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Between the radical, creative capacity of our imagination and the social imaginary we are immersed in is an intermediate space philosophers have termed the imaginal, populated by images or (re)presentations that are presences in themselves. Offering a new, systematic understanding of the imaginal and its nexus with the political, Chiara Bottici brings fresh perspective to the formation of political and power relationships and the paradox of a world rich in imagery yet seemingly devoid of imagination. Bottici begins by defining the difference between the imaginal and the imaginary, locating the imaginal's root meaning in the image and its ability to both characterize a public and establish a set of activities within that public. She identifies the imaginal's critical role in powering representative democracies and its amplification through globalization. She then addresses the troublesome increase in images now mediating politics and the transformation of politics into empty spectacle. The spectacularization of politics has led to its virtualization, Bottici observes, transforming images into processes with an uncertain relationship to reality, and, while new media has democratized the image in a global society of the spectacle, the cloned image no longer mediates politics but does the act for us. Bottici concludes with politics' current search for legitimacy through an invented ideal of tradition, a turn to religion, and the incorporation of human rights language.

Digital spaces, and in particular social networking sites, are becoming increasingly present and influential in the functioning of our democracies. In this paper, we propose an integrated methodology for the data collection, the reconstruction, the analysis and the visualization of the development of a country's political landscape from Twitter data. The proposed method relies solely on the interactions between Twitter accounts and is independent of the characteristics of the shared contents such as the language of the tweets. We validate our methodology on a case study on the 2017 French presidential election (60 million Twitter exchanges between more than 2.4 million users) via two independent methods: the comparison between our automated political categorization and a human categorization based on the evaluation of a sample of 5000 profiles descriptions; the correspondence between the reconfigurations detected in the reconstructed political landscape and key political events reported in the media. This latter validation demonstrated the ability of our approach to accurately reflect the reconfigurations at play in the off-line political scene. We built on this reconstruction to give insights into the opinion dynamics and the reconfigurations of political communities at play during a presidential election. First, we propose a quantitative description and analysis of the political engagement of members of political communities. Second, we analyze the impact of political communities on information diffusion and in particular on their role in the fake news phenomena. We measure a differential echo chamber effect on the different types of political news (fake news, debunks, standard news) caused by the community structure and emphasize the importance of addressing the meso-structures of political networks in understanding the fake news phenomena. Giving access to an intermediate level, between sociological surveys in the field and large statistical studies (such as those conducted by national or international organizations) we demonstrate that social networks data make it possible to qualify and quantify the activity of political communities in a multi-polar political environment; as well as their temporal evolution and reconfiguration, their structure, their alliance strategies and their semantic particularities during a presidential campaign through the analysis of their digital traces. We conclude this paper with a comment on the political and ethical implications of the use of social networks data in politics. We stress the importance of developing social macroscopes that will enable citizens to better understand how they collectively make society and propose as example the \"Politoscope\", a macroscope that delivers some of our results in an interactive way.

How do attitudes toward vaccination change over the course of a public health crisis? We report results from a longitudinal survey of United States residents during six months (March 16 –August 16, 2020) of the COVID-19 pandemic. Contrary to past research suggesting that the increased salience of a disease threat should improve attitudes toward vaccines, we observed a decrease in intentions of getting a COVID-19 vaccine when one becomes available. We further found a decline in general vaccine attitudes and intentions of getting the influenza vaccine. Analyses of heterogeneity indicated that this decline is driven by participants who identify as Republicans, who showed a negative trend in vaccine attitudes and intentions, whereas Democrats remained largely stable. Consistent with research on risk perception and behavior, those with less favorable attitudes toward a COVID-19 vaccination also perceived the virus to be less threatening. We provide suggestive evidence that differential exposure to media channels and social networks could explain the observed asymmetric polarization between self-identified Democrats and Republicans.

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.

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.

This study developed, validated, and evaluated a framework of factors influencing dietary behaviours in urban African food environments, to inform research prioritisation and intervention development in Africa. A multi-component methodology, drawing on concept mapping, was employed to construct a framework of factors influencing dietary behaviours in urban Africa. The framework adapted a widely used socio-ecological model (developed in a high-income country context) and was developed using a mixed-methods research approach that comprised: i. Evidence synthesis consisting of a systematic review of 39 papers covering 14 African countries; ii. Qualitative interview data collected for adolescents and adults (n = 144) using photovoice in urban Ghana and Kenya; and iii. Consultation with interdisciplinary African experts (n = 71) from 27 countries, who contributed to at least one step of the framework (creation, validation/evaluation, finalisation). The final framework included 103 factors influencing dietary behaviours. Experts identified the factors influencing dietary behaviours across all the four levels of the food environment i.e. the individual, social, physical and macro levels. Nearly half (n = 48) were individual-level factors and just under a quarter (n = 26) were at the macro environmental level. Fewer factors associated with social (n = 15) and physical (14) environments were identified. At the macro level, the factors ranked as most important were food prices, cultural beliefs and seasonality. Factors ranked as important at the social level were household composition, family food habits and dietary practices. The type of food available in the neighbourhood and convenience were seen as important at the physical level, while individual food habits, food preferences and socioeconomic status were ranked highly at the individual level. About half of the factors (n = 54) overlap with those reported in an existing socio-ecological food environment framework developed in a high-income country context. A further 49 factors were identified that were not reported in the selected high-income country framework, underlining the importance of contextualisation. Our conceptual framework offers a useful tool for research to understand dietary transitions in urban African adolescents and adults, as well as identification of factors to intervene when promoting healthy nutritious diets to prevent multiple forms of malnutrition.

The main objective of this study is to describe the process of collecting data extracted from Twitter (X) during the Brazilian presidential elections in 2022, encompassing the post-election period and the event of the attack on the buildings of the executive, legislative, and judiciary branches in January 2023. The work of collecting data took one year. Additionally, the study provides an overview of the general characteristics of the dataset created from 282 million tweets, named “The Interfaces Twitter Elections Dataset” (ITED-Br), the third most extensive dataset of tweets with political purposes. The process of collecting and creating the database for this study went through three major stages, subdivided into several processes: (1) A preliminary analysis of the platform and its operation; (2) Contextual analysis, creation of the conceptual model, and definition of Keywords and (3) Implementation of the Data Collection Strategy. Python algorithms were developed to model each primary collection type. The “token farm” algorithm, was employed to iterate over available API keys. While Twitter is generally a “public” access platform and fits into big data standards, extracting valuable information is not trivial due to the volume, speed, and heterogeneity of data. This study concludes that acquiring informational value requires expertise not only in sociopolitical areas but also in computational and informational studies, highlighting the interdisciplinary nature of such research.

Emerging evidence demonstrates that female-authored publications are not well represented in course readings in some fields, resulting in a syllabi gender gap. Lack of representation may decrease student awareness of opportunities in professional fields and disadvantage the career success of female academics. We contribute to the evidence on the syllabi gender gap by: 1) quantifying the extent to which female authors are represented in assigned course readings; 2) examining representation of female authors by gender of instructor and discipline; and 3) comparing female representation in syllabi with the workforce and with representation as authors of peer-reviewed journal articles. From a list of courses offered in 2018-2019 at Washington University in St. Louis, we selected a stratified random sample of course syllabi from four disciplines (humanities; social science; science, technology, engineering, and mathematics; and other). We coded the gender of course instructors and course reading authors using the genderize application programming interface. We examined representation of female authors at the reading, course, and discipline level using descriptive statistics and data visualization. The final sample included 2435 readings from 129 unique courses. The mean percentage of female authors per reading was 34.1%; 822 (33.8%) of readings were female-led (i.e., a female first or sole author). Female authorship varied by discipline, with the highest percentage of female-led readings in social science (40%). Female instructors assigned a higher percentage of readings with female first authors and readings with higher percentages of females on authorship teams. The representation of female authors on syllabi was lower than representation of females as authors in the peer-reviewed literature or in workforce. Adding to evidence of the syllabi gender gap, we found that female authors were underrepresented as sole and first authors and as members of authorship teams. Since assigned readings promote academic scholarship and influence workforce diversity, we recommend several strategies to diversify the syllabi through increasing awareness of the gap and improving access to female-authored publications.

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.