Search

Around the time the U.S. government was testing nuclear bombs on Bikini Atoll in the spring of 1954, residents of Bellingham, Washington, inspected their windshields and noticed holes, pits, and [...]

Around the time the US government was testing nuclear bombs on Bikini Atoll in the spring of 1954, residents of Bellingham, Washington, inspected their windshields and noticed holes, pits, and [...]

The 2020 US election was accompanied by an effort to spread a false meta-narrative of widespread voter fraud. This meta-narrative took hold among a substantial portion of the US population, undermining trust in election procedures and results, and eventually motivating the events of 6 January 2021. We examine this effort as a domestic and participatory disinformation campaign in which a variety of influencers—including hyperpartisan media and political operatives—worked alongside ordinary people to produce and amplify misleading claims, often unwittingly. To better understand the nature of participatory disinformation, we examine three cases of misleading claims of voter fraud, applying an interpretive, mixed method approach to the analysis of social media data. Contrary to a prevailing view of such campaigns as coordinated and/or elite-driven efforts, this work reveals a more hybrid form, demonstrating both top-down and bottom-up dynamics that are more akin to cultivation and improvisation. [ABSTRACT FROM AUTHOR]

We can anticipate many false claims, including alleged mass voting by non-citizens or ‘suspicious vans’ outside polling booths. We should quickly counter them. [ABSTRACT FROM AUTHOR]

This paper presents a new conceptual framework of the disaster risk of critical infrastructure systems in terms of societal impacts. Much research on infrastructure reliability focuses on specific issues related to the technical system or human coping. Focusing on the end goal of infrastructure services – societal functioning – this framework offers a new way to understand how those more focused research areas connect and the current thinking in each. Following an overview of the framework, each component is discussed in turn, including the initial buildout of physical systems; event occurrence; service interruptions; service provider response; user adaptations to preserve or create needed services; and the ending deficit in societal function. Possible uses of the framework include catalysing and guiding a systematic research agenda that could ultimately lead to a computational framework and stimulating discussion on resilience within utility and emergency management organisations and the larger community. [ABSTRACT FROM AUTHOR]

An editorial is presented which expresses the views of academics facing challenges to academic freedom in the U.S. Topics include the historical context of academic freedom, current political pressures against research on misinformation, and the impact of these pressures on scholars' work and safety.

This research examines how information about an oil spill, its impacts, and the use of dispersants to treat the oil, moved through social media and the surrounding Internet during the 2010 BP Deepwater Horizon oil spill. Using a collection of tweets captured during the spill, we employ a mixed-method approach including an in-depth qualitative analysis to examine the content of Twitter posts, the connections that Twitter users made with each other, and the links between Twitter content and the surrounding Internet. This article offers a range of findings to help practitioners and others understand how social media is used by a variety of different actors during a slow-moving, long-term, environmental disaster. We enumerate some of the most salient themes in the Twitter data, noting that concerns about health impacts were more likely to be communicated in tweets about dispersant use, than in the larger conversation. We describe the accounts and behaviors of highly retweeted Twitter users, noting how locals helped to shape the network and the conversation. Importantly, our results show the online crowd wanting to participate in and contribute to response efforts, a finding with implications for future oil spill response. [ABSTRACT FROM PUBLISHER]

The Deepwater Horizon (DWH) oil spill was a pivotal moment in the expression of and reporting on stakeholder risk perceptions about oil spills, response options, and safety. Public engagement through both traditional and social media was arguably much higher than in prior spills. The DWH response organization undertook a wide variety of activities to manage risks and communicate with both the general public and those directly affected, such as commercial fishers. However, these did not fully address widespread concerns about ecological and human health risks associated with dispersant use. Consequentially the DWH spill heightened awareness of persistent risk communication problems around oil spill response, and especially dispersant use. Oil spill risk research and experience suggests that institutional and operational factors inhibit engaging communities and stakeholders during oil spill preparedness and response, and that such engagement is essential for effective risk management. In this article we review and assess current oil spill preparedness and response practices for community and stakeholder engagement, including related institutional and operational constraints. This assessment suggests five example risk management practices to improve and advance risk communications during oil spill preparedness and response activities. [ABSTRACT FROM PUBLISHER]

Scenario analysis (SA) is the process of developing plausible futures around the forces affecting an organization in the face of uncertainties over which it has little control. SA is widely used in the private sector and increasingly a tool of environmental planners grappling with problems of great complexity and uncertainty. SA ideally marries expert judgment with the broader perspectives engaged stakeholders bring. While the 1990 Oil Pollution Act (OPA) brought substantive improvement to oil spill contingency planning, many issues remain. Reguatorily prescribed definitions of ‘worst case’ lead to SA practice that seldom achieves the full promise of the SA approach. Contingency planning overly focused on tactical and operational considerations can leave response managers little prepared to deal with public concerns that emerge in the event of a major spill, concerns increasingly magnified through social media. Politics continues to contribute to poorly conceived contingency planning in which adopted scenarios bear little resemblance to events that subsequently transpire. Risk attenuation and risk amplification both inhibit scenario-based planning around oil spills, evinced by the Deepwater Horizon spill. Improvements in pre-planning in the aftermath of theExxon Valdezoil spill nevertheless provide a foundation for more effective use of SA. [ABSTRACT FROM PUBLISHER]

Complexity and uncertainty influence opinions, beliefs, and decisions about health, safety, and other kinds of risk, as demonstrated in research on health, climate change, storm forecasts, pandemic disease, and in other domains. Drawing from this research, this article summarizes insights into how people understand and process uncertain or complex information and explores key oil spill and oil spill response-relevant issues regarding the communication of complexity and uncertainty—from the presentation of uncertainties around forecast parameters to the deployment of online oil spill response simulation tools. Recommended practices from this article include (a) to continue to develop and evaluate interactive Web-based oil spill response simulations to help users explore tradeoffs in response decisions, (b) to take how people simplify information into account in designing communications processes and products (and evaluate), (c) to provide numbers along with verbal probability descriptions, and (d) if using graphics, to communicate probability or uncertainty, using simple graphics and testing them, as effects may not be predictable and some kinds of graphics are easier to understand than others, depending on context, numeracy, and graphicacy. [ABSTRACT FROM PUBLISHER]

This study applies a mental models survey approach to assess public thinking about oil spills and oil spill response. Based on prior interdisciplinary oil spill response research, the study first applies qualitative interview results and a response risk decision model to the design of a survey instrument. The decision model considers controlled burning, public health, and seafood safety. Surveying U.S. coastal residents (36,978 pairs of responses) through Google Insights identifies beliefs and gaps in understanding as well as related values and preferences about oil spills, and oil spill responses. A majority of respondents are concerned about economic impacts of major oil spills, and tend to see ocean ecosystems as fragile. They tend to see information about chemical dispersants as more important than ecological baseline information, and dispersants as toxic, persistent, and less effective than other response options. Although respondents regard laboratory studies as predictive of the effects of oil and of controlled burning, they are less confident that scientists agree on the toxicity and effectiveness of dispersants. The results illustrate opportunities to reframe discussions of oil spill response in terms of tradeoffs between response options, and new possibilities for assessing public opinions and beliefs during events. [ABSTRACT FROM PUBLISHER]

The article discusses the impact of social media usage such as Twitter during the March and April 2009 apparent flood danger to the Red River Valley, which separates the North Dakota and Minnesota in a region that extends across the U.S.-Canadian border into the province of Manitoba. It also assesses the credibility of public journalism and user-generated media. Further, it mentions that an analysis of tens of thousands of tweets sent during the flood threat period indicates mechanisms for information production, distribution and organization in use in the otherwise unmanageable information space of microblogging.

The article discusses the highlights of the International Association for the Advancement of Artificial Intelligence (AAAI) Conference on Web and Social Media (ICWSM-18) in Stanford, California on June 25-28, 2018. Topics discussed include topical interests in emojis and chatbots, methodological and ethical challenges for researchers and to societal challenges around cyberbullying and online misinformation.

An unusual career trajectory led to the intersection of computer science and social science—and a new perspective on gender diversity in computing. This is one of five articles providing personal perspectives on gender diversity in computing. The Web extra at http://youtu.be/S8OFA0J7iR4 is a video interview in which Kate Starbird, an assistant professor in the Department of Human Centered Design & Engineering at the University of Washington, discusses the importance of gender diversity in computing with Alfred C. (Alf) Weaver, the director of the University of Virginia's Applied Research Institute. [ABSTRACT FROM PUBLISHER]

The article about the misinformation about Covid-19 pandemic, climate change and politics in the U.S. It mentions that ways to address a foreign disinformation campaign, along with discusses that legal difference between speech and action. It discusses that removal barriers to accountability for social media platforms that fail to address misinformation.

The COVID-19 pandemic provides a unique opportunity to study science communication and, in particular, the transmission of consensus. In this study, we show how "science communicators," writ large to include both mainstream science journalists and practiced conspiracy theorists, transform scientific evidence into two dueling consensuses using the effectiveness of masks as a case study. We do this by compiling one of the largest, hand-coded citation datasets of cross-medium science communication, derived from 5 million Twitter posts of people discussing masks. We find that science communicators selectively uplift certain published works while denigrating others to create bodies of evidence that support and oppose masks, respectively. Anti-mask communicators in particular often use selective and deceptive quotation of scientific work and criticize opposing science more than pro-mask communicators. Our findings have implications for scientists, science communicators, and scientific publishers, whose systems of sharing (and correcting) knowledge are highly vulnerable to what we term adversarial science communication.

The 2020 United States (US) presidential election was - and has continued to be - the focus of pervasive and persistent mis- and disinformation spreading through our media ecosystems, including social media. This event has driven the collection and analysis of large, directed social network datasets, but such datasets can resist intuitive understanding. In such large datasets, the overwhelming number of nodes and edges present in typical representations create visual artifacts, such as densely overlapping edges and tightly-packed formations of low-degree nodes, which obscure many features of more practical interest. We apply a method, coengagement transformations, to convert such networks of social data into tractable images. Intuitively, this approach allows for parameterized network visualizations that make shared audiences of engaged viewers salient to viewers. Using the interpretative capabilities of this method, we perform an extensive case study of the 2020 United States presidential election on Twitter, contributing an empirical analysis of coengagement. By creating and contrasting different networks at different parameter sets, we define and characterize several structures in this discourse network, including bridging accounts, satellite audiences, and followback communities. We discuss the importance and implications of these empirical network features in this context. In addition, we release open-source code for creating coengagement networks from Twitter and other structured interaction data.