Derek L. Hansen

The impact of polices on government social media usage: Issues, challenges, and recommendations

Abstract Government agencies are increasingly using social media to connect with those they serve. These connections have the potential to extend government services, solicit new ideas, and improve decision-making and problem-solving. However, interacting via social media introduces new challenges related to privacy, security, data management, accessibility, social inclusion, governance, and other information policy issues. The rapid adoption of social media by the population and government agencies has outpaced the regulatory framework related to information, although the guiding principles behind many regulations are still relevant. This paper examines the existing regulatory framework and the ways in which it applies to social media use by the U.S. federal government, highlighting opportunities and challenges agencies face in implementing them, as well as possible approaches for addressing these challenges.

Dynamic changes in motivation in collaborative citizen-science projects

Online citizen science projects engage volunteers in collecting, analyzing, and curating scientific data. Existing projects have demonstrated the value of using volunteers to collect data, but few projects have reached the full collaborative potential of scientists and volunteers. Understanding the shared and unique motivations of these two groups can help designers establish the technical and social infrastructures needed to promote effective partnerships. We present findings from a study of the motivational factors affecting participation in ecological citizen science projects. We show that volunteers are motivated by a complex framework of factors that dynamically change throughout their cycle of work on scientific projects; this motivational framework is strongly affected by personal interests as well as external factors such as attribution and acknowledgment. Identifying the pivotal points of motivational shift and addressing them in the design of citizen-science systems will facilitate improved collaboration between scientists and volunteers.

Computing political preference among twitter followers

There is great interest in understanding media bias and political information seeking preferences. As many media outlets create online personas, we seek to automatically estimate the political preferences of their audience, rather than of the outlet itself. In this paper, we present a novel method for computing preference among an organizations Twitter followers. We present an application of this technique to estimate political preference of the audiences of U.S. media outlets. We also discuss how these results may be used and extended.

Social Participation in Health 2.0

Computer scientists are working with biomedical researchers, policy specialists, and medical practitioners to usher in a new era in healthcare. A recently convened panel of experts considered various research opportunities for technology-mediated social participation in Health 2.0.

First Steps to Netviz Nirvana: Evaluating Social Network Analysis with NodeXL

Social Network Analysis (SNA) has evolved as a popular, standard method for modeling meaningful, often hidden structural relationships in communities. Existing SNA tools often involve extensive pre-processing or intensive programming skills that can challenge practitioners and students alike. NodeXL, an open-source template for Microsoft Excel, integrates a library of common network metrics and graph layout algorithms within the familiar spreadsheet format, offering a potentially low-barrier-to-entry framework for teaching and learning SNA. We present the preliminary findings of 2 user studies of 21 graduate students who engaged in SNA using NodeXL. The majority of students, while information professionals, had little technical background or experience with SNA techniques. Six of the participants had more technical backgrounds and were chosen specifically for their experience with graph drawing and information visualization. Our primary objectives were (1) to evaluate NodeXL as an SNA tool for a broad base of users and (2) to explore methods for teaching SNA. Our complementary dual case-study format demonstrates the usability of NodeXL for a diverse set of users, and significantly, the power of a tightly integrated metrics/visualization tool to spark insight and facilitate sense-making for students of SNA.

Supporting content curation communities: The case of the Encyclopedia of Life

This article explores the opportunities and challenges of creating and sustaining large-scale “content curation communities” through an in-depth case study of the Encyclopedia of Life (EOL). Content curation communities are large-scale crowdsourcing endeavors that aim to curate existing content into a single repository, making these communities different from content creation communities such as Wikipedia. In this article, we define content curation communities and provide examples of this increasingly important genre. We then follow by presenting EOL, a compelling example of a content curation community, and describe a case study of EOL based on analysis of interviews, online discussions, and survey data. Our findings are characterized into two broad categories: information integration and social integration. Information integration challenges at EOL include the need to (a) accommodate and validate multiple sources and (b) integrate traditional peer reviewed sources with user-generated, nonpeer-reviewed content. Social integration challenges at EOL include the need to (a) establish the credibility of open-access resources within the scientific community and (b) facilitate collaboration between experts and novices. After identifying the challenges, we discuss the potential strategies EOL and other content curation communities can use to address them, and provide technical, content, and social design recommendations for overcoming them.

A method for computing political preference among Twitter followers

Abstract There is great interest in understanding media bias and political information seeking preferences. As many media outlets create online personas, we seek to automatically estimate the political preferences of their audience, rather than examining the bias of the media source. In this paper, we present a novel method for computing the political preferences of an organizations Twitter followers. We present an application of this technique to estimate the political preferences of the audiences of U.S. media outlets, government agencies, and interest groups and think tanks. We also discuss how these results may be used and extended.

Motivations Affecting Initial and Long-Term Participation in Citizen Science Projects in Three Countries

Reliance on volunteer participation for citizen science has become extremely popular. Cutting across disciplines, locations, and participation practices, hundreds of thousands of volunteers throughout the world are helping scientists accomplish tasks they could not otherwise perform. Although existing projects have demonstrated the value of involving volunteers in data collection, relatively few projects have been successful in maintaining volunteers’ continued involvement over long periods of time. Therefore, it is important to understand the temporal nature of volunteers’ motivations and their effect on participation practices, so that effective partnerships between volunteers and scientists can be established. This paper presents case studies of longitudinal participation practices in citizen science in three countries—the United States, India, and Costa Rica. The findings reveal a temporal process of participation, in which initial participation stems in most cases from self-directed motivations, such as personal interest. In contrast, long-term participation is more complex and includes both self-directed motivations and collaborative motivations.

PLAYING IN THE SAME TWITTER NETWORK

New media platforms such as Twitter allow users to choose from a wide range of political information sources including traditional media, politicians, bloggers and other Twitter users. This study extends the theory of channel complementarity by considering the complementary selection of information sources that occur within a single social media space. This study also conceptualizes information seeking as a social network created by follow relationships on Twitter, allowing tracking actual patterns of source selection. We applied this theoretical and methodological framework to examine information seeking on Twitter related to four US gubernatorial races. We identified the main types of highly followed accounts (i.e. information hubs). Traditional news media outlets (national and local) accounted for nearly half of the information hubs, and grassroots sources such as activists and other users accounted for nearly a third. The patterns of follow relationships indicated local and national subgroups (i.e. clusters) of users identified based on network topology. Local clusters included a subgroup of more densely interconnected users in which local news media and political candidates were hubs. National clusters included a subgroup of more sparsely interconnected users in which national media and online-only news sources served as hubs. High-density clusters were also more likely to host information sources that exhibited two-way information flow with other users, while low-density clusters preferred hubs that follow traditional one-to-many information flow. Theoretical and practical implications for news media and political candidates are discussed.

Mixing it up: recommending collections of items

Recommender systems traditionally recommend individual items. We introduce the idea of collection recommender systems and describe a design space for them including 3 main aspects that contribute to the overall value of a collection: the value of the individual items, co-occurrence interaction effects, and order effects including placement and arrangement of items. We then describe an empirical study examining how people create mix tapes. The study found qualitative and quantitative evidence for order effects (e.g., first songs are rated higher than later songs; some songs go poorly together sequentially). We propose several ideas for research in this space, hoping to start a much longer conversation on collection recommender systems.