Abstract

The small screen size and rushed nature of mobile phone use create bandwidth limitations for users, leading them to be less deliberate and more spontaneous in their online interactions. This has resulted in the widespread phenomenon of sharing without clicking. When online users see a link or headline that appears to be aligned with their ideology, they are more likely to share it rather than scrutinizing the contents of the link or story. Does this mean they are being more honest in expressing their stance when they share without clicking? We seek to answer this question by investigating users’ sharing of political content on Facebook. What types of content are more often shared without clicking? Would the content shared without clicking be less moderate and more closely aligned with the user’s ideological affiliation? We expect to see more sharing of extreme, rather than moderate, political content and more often without clicking, which would be indicative of spontaneity. We expect to pair this data with concurrent investigations on the role played by device in predicting sharing behaviors, wherein we expect mobile device use to be associated with more spontaneous sharing of political content. Together, our findings can advance our understanding of the role played by communication technologies and the affordances and contexts of social media in influencing online political discussion, and thereby inform design of interfaces and interventions for fostering more robust deliberation.

Principal Investigator

S. Shyam Sundar

James P. Jimirro Professor of Media Effects & Codirector, Media Effects Research Laboratory, Pennsylvania State University

Bio

S. Shyam Sundar (PhD, Stanford University) is James P. Jimirro Professor of Media Effects and founding director of the Media Effects Research Laboratory at Pennsylvania State University (http://bellisario.psu.edu/people/individual/s.-shyam-sundar). His research investigates social and psychological effects of digital media, including mobile phones, social media, chatbots, robots, smart speakers, and algorithms. His experiments examine the role played by technological affordances such as interactivity in shaping user experience of mediated communications in a variety of interfaces. He has extensively studied online sources and their effects on information credibility. An ongoing project funded by the National Science Foundation explicates fake news, building a taxonomy of different kinds of false information for the purpose of developing machine-learning tools that can automatically detect fake news. Sundar edited the first-ever Handbook on the Psychology of Communication Technology (Wiley-Blackwell, 2015) and served as editor-in-chief of the Journal of Computer-Mediated Communication from 2013 to 2017. Identified as the most published author of internet-related research in the field of communication during the medium’s first decade, Sundar is a Fellow of the International Communication Association and recipient of the Paul J. Deutschmann Award for Excellence in Research from the Association for Education in Journalism and Mass Communication.

Participants

Guangqing Chi

Associate Professor of Rural Sociology and Demography and Public Health Sciences, Pennsylvania State University

Bio

Guangqing Chi is an associate professor of rural sociology and demography and director of the Computational and Spatial Analysis Core of the Social Science Research Institute and Population Research Institute at the Pennsylvania State University. His research is focused on socio-environmental systems, aiming to understand the interactions between human populations and built and natural environments and to identify important assets (social, environmental, infrastructural, institutional) to help vulnerable populations adapt and become resilient to environmental changes. He studies climate-driven migration and left-behind children in Central Asia, permafrost erosion impacts on coastal communities in Alaska, and ecological migration in China. He is an expert in spatial analysis and an author of the book Spatial Regression Models for the Social Sciences (SAGE, 2019). He leads the establishment of an infrastructure for collecting and managing Twitter data as well as a capacity in processing and analyzing the 50 TB data that have been collected. He also leads a project funded by the National Science Foundation to study the (mis)representativeness of Twitter data and develop weights to generalize the data; this endeavor will create opportunities for social scientists to take advantage of the rich social media data.

Jinping Wang

PhD Student, Donald P. Bellisario College of Communications, Pennsylvania State University

Bio

Jinping Wang is a doctoral candidate of mass communications at Pennsylvania State University. Her research interests include social and psychological implications of communication technologies. Specifically, she focuses on how different technological affordances shape the way individuals disclose their opinions, emotions, and identities, as well as how these expressions continue to influence both senders and receivers. She also has computational and programming skills to scrape web data and analyze them using techniques such as natural language processing, machine learning, and network analysis. Her work has appeared in leading conferences and journals and has won several awards.

Junjun Yin

Assistant Research Professor, Social Science Research Institute & Associate, Institute for Computational and Data Sciences, Pennsylvania State University

Bio

Junjun Yin is an assistant research professor at the Social Science Research Institute and Population Research Institute, Pennsylvania State University. His research interests center on computational GIScience with a focus on developing geospatial Big Data analytics for studying urban and population dynamics concerning urban mobility, accessibility, and sustainability. To exploit the research opportunities brought by various types of geospatial Big Data, one of his current research themes is collaborating with other social scientists using Twitter data as a geospatial Big Data source for addressing social problems and societal issues. His work in using Big Data for social science research is enabled by state-of-the-art high-performance computing environments supported by the Extreme Science and Engineering Discovery Environment (XSEDE) and the Institute for Computational and Data Sciences at Penn State.