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Exposure to media content is an important component of opinion formation around climate change. Online social media such as Twitter, the focus of this study, provide an avenue to study public engagement and digital media dissemination related to climate change. Sharing a link to an online article is an indicator of media engagement. Aggregated link-sharing forms a network structure which maps collective media engagement by the user population. Here we construct bipartite networks linking Twitter users to the web pages they shared, using a dataset of approximately 5.3 million English-language tweets by almost 2 million users during an eventful seven-week period centred on the announcement of the US withdrawal from the Paris Agreement on climate change. Community detection indicates that the observed information-sharing network can be partitioned into two weakly connected components, representing subsets of articles shared by a group of users. We characterise these partitions through analysis of web domains and text content from shared articles, finding them to be broadly described as a left-wing/environmentalist group and a right-wing/climate sceptic group. Correlation analysis shows a striking positive association between left/right political ideology and environmentalist/sceptic climate ideology respectively. Looking at information-sharing over time, there is considerable turnover in the engaged user population and the articles that are shared, but the web domain sources and polarised network structure are relatively persistent. This study provides evidence that online sharing of news media content related to climate change is both polarised and politicised, with implications for opinion dynamics and public debate around this important societal challenge.

The success of a government's COVID-19 control strategy relies on public trust and broad acceptance of response measures. We investigated public perceptions of the UK government's COVID-19 response, focusing on the relationship between trust and perceived transparency, during the first wave (April 2020) of the COVID-19 pandemic in the United Kingdom. Anonymous survey data were collected (2020-04-06 to 2020-04-22) from 9,322 respondents, aged 20+ using an online questionnaire shared primarily through Facebook. We took an embedded-mixed-methods approach to data analysis. Missing data were imputed via multiple imputation. Binomial & multinomial logistic regression were used to detect associations between demographic characteristics and perceptions or opinions of the UK government's response to COVID-19. Structural topic modelling (STM), qualitative thematic coding of sub-sets of responses were then used to perform a thematic analysis of topics that were of interest to key demographic groups. Most respondents (95.1%) supported government enforcement of behaviour change. While 52.1% of respondents thought the government was making good decisions, differences were apparent across demographic groups, for example respondents from Scotland had lower odds of responding positively than respondents in London. Higher educational levels saw decreasing odds of having a positive opinion of the government response and decreasing household income associated with decreasing positive opinion. Of respondents who thought the government was not making good decisions 60% believed the economy was being prioritised over people and their health. Positive views on government decision-making were associated with positive views on government transparency about the COVID-19 response. Qualitative analysis about perceptions of government transparency highlighted five key themes: (1) the justification of opacity due to the condition of crisis, (2) generalised mistrust of politics, (3) concerns about the role of scientific evidence, (4) quality of government communication and (5) questions about political decision-making processes. Our study suggests that trust is not homogenous across communities, and that generalised mistrust, concerns about the transparent use and communication of evidence and insights into decision-making processes can affect perceptions of the government's pandemic response. We recommend targeted community engagement, tailored to the experiences of different groups and a new focus on accountability and openness around how decisions are made in the response to the UK COVID-19 pandemic.

How did data get so big? Through political, social and economic interests, shows Sabina Leonelli, in the fourth essay on how the past 150 years have shaped the science system, marking Nature ’s anniversary. How did data get so big? Through political, social and economic interests, shows Sabina Leonelli.

As the current recession ends, many workers will not be returning to the jobs they once held--those jobs are gone. InThe New Division of Labor, Frank Levy and Richard Murnane show how computers are changing the employment landscape and how the right kinds of education can ease the transition to the new job market. The book tells stories of people at work--a high-end financial advisor, a customer service representative, a pair of successful chefs, a cardiologist, an automotive mechanic, the author Victor Hugo, floor traders in a London financial exchange. The authors merge these stories with insights from cognitive science, computer science, and economics to show how computers are enhancing productivity in many jobs even as they eliminate other jobs--both directly and by sending work offshore. At greatest risk are jobs that can be expressed in programmable rules--blue collar, clerical, and similar work that requires moderate skills and used to pay middle-class wages. The loss of these jobs leaves a growing division between those who can and cannot earn a good living in the computerized economy. Left unchecked, the division threatens the nation's democratic institutions. The nation's challenge is to recognize this division and to prepare the population for the high-wage/high-skilled jobs that are rapidly growing in number--jobs involving extensive problem solving and interpersonal communication. Using detailed examples--a second grade classroom, an IBM managerial training program, Cisco Networking Academies--the authors describe how these skills can be taught and how our adjustment to the computerized workplace can begin in earnest.

Online messaging app Telegram has increased in popularity in recent years surpassing Twitter and Snapchat by the number of active monthly users in late 2020. The messenger has also been crucial to protest movements in several countries in 2019-2020, including Belarus, Russia and Hong Kong. Yet, to date only few studies examined online activities on Telegram and none have analyzed the platform with regard to the protest mobilization. In the present study, we address the existing gap by examining Telegram-based activities related to the 2019 protests in Hong Kong. With this paper we aim to provide an example of methodological tools that can be used to study protest mobilization and coordination on Telegram. We also contribute to the research on computational text analysis in Cantonese—one of the low-resource Asian languages,—as well as to the scholarship on Hong Kong protests and research on social media-based protest mobilization in general. For that, we rely on the data collected through Telegram’s API and a combination of network analysis and computational text analysis. We find that the Telegram-based network was cohesive ensuring efficient spread of protest-related information. Content spread through Telegram predominantly concerned discussions of future actions and protest-related on-site information (i.e., police presence in certain areas). We find that the Telegram network was dominated by different actors each month of the observation suggesting the absence of one single leader. Further, traditional protest leaders—those prominent during the 2014 Umbrella Movement,—such as media and civic organisations were less prominent in the network than local communities. Finally, we observe a cooldown in the level of Telegram activity after the enactment of the harsh National Security Law in July 2020. Further investigation is necessary to assess the persistence of this effect in a long-term perspective.

This paper studies the influence of community capitals on well-being through a Community Capital Index (CCI) within coffee-growing families in southern Colombia. Our results show different farm typologies, with different levels of capital endowment translated into well-being that, in our case, were represented in the CCI. Specifically, social and political capitals positively affect coffee-growing families’ decisions in terms of life strategies. The results of this study increase our understanding of welfare enhancement and its relationship with capital endowment according to the type of coffee producer, having implications for the planning of more effective programs towards the improvement of quality of life.

In a rapidly changing world, we rely upon experts to assess the promise and risks of new technology. But how do these experts make sense of a highly uncertain future? In Arguments that Count , Rebecca Slayton offers an important new perspective. Drawing on new historical documents and interviews as well as perspectives in science and technology studies, she provides an original account of how scientists came to terms with the unprecedented threat of nuclear-armed intercontinental ballistic missiles (ICBMs). She compares how two different professional communities -- physicists and computer scientists -- constructed arguments about the risks of missile defense, and how these arguments changed over time. Slayton shows that our understanding of technological risks is shaped by disciplinary repertoires -- the codified knowledge and mathematical rules that experts use to frame new challenges. And, significantly, a new repertoire can bring long-neglected risks into clear view. In the 1950s, scientists recognized that high-speed computers would be needed to cope with the unprecedented speed of ICBMs. But the nation's elite science advisors had no way to analyze the risks of computers so used physics to assess what they could: radar and missile performance. Only decades later, after establishing computing as a science, were advisors able to analyze authoritatively the risks associated with complex software -- most notably, the risk of a catastrophic failure. As we continue to confront new threats, including that of cyber attack, Slayton offers valuable insight into how different kinds of expertise can limit or expand our capacity to address novel technological risks.