Social dimensions impact individual sleep quantity and quality.

While sleep positively impacts well-being, health, and productivity, the effects of societal factors on sleep remain underexplored. Here we analyze the sleep of 30,082 individuals across 11 countries using 52 million activity records from wearable devices. Our data are consistent with past studies of gender and age-associated sleep characteristics. However, our analysis of wearable device data uncovers differences in recorded vs. self-reported bedtime and sleep duration. The dataset allowed us to study how country-specific metrics such as GDP and cultural indices relate to sleep in groups and individuals. Our analysis indicates that diverse sleep metrics can be represented by two dimensions: sleep quantity and quality. We find that 55% of the variation in sleep quality, and 63% in sleep quantity, are explained by societal factors. Within a societal boundary, individual sleep experience was modified by factors like exercise. Increased exercise or daily steps were associated with better sleep quality (for example, faster sleep onset and less time awake in bed), especially in countries like the U.S. and Finland. Understanding how social norms relate to sleep will help create strategies and policies that enhance the positive impacts of sleep on health, such as productivity and well-being.

The role of the big geographic sort in online news circulation among U.S. Reddit users.

Past research has attributed the circulation of online news to two main factors-individual characteristics (e.g., a person's information literacy) and social media effects (e.g., algorithm-mediated information diffusion)-and has overlooked a third one: the critical mass created by the offline self-segregation of Americans into like-minded geographical regions such as states (a phenomenon called 'The Big Sort'). We hypothesized that this latter factor matters for the online spreading of news not least because online interactions, despite having the potential of being global, end up being localized: interaction probability is known to rapidly decay with distance. Upon analysis of more than 8M Reddit comments containing news links spanning four years, from January 2016 to December 2019, we found that Reddit did not work as an 'hype machine' for news (as opposed to what previous work reported for other platforms, circulation was not mainly caused by platform-facilitated network effects). Rather, news circulation in Reddit worked as a supply-and-demand system: news items scaled linearly with the number of users in each state (with a scaling exponent [Formula: see text]  [Formula: see text], and a goodness of fit [Formula: see text]). Furthermore, deviations from such a universal pattern were best explained by state-level personality and cultural factors ([Formula: see text]), rather than socioeconomic conditions ([Formula: see text]) or political characteristics ([Formula: see text]). Higher-than-expected circulation of any type of news was found in states characterised by residents who tend to be less diligent in terms of their personality (low in conscientiousness) and by loose cultures understating the importance of adherence to norms (low in cultural tightness). Interestingly, the combination of those factors with low levels of education was then associated with the circulation of a particular type of news, that is, misinformation. These results suggest that online interactions are geographically bounded and, as such, news circulation cannot be studied purely as an Internet phenomenon but should be grounded into a user's offline cultural environment, which has become increasingly segregated over the decades, and is admittedly hard to change.

Quantifying the impact of positive stress on companies from online employee reviews.

Workplace stress is often considered to be negative, yet lab studies on individuals suggest that not all stress is bad. There are two types of stress: distress refers to harmful stimuli, while eustress refers to healthy, euphoric stimuli that create a sense of fulfillment and achievement. Telling the two types of stress apart is challenging, let alone quantifying their impact across corporations. By leveraging a dataset of 440 K reviews about S &P 500 companies published during twelve successive years, we developed a deep learning framework to extract stress mentions from these reviews. We proposed a new methodology that places each company on a stress-by-rating quadrant (based on its overall stress score and overall rating on the site), and accordingly scores the company to be, on average, either a low stress, passive, negative stress, or positive stress company. We found that (former) employees of positive stress companies tended to describe high-growth and collaborative workplaces in their reviews, and that such companies' stock evaluations grew, on average, 5.1 times in 10 years (2009-2019) as opposed to the companies of the other three stress types that grew, on average, 3.7 times in the same time period. We also found that the four stress scores aggregated every year-from 2008 to 2020 -closely followed the unemployment rate in the U.S.: a year of positive stress (2008) was rapidly followed by several years of negative stress (2009-2015), which peaked during the Great Recession (2009-2011). These results suggest that automated analyses of the language used by employees on corporate social-networking tools offer yet another way of tracking workplace stress, allowing quantification of its impact on corporations.

Multidimensional tie strength and economic development.

The strength of social relations has been shown to affect an individual's access to opportunities. To date, however, the correspondence between tie strength and population's economic prospects has not been quantified, largely because of the inability to operationalise strength based on Granovetter's classic theory. Our work departed from the premise that tie strength is a unidimensional construct (typically operationalized with frequency or volume of contact), and used instead a validated model of ten fundamental dimensions of social relationships grounded in the literature of social psychology. We built state-of-the-art NLP tools to infer the presence of these dimensions from textual communication, and analyzed a large conversation network of 630K geo-referenced Reddit users across the entire US connected by 12.8M social ties created over the span of 7 years. We found that unidimensional tie strength is only weakly correlated with economic opportunities ([Formula: see text]), while multidimensional constructs are highly correlated ([Formula: see text]). In particular, economic opportunities are associated to the combination of: (i) knowledge ties, which bridge geographically distant groups, facilitating the knowledge dissemination across communities; and (ii) social support ties, which knit geographically close communities together, and represent dependable sources of social and emotional support. These results point to the importance of developing high-quality measures of tie strength in network theory.

Visualizing Internal Sustainability Efforts in Big Companies.

Internal sustainability efforts (ISE) refer to a wide range of internal corporate policies focused on employees. They promote, for example, work-life balance, gender equality, and a harassment-free working environment. At times, however, companies fail to keep their promises by not publicizing truthful reports on these practices, or by overlooking employees voices on how these practices are implemented. To partly fix that, we developed a deep-learning (DL) framework that scored fourth fifths of the S&P 500 companies in terms of six ISEs, and a web-based system that engages users in a learning and reflection process about these ISEs. We evaluated the system in two crowdsourced studies with 421 participants, and compared our treemap visualization with a baseline textual representation. We found that our interactive treemap increased by up to 7% our participants opinion change about ISEs, demonstrating its potential in machine-learning (ML) driven visualizations.

Epidemic dreams: dreaming about health during the COVID-19 pandemic.

The continuity hypothesis of dreams suggests that the content of dreams is continuous with the dreamer's waking experiences. Given the unprecedented nature of the experiences during COVID-19, we studied the continuity hypothesis in the context of the pandemic. We implemented a deep-learning algorithm that can extract mentions of medical conditions from text and applied it to two datasets collected during the pandemic: 2888 dream reports (dreaming life experiences), and 57 milion tweets (waking life experiences) mentioning the pandemic. The health expressions common to both sets were typical COVID-19 symptoms (e.g. cough, fever and anxiety), suggesting that dreams reflected people's real-world experiences. The health expressions that distinguished the two sets reflected differences in thought processes: expressions in waking life reflected a linear and logical thought process and, as such, described realistic symptoms or related disorders (e.g. nasal pain, SARS, H1N1); those in dreaming life reflected a thought process closer to the visual and emotional spheres and, as such, described either conditions unrelated to the virus (e.g. maggots, deformities, snake bites), or conditions of surreal nature (e.g. teeth falling out, body crumbling into sand). Our results confirm that dream reports represent an understudied yet valuable source of people's health experiences in the real world.

Streetonomics: Quantifying culture using street names.

Quantifying a society's value system is important because it suggests what people deeply care about-it reflects who they actually are and, more importantly, who they will like to be. This cultural quantification has been typically done by studying literary production. However, a society's value system might well be implicitly quantified based on the decisions that people took in the past and that were mediated by what they care about. It turns out that one class of these decisions is visible in ordinary settings: it is visible in street names. We studied the names of 4,932 honorific streets in the cities of Paris, Vienna, London and New York. We chose these four cities because they were important centers of cultural influence for the Western world in the 20th century. We found that street names greatly reflect the extent to which a society is gender biased, which professions are considered elite ones, and the extent to which a city is influenced by the rest of the world. This way of quantifying a society's value system promises to inform new methodologies in Digital Humanities; makes it possible for municipalities to reflect on their past to inform their future; and informs the design of everyday's educational tools that promote historical awareness in a playful way.

The Dreamcatcher: Interactive Storytelling of Dreams.

Sleep scientists have extensively validated the continuity hypothesis, according to which our dreams reflect what happens during our waking life. Yet, only a few attempts have been made to increase the general public's awareness about the benefits of dream analysis in better understanding and improving our daily life. We designed "The Dreamcatcher," an interactive visual tool that explores the link between dreams and waking life through a collection of dream reports. We conducted a user study with 154 participants and found a 25% increase in the number of people believing that dream analysis can improve our daily lives after interacting with our tool. The visualization informed people about the potential of the continuity hypothesis to a surprising extent, to the point that it increased their concerns about sharing their own dream reports, thus opening new questions on how to design privacy-aware tools for dream collection.

Humane Visual AI: Telling the Stories Behind a Medical Condition.

A biological understanding is key for managing medical conditions, yet psychological and social aspects matter too. The main problem is that these two aspects are hard to quantify and inherently difficult to communicate. To quantify psychological aspects, this work mined around half a million Reddit posts in the sub-communities specialised in 14 medical conditions, and it did so with a new deep-learning framework. In so doing, it was able to associate mentions of medical conditions with those of emotions. To then quantify social aspects, this work designed a probabilistic approach that mines open prescription data from the National Health Service in England to compute the prevalence of drug prescriptions, and to relate such a prevalence to census data. To finally visually communicate each medical condition's biological, psychological, and social aspects through storytelling, we designed a narrative-style layered Martini Glass visualization. In a user study involving 52 participants, after interacting with our visualization, a considerable number of them changed their mind on previously held opinions: 10% gave more importance to the psychological aspects of medical conditions, and 27% were more favourable to the use of social media data in healthcare, suggesting the importance of persuasive elements in interactive visualizations.

Cartographic Design of Cultural Maps.

Throughout history, maps have been used as a tool to explore cities. They visualize a city's urban fabric through its streets, buildings, and points of interest. Besides purely navigation purposes, street names also reflect a city's culture through its commemorative practices. Therefore, cultural maps that unveil socio-cultural characteristics encoded in street names could potentially raise citizens' historical awareness. But designing effective cultural maps is challenging, not only due to data scarcity but also due to the lack of effective approaches to engage citizens with data exploration. To address these challenges, we collected a dataset of 5000 streets across the cities of Paris, Vienna, London, and New York, and built their cultural maps grounded on cartographic storytelling techniques. Through data exploration scenarios, we demonstrated how cultural maps engage users and allow them to discover distinct patterns in the ways these cities are gender-biased, celebrate various professions, and embrace foreign cultures.

Our dreams, our selves: automatic analysis of dream reports.

Sleep scientists have shown that dreaming helps people improve their waking lives, and they have done so by developing sophisticated content analysis scales. Dream analysis entails time-consuming manual annotation of text. That is why dream reports have been recently mined with algorithms, and these algorithms focused on identifying emotions. In so doing, researchers have not tackled two main technical challenges though: (i) how to mine aspects of dream reports that research has found important, such as characters and interactions; and (ii) how to do so in a principled way grounded in the literature. To tackle these challenges, we designed a tool that automatically scores dream reports by operationalizing the widely used dream analysis scale by Hall and Van de Castle. We validated the tool's effectiveness on hand-annotated dream reports (the average error is 0.24), scored 24 000 reports-far more than any previous study-and tested what sleep scientists call the 'continuity hypothesis' at this unprecedented scale: we found supporting evidence that dreams are a continuation of what happens in everyday life. Our results suggest that it is possible to quantify important aspects of dreams, making it possible to build technologies that bridge the current gap between real life and dreaming.

FaceLift: a transparent deep learning framework to beautify urban scenes.

In the area of computer vision, deep learning techniques have recently been used to predict whether urban scenes are likely to be considered beautiful: it turns out that these techniques are able to make accurate predictions. Yet they fall short when it comes to generating actionable insights for urban design. To support urban interventions, one needs to go beyond predicting beauty, and tackle the challenge of recreating beauty. Unfortunately, deep learning techniques have not been designed with that challenge in mind. Given their 'black-box nature', these models cannot be directly used to explain why a particular urban scene is deemed to be beautiful. To partly fix that, we propose a deep learning framework (which we name FaceLift) that is able to both beautify existing urban scenes (Google Street Views) and explain which urban elements make those transformed scenes beautiful. To quantitatively evaluate our framework, we cannot resort to any existing metric (as the research problem at hand has never been tackled before) and need to formulate new ones. These new metrics should ideally capture the presence (or absence) of elements that make urban spaces great. Upon a review of the urban planning literature, we identify five main metrics: walkability, green spaces, openness, landmarks and visual complexity. We find that, across all the five metrics, the beautified scenes meet the expectations set by the literature on what great spaces tend to be made of. This result is further confirmed by a 20-participant expert survey in which FaceLift has been found to be effective in promoting citizen participation. All this suggests that, in the future, as our framework's components are further researched and become better and more sophisticated, it is not hard to imagine technologies that will be able to accurately and efficiently support architects and planners in the design of the spaces we intuitively love.

Tesco Grocery 1.0, a large-scale dataset of grocery purchases in London.

We present the Tesco Grocery 1.0 dataset: a record of 420 M food items purchased by 1.6 M fidelity card owners who shopped at the 411 Tesco stores in Greater London over the course of the entire year of 2015, aggregated at the level of census areas to preserve anonymity. For each area, we report the number of transactions and nutritional properties of the typical food item bought including the average caloric intake and the composition of nutrients. The set of global trade international numbers (barcodes) for each food type is also included. To establish data validity we: i) compare food purchase volumes to population from census to assess representativeness, and ii) match nutrient and energy intake to official statistics of food-related illnesses to appraise the extent to which the dataset is ecologically valid. Given its unprecedented scale and geographic granularity, the data can be used to link food purchases to a number of geographically-salient indicators, which enables studies on health outcomes, cultural aspects, and economic factors.

Interdisciplinary Research in Artificial Intelligence: Challenges and Opportunities.

The use of artificial intelligence (AI) in a variety of research fields is speeding up multiple digital revolutions, from shifting paradigms in healthcare, precision medicine and wearable sensing, to public services and education offered to the masses around the world, to future cities made optimally efficient by autonomous driving. When a revolution happens, the consequences are not obvious straight away, and to date, there is no uniformly adapted framework to guide AI research to ensure a sustainable societal transition. To answer this need, here we analyze three key challenges to interdisciplinary AI research, and deliver three broad conclusions: 1) future development of AI should not only impact other scientific domains but should also take inspiration and benefit from other fields of science, 2) AI research must be accompanied by decision explainability, dataset bias transparency as well as development of evaluation methodologies and creation of regulatory agencies to ensure responsibility, and 3) AI education should receive more attention, efforts and innovation from the educational and scientific communities. Our analysis is of interest not only to AI practitioners but also to other researchers and the general public as it offers ways to guide the emerging collaborations and interactions toward the most fruitful outcomes.

Mapping and Visualizing Deep-Learning Urban Beautification.

Information visualization has great potential to make sense of the increasing amount of data generated by complex machine-learning algorithms. We design a set of visualizations for a new deep-learning algorithm called FaceLift (goodcitylife.org/facelift). This algorithm is able to generate a beautified version of a given urban image (such as from Google Street View), and our visualizations compare pairs of original and beautified images. With those visualizations, we aim at helping practitioners understand what happened during the algorithmic beautification without requiring them to be machine-learning experts. We evaluate the effectiveness of our visualizations to do just that with a survey among practitioners. From the survey results, we derive general design guidelines on how information visualization makes complex machine-learning algorithms more understandable to a general audience.

Diversity of indoor activities and economic development of neighborhoods.

Over the last few decades, public life has taken center stage in urban studies, but that is about to change. At times, indoor activities have been shown to matter more than what is publicly visible (they have been found to be more predictive of future crimes, for example). Until recently, however, data has not been available to study indoor activities at city scale. To that end, we propose a new methodology that relies on tagging information of geo-referenced pictures and unfolds in three main steps. First, we collected and classified a comprehensive set of activity-related words, creating the first dictionary of urban activities. Second, for both London and New York City, we collected geo-referenced Flickr tags and matched them with the words in the dictionary. This step produced both a systematic classification (our activity-related words were best classified in eleven categories) and two city-wide indoor activity maps which, when compared to open data of public amenities and sensory maps of smell and sound matched theoretical expectations. Third, we studied, for the first time, activities happening indoor in relation to neighborhood socio-economic conditions. We found the very same result for both London and New York City. In deprived areas, people focused on any of the activity types (leading to specialization), and it did not matter on which one they did so. By contrast, in well-to-do areas, people engaged not in one type of activity but in a variety of them (leading to diversification).

World wide spatial capital.

In its most basic form, the spatial capital of a neighborhood entails that most aspects of daily life are located close at hand. Urban planning researchers have widely recognized its importance, not least because it can be transformed in other forms of capital such as economical capital (e.g., house prices, retail sales) and social capital (e.g., neighborhood cohesion). Researchers have already studied spatial capital from official city data. Their work led to important planning decisions, yet it also relied on data that is costly to create and update, and produced metrics that are difficult to compare across cities. By contrast, we propose to measure spatial capital in cheap and standardized ways around the world. Hence the name of our project "World Wide Spatial Capital". Our measures are cheap as they rely on the most basic information about a city that is currently available on the Web (i.e., which amenities are available and where). They are also standardized because they can be applied in any city in the five continents (as opposed to previous metrics that were mainly applied in USA and UK). We show that, upon these metrics, one could produce insights at the core of the urban planning discipline: which areas would benefit the most from urban interventions; how to inform planning depending on whether a city's activity is mono- or poly-centric; how different cities fare against each other; and how spatial capital correlates with other urban characteristics such as mobility patterns and road network structure.

Chatty maps: constructing sound maps of urban areas from social media data.

Urban sound has a huge influence over how we perceive places. Yet, city planning is concerned mainly with noise, simply because annoying sounds come to the attention of city officials in the form of complaints, whereas general urban sounds do not come to the attention as they cannot be easily captured at city scale. To capture both unpleasant and pleasant sounds, we applied a new methodology that relies on tagging information of georeferenced pictures to the cities of London and Barcelona. To begin with, we compiled the first urban sound dictionary and compared it with the one produced by collating insights from the literature: ours was experimentally more valid (if correlated with official noise pollution levels) and offered a wider geographical coverage. From picture tags, we then studied the relationship between soundscapes and emotions. We learned that streets with music sounds were associated with strong emotions of joy or sadness, whereas those with human sounds were associated with joy or surprise. Finally, we studied the relationship between soundscapes and people's perceptions and, in so doing, we were able to map which areas are chaotic, monotonous, calm and exciting. Those insights promise to inform the creation of restorative experiences in our increasingly urbanized world.