVID-19 on Weibo, including that the virus is human-synthesized or a bioweapon, differ substan-tially from those in the United States. They attribute more respo...

This paper studies conspiracy and debunking narratives about COVID-19 origination on a major Chinese social media platform, Weibo, from January to April 2020. Popular conspiracies about COVID-19 on Weibo, including that the virus is human-synthesized or a bioweapon, differ substantially from those in the US. They attribute more responsibility to th...

Empowering ordinary citizens with the capacity to deliberate is a core issue in science communication. Despite growing deliberative practices in developed nations, it is significantly less understood how public deliberation can happen among populations who live with limited educational resources and poor urban infrastructure in developing countries...

The novel coronavirus disease (COVID-19) pandemic is a global threat presenting health, economic and social challenges that continue to escalate. Meta-population epidemic modeling studies in the susceptible-exposed-infectious-removed (SEIR) style have played important roles in informing public health and shaping policy making to mitigate the spread...

Understanding how individuals perceive the barriers and benefits of precautionary actions is key for effective communication about public health crises, such as the COVID-19 outbreak. This study used innovative computational methods to analyze 30,000 open-ended responses from a large-scale survey to track how Wisconsin (U.S.A.) residents' perceptio...

Citizen participatory budgeting (CPB) is a mechanism to enhance governance, particularly regarding accountability and responsiveness. The growing literature on the topic, however, has not addressed the participation effectiveness of CPB in developing countries. Drawing mainly from first-hand data, this study examines the participation effectiveness...

A prerequisite for the durability of authoritarian regimes as well as their effective governance is the regime’s ability to gather reliable information about the actions of lower-tier officials. Allowing public participation in the form of online complaints is one approach authoritarian regimes have taken to improve monitoring of lower-tier officia...

While national and local governments increasingly deploy crowdsourcing in lawmaking as an open government practice, it remains unclear how crowdsourcing creates value when it is applied in policymaking. Therefore, in this article, we examine value creation in crowdsourcing for public policymaking. We introduce a framework for analysing value creati...

The development of computational data science techniques in natural language processing and machine learning algorithms to analyze large and complex textual information opens new avenues for studying the interaction between economics and politics. We apply these techniques to analyze the design of financial regulatory structure in the United States...

The development of computational data science techniques in natural language processing (NLP) and machine learning (ML) algorithms to analyze large and complex textual information opens new avenues to study intricate processes, such as government regulation of financial markets, at a scale unimaginable even a few years ago. This paper develops scal...