Researching Real-World Web Use with Roxy: Collecting Observational Web Data with Informed Consent

Outside of a laboratory environment, it has been difficult for researchers to collect both behavioral and self-reported Web use data from the same participants. To address this challenge, we created Roxy, which is software that collects real-world Web-use data with participants' informed consent. Roxy gathers Web log data as well as the text and HTML code of each page visited by participants. In this workbench note, we describe Roxy's data-gathering capabilities and search functions, then illustrate how we used the software in a multimethod study. The use case examines selective exposure to political communication during the November 2010 U.S. general election campaign

Wikis in the Classroom: An Agenda for Studying Collaborative Writing in Information Systems Research

This paper proposes a research agenda for a relatively new area of research in information systems: wikis in collaborative writing. We introduce wikis and the concept of collaborative writing using four different educational cases of wiki-usage for collaborative writing in the classroom setting. Eight research questions are suggested related to this area of research. We propose that Adaptive Structuration Theory (AST) is a useful theoretical framework to study these questions. The paper suggests the importance of this new area of research through four case studies and identifying research questions that need to be addressed using the AST framework and suggesting implications for educational practice

Researching Real-World Web Use with Roxy, A Research Proxy

Outside of a lab environment, it has been difficult for researchers to collect both behavioral and self-reported Web-use data from the same participants. To address this challenge we created Roxy, open source software that collects real-world Web-use data with participants’ informed consent. Roxy gathers Web log data as well as the text and HTML code of each page visited by participants. We describe Roxy’s data gathering capabilities and search functions and then illustrate how we used the software in a multi-method study. The use case examines selective exposure to political communication during the November 2010 U.S. general election campaign

Exposure to Extremely Partisan News from the Other Political Side Shows Scarce Boomerang Effects.

A narrow information diet may be partly to blame for the growing political divides in the United States, suggesting exposure to dissimilar views as a remedy. These efforts, however, could be counterproductive, exacerbating attitude and affective polarization. Yet findings on whether such boomerang effect exists are mixed and the consequences of dissimilar exposure on other important outcomes remain unexplored. To contribute to this debate, we rely on a preregistered longitudinal experimental design combining participants survey self-reports and their behavioral browsing data, in which one should observe boomerang effects. We incentivized liberals to read political articles on extreme conservative outlets (Breitbart, The American Spectator, and The Blaze) and conservatives to read extreme left-leaning sites (Mother Jones, Democracy Now, and The Nation). We maximize ecological validity by embedding the treatment in a larger project that tracks over time changes in online exposure and attitudes. We explored the effects on attitude and affective polarization, as well as on perceptions of the political system, support for democratic principles, and personal well-being. Overall we find little evidence of boomerang effects

Exposure to extremely partisan news from the other political side shows scarce boomerang effects

A narrow information diet may be partly to blame for the growing political divides in the United States, suggesting exposure to dissimilar views as a remedy. These efforts, however, could be counterproductive, exacerbating attitude and affective polarization. Yet findings on whether such boomerang effect exists are mixed and the consequences of dissimilar exposure on other important outcomes remain unexplored. To contribute to this debate, we rely on a preregistered longitudinal experimental design combining participants survey self-reports and their behavioral browsing data, in which one should observe boomerang effects. We incentivized liberals to read political articles on extreme conservative outlets (Breitbart, The American Spectator, and The Blaze) and conservatives to read extreme left-leaning sites (Mother Jones, Democracy Now, and The Nation). We maximize ecological validity by embedding the treatment in a larger project that tracks over time changes in online exposure and attitudes. We explored the effects on attitude and affective polarization, as well as on perceptions of the political system, support for democratic principles, and personal well-being. Overall we find little evidence of boomerang effects

Web Historian - Community Edition

<p>Chrome browser extension designed to integrate web browsing history data collection into research projects collecting other types of data from participants (e.g. surveys, in-depth interviews, experiments). It uses client-side D3 visualizations to inform participants about the data being collected during the informed consent process. It allows participants to delete specific browsing data or opt-out of browsing data collection. It directs participants to an online survey once they have reviewed their data and made a choice of whether to participate. It has been used with Qualtrics surveys, but any survey that accepts data from a URL will work. It works with the open source <a href="https://passivedatakit.org/">Passive Data Kit</a> (PDK) as the backend for data collection. To successfully upload, you need to fill in the address of your PDK server in the js/app/config.js file.</p

Political content and news are polarized but other content is not in YouTube watch histories

Research on ideological biases and polarization on social media platforms primarily focuses on news and political content. Non-political content, which is vastly more popular, is often overlooked. Because partisanship is correlated with citizens’ non-political attitudes and non-political content can carry political cues, we explore whether ideological biases and partisan segregation extend to users’ non-political exposures online. We focus on YouTube, one of the most popular platforms. We rely online data from American adults (N = 2,237). From over 129 million visits to over 37 million URLs, we analyze 1,037,392 visits to YouTube videos from 1,874 participants. We identify YouTube channels of 942 news domains, utilize a BERT-based classifier to identify political videos outside news channels, and estimate the ideology of all the videos in our data. We compare ideological biases in exposure to (a) news, (b) political, and (c) non-political content. We examine both exposure congeniality (i.e., are users consuming like-minded content?) and polarization (i.e. are there overlaps between Democrats and Republicans in the content they consume?). We find substantial congeniality in the consumption of news and political videos, especially among Republicans, and high levels of polarization in this exposure (i.e., limited overlaps between Democrats and Republicans). We also show that both exposure congeniality and polarization are significantly lower for non-political content, in that non-political videos are less likely to be ideologically like-minded and both Democrats and Republicans consume similar non-political content. Theoretical and practical implications of these findings are discussed