No abstract

No abstract

While emerging technologies such as social media have demonstrated value for crisis communications, significant question remains regarding how these tools can be most effectively leveraged to facilitate the flow of valid information under crisis conditions. In an effort to address these issues, this article examines the use of Twitter during the 2015-2016 Zika virus outbreak in the United States. Particular attention is paid to network structures within the Zika conversation and how different actors and communities contribute to the flow of information throughout the broader Twitter community. Public-facing organizations can benefit from a deeper understanding of the nature and structure of spontaneously occurring communities on social media as well as the types of content that they create and circulate. As such, these findings have significant implications for the development of effective social media strategies during natural disasters and public health emergencies. In particular, this analysis identifies several predominant themes communicated through Zika-related tweets as well as a number of distinct communities and influential actors. The findings suggest that respected political actors, public institutions, as well as those with valid scientific credentials can help to facilitate the flow of accurate and vital information across disparate communities.

This study aims to reveal patterns of e-petition co-signing behavior that are indicative of the political mobilization of online “communities”. We discuss the case of We the People, a US national experiment in the use of social media technology to enable users to propose and solicit support for policy suggestions to the White House. We apply Baumgartner and Jones's work on agenda setting and punctuated equilibrium, which suggests that policy issues may lie dormant for periods of time until some event triggers attention from the media, interest groups, and elected representatives. In the case study presented, we focus on 21 petitions initiated during the week after the Sandy Hook shooting (14–21 December 2012) in opposition to gun control or in support of policy proposals that are alternatives to gun control, which we view as mobilized efforts to maintain stability and equilibrium in a policy system threatening to change. Using market basket analysis and social network analysis we found a core group of petitions in the “support law-abiding gun owners” theme that were highly connected and four “communities” of e-petitioners mobilizing in opposition to change in gun control policies and in favor of alternative proposals.

The growing influence of social bots in political discussion networks has raised significant concerns, particularly given their potential to adversely impact democratic outcomes. In this study, we report the results of a case study analysis of bot activity in a recent, high-profile political discussion network. Specifically, we examine the prevalence and impact of bots in a Twitter network discussing the Special Counsel investigation into Russian interference in the 2016 U.S. elections. Using this discussion network, we conduct a “before-and-after” analysis to examine the prevalence of social bots in the discussion network as well as their influence on key network features such as (1) network structure, (2) content/messaging, (3) sentiment, and (4) influentialness. Our findings suggest that bots can affect political discussion networks in several significant ways. We found that bot-like accounts created the appearance of a virtual community around far-right political messaging, attenuated the influence of traditional actors (i.e., media personalities, subject matter experts), and influenced network sentiment by amplifying pro-Trump messaging. The results of this analysis add to a growing body of literature on the use and influence of social bots while at the same time uniquely examining their influence in a nonelectoral, political setting.

No abstract

The Information Schools, also referred to as iSchools, have a unique approach to data science with three distinct components: human‐centeredness, socially responsible, and rooted in context. In this position paper, we highlight and expand on these components and show how they are integrated in various research and educational activities related to data science that are being carried out at iSchools. We argue that the iSchool way of doing data science is not only highly relevant to the current times, but also crucial in solving problems of tomorrow. Specifically, we accentuate the issues of developing insights and solutions that are not only data‐driven, but also incorporate human values, including transparency, privacy, ethics, fairness, and equity. This approach to data science has meaningful implications on how we educate the students and train the next generation of scholars and policymakers. Here, we provide some of those design decisions, rooted in evidence‐based research, along with our perspective on how data science is currently situated and how it should be advanced in iSchools.

Electronic petitioning (e-petitioning) provides a unique and promising channel through which people can directly express their policy preferences. E-petitions may be viewed as a natural laboratory for determining subjects of public interest, and thus can be used by policy analysts to understand social needs and constraints. In this paper, we introduce textual analysis tools (such as NER and topic modeling) and extract three types of novel variables (informativeness, named entities, and 21 topics) from We the People petition texts. The regression result shows that informativeness, named location, and several topics are significantly correlated with the log of the signature counts. These exploratory but promising results indicate that textual analysis tools can complement traditional statistical methods by providing descriptive measures that are helpful for making causal inferences from electronic petition data. These new tools, we believe, will facilitate policy analysis and policy informatics by enabling meaningful use of large volumes of online archives containing public expression regarding policy preferences.