Weather impacts expressed sentiment

Clouded judgments? The role of virtual weather in word valence evaluations

Exploring the dynamic interface of environmental psychology and psycholinguistics, this investigation delves into how simulated weather conditions - sunny versus rainy - affect emotional perceptions of linguistic stimuli within a Virtual Reality (VR) framework. Participants assessed words' emotional valence being exposed to these distinct environmental simulations. Contrary to expectations, we found that while rainy conditions modestly prolonged response times, they did not significantly alter the emotional valence attributed to words. Our study shows that weather can affect emotional cognition, but intrinsic emotional word properties are resilient to environmental influences. This highlights the complex interplay between environmental factors and linguistic processing and reaffirms the importance of environmental contexts in cognitive and emotional evaluations, especially in the face of climate change. By integrating VR technology with environmental psychology and linguistics, our research offers novel insights into the subtle yet significant ways in which virtual and real-world environments converge to shape human emotional and cognitive responses.

Effects of extreme temperatures on public sentiment in 49 Chinese cities

The rising sentiment challenges of the metropolitan residents may be attributed to the extreme temperatures. However, nationwide real-time empirical studies that examine this claim are rare. In this research, we construct a daily extreme temperature index and sentiment metric using geotagged posts on one of China's largest social media sites, Weibo, to verify this hypothesis. We find that extreme temperatures causally decrease individuals' sentiment, and extremely low temperature may decrease more than extremely high temperature. Heterogeneity analyses reveal that individuals living in high levels of PM2.5, existing new COVID-19 diagnoses and low-disposable income cities on workdays are more vulnerable to the impact of extreme temperatures on sentiment. More importantly, the results also demonstrate that the adverse effects of extremely low temperatures on sentiment are more minor for people living in northern cities with breezes. Finally, we estimate that with a one-standard increase of extremely high (low) temperature, the sentiment decreases by approximately 0.161 (0.272) units. Employing social media to monitor public sentiment can assist policymakers in developing data-driven and evidence-based policies to alleviate the adverse impacts of extreme temperatures.

High temperatures and urban entrepreneurship levels: Evidence from China

This paper performed as a frontier try to investigate the effect of high temperatures on entrepreneurship, assessed from an urban perspective. This paper estimated the impact of high temperatures on urban entrepreneurship levels using data from 281 prefecture cities in China, during the period 2000-2017. This paper found that a single day with a temperature of above 30 °C led to a decrease of 0.47 % in urban entrepreneurship levels, compared with a single day recording comfortable temperatures. Following a series of robustness tests, the results were found to be significant. Next, this paper conducted a series of heterogeneity analyses and discovered that cities with advanced industrial structures, larger sizes and more essential hierarchies were less affected by high temperatures. Finally, this paper further analyzed the potential influence mechanisms of high temperatures on entrepreneurship. This paper found that high temperature affects urban entrepreneurship levels by worsening the entrepreneurial environment, especially by reducing human capital, hindering innovation, decreasing the financial support available to enterprises, and hindering economic development. The results of our study have thus enriched the literature on entrepreneurship by exploring the impact of climate change on entrepreneurship.

Patterns in negative emotions, sleep disorders, and temperature: Evidence from microblog big data

Existing studies have shown that temperature is related to mental illness and sleep disorders. However, few studies have explored the relationship between temperature and microblog negative emotions (MNE) and the predictive effect of MNE on sleep disorders. The present study elucidating the temperature patterns of MNE and sleep disorders, examines the predictive capability of these adverse emotions in precipitating sleep disorders, and operating within the schema of "climate-psychology-behavior". A negative binomial regression model (NBR) was formulated, amalgamating Temperature data, negative affective information procured from microblog, and sleep disorder records. Temperature and Apparent Air Temperature (AAT) were found to have a non-linear association with microblog negative emotions and sleep disorders, exhibiting a modest effect within a specified range, while extreme temperatures (both high and low) demonstrated substantial effects. In the constructed model, gender serves as a moderating factor, with females being more susceptible to temperature and AAT effects on MNE and sleep disorders than their male counterparts. Interestingly, AAT surfaced as a superior predictor compared to actual temperature. MNE were effective predictors of sleep disorders. Employing social media-centric models, as showcased in this study, augments the identification and prevention strategies targeting disease symptoms or pathologies within mental and public health domains.

Joyful by nature: approaches to investigate the evolution and function of joy in non-human animals

The nature and evolution of positive emotion is a major question remaining unanswered in science and philosophy. The study of feelings and emotions in humans and animals is dominated by discussion of affective states that have negative valence. Given the clinical and social significance of negative affect, such as depression, it is unsurprising that these emotions have received more attention from scientists. Compared to negative emotions, such as fear that leads to fleeing or avoidance, positive emotions are less likely to result in specific, identifiable, behaviours being expressed by an animal. This makes it particularly challenging to quantify and study positive affect. However, bursts of intense positive emotion (joy) are more likely to be accompanied by externally visible markers, like vocalisations or movement patterns, which make it more amenable to scientific study and more resilient to concerns about anthropomorphism. We define joy as intense, brief, and event-driven (i.e. a response to something), which permits investigation into how animals react to a variety of situations that would provoke joy in humans. This means that behavioural correlates of joy are measurable, either through newly discovered 'laughter' vocalisations, increases in play behaviour, or reactions to cognitive bias tests that can be used across species. There are a range of potential situations that cause joy in humans that have not been studied in other animals, such as whether animals feel joy on sunny days, when they accomplish a difficult feat, or when they are reunited with a familiar companion after a prolonged absence. Observations of species-specific calls and play behaviour can be combined with biometric markers and reactions to ambiguous stimuli in order to enable comparisons of affect between phylogenetically distant taxonomic groups. Identifying positive affect is also important for animal welfare because knowledge of positive emotional states would allow us to monitor animal well-being better. Additionally, measuring if phylogenetically and ecologically distant animals play more, laugh more, or act more optimistically after certain kinds of experiences will also provide insight into the mechanisms underlying the evolution of joy and other positive emotions, and potentially even into the evolution of consciousness.

Prediction and analysis of atmospheric visibility in five terrain types with artificial intelligence

Scattering visiometers are widely used to measure atmospheric visibility; however, visibility is difficult to measure accurately because the extinction coefficient decays exponentially with visual range according to the Koschmid's law. Moreover, models for predicting visibility are lacking due to the lack of accurate visibility observations to verify. This study formulated an artificial intelligence method for measuring atmospheric visibility in five topographical regions: hills, basins, plains, alluvial plains, and rift valleys. Four air pollution factors and five meteorological factors were selected as independent variables for predicting visibility by using three artificial intelligence models, namely a support vector machine (SVM) model, a multilayer perceptron (MLP) model, and an extreme gradient boosting (XGBoost) model. The GridSearchCV function was used to automatically tune model hyperparameters to determine the optimal parameter values of the three models for the five target areas. The predictions of the aforementioned three models underwent considerable considerably scale shrinking relative to observed values. The inappropriately low predicted visibility values might have been caused by the use of inaccurate observations for training. To solve this problem, formulas of scale ratio and downshift were used to adjust the predicted values. Statistical measurements of model performance measures by five quantitative methods (e.g., correlation coefficient, mean absolute error) showed that adjusted predictions were in strong agreement with the observation data for the five target areas. Therefore, the adjusted prediction has high reliability. Because of obvious differences in the topography, weather, and air quality of the five target areas, different models provided optimal predictions for different areas. In densely populated western Taiwan, the MLP model is most suitable for predicting visibility on hills whereas the XGBoost model is most suitable for predicting visibility on basins and plains. In eastern Taiwan, the SVM model is most suitable for predicting visibility on alluvial plains and rift valleys. Thus, the optimal prediction model should be identified according to the conditions in each area. These results can inform decision-making processes or improve visibility predicting in specific areas.

Young people's mental and social distress in times of international crisis: evidence from helpline calls, 2019-2022

We document mental and social distress of children, adolescents and adults, using data on 3 million calls to German helplines between January 2019 and May 2022. High-frequency data from crisis helpline logs offer rich information on the evolution of "revealed distress" among the most vulnerable, unaffected by researchers' study design and framing. Distress of adults, measured by the volume of calls, rose significantly after both the outbreak of the pandemic and the Russian invasion of Ukraine. In contrast, the overall revealed distress of children and adolescents did not increase during those crises. The nature of young people's concerns, however, changed more strongly than for adults after the COVID-19 outbreak. Consistent with the effects of social distancing, call topics of young people shifted from problems with school and peers to problems with family and mental health. We find the share of severe mental health problems among young people to have increased with a delay, in the second and third year of the pandemic.

Negative expressions are shared more on Twitter for public figures than for ordinary users

Social media users tend to produce content that contains more positive than negative emotional language. However, negative emotional language is more likely to be shared. To understand why, research has thus far focused on psychological processes associated with tweets' content. In the current study, we investigate if the content producer influences the extent to which their negative content is shared. More specifically, we focus on a group of users that are central to the diffusion of content on social media-public figures. We found that an increase in negativity was associated with a stronger increase in sharing for public figures compared to ordinary users. This effect was explained by two user characteristics, the number of followers and thus the strength of ties and the proportion of political tweets. The results shed light on whose negativity is most viral, allowing future research to develop interventions aimed at mitigating overexposure to negative content.

Climate Change and Substance-Use Behaviors: A Risk-Pathways Framework

Climate change is undermining the mental and physical health of global populations, but the question of how it is affecting substance-use behaviors has not been systematically examined. In this narrative synthesis, we find that climate change could increase harmful substance use worldwide through at least five pathways: psychosocial stress arising from the destabilization of social, environmental, economic, and geopolitical support systems; increased rates of mental disorders; increased physical-health burden; incremental harmful changes to established behavior patterns; and worry about the dangers of unchecked climate change. These pathways could operate independently, additively, interactively, and cumulatively to increase substance-use vulnerability. Young people face disproportionate risks because of their high vulnerability to mental-health problems and substance-use disorders and greater number of life years ahead in which to be exposed to current and worsening climate change. We suggest that systems thinking and developmental life-course approaches provide practical frameworks for conceptualizing this relationship. Further conceptual, methodological, and empirical work is urgently needed to evaluate the nature and scope of this burden so that effective adaptive and preventive action can be taken.

Heatwave and mental health

Physical health has been associated with ambient temperature and heatwave. With the frequent occurrence of heatwave, the adaptive effects and mechanisms on mental health remain uncertain. On the basis of the China Health and Nutrition Survey, we estimated the relationship between heatwaves and self-assessed mental health scores in the Chinese population aged 50 and above. This study has identified that with each additional heatwave event, mental health scores decreased by an average of 0.027 points, which is equivalent to 0.3% of the average level. Heat is more likely to affect groups with low education, no medical insurance, and living in rural areas. In mechanistic exploration, we found that stress emotion is a fully mediating effect. Heat led to reduced health activities and more frequent drinking, which may lead to lower psychological well-being. Moreover, good dietary preference is a regulator that can help mitigate the adverse effects of heat on mental health. This study corroborates the impact of heat on spiritual welfare, and demonstrates the mechanisms and channels of impact, which can help reduce global economic losses due to mental health problems.

The long-term impacts of air quality on fine-grained online emotional responses to haze pollution in 160 Chinese cities

Air pollution poses a great threat to public health and social stability by influencing multiple emotions. In particular, the air quality in developing countries is deteriorating along with rapid industrialization and urbanization, and multiple emotions may change along with regulation updates and air quality trending. Monitoring changes in public emotion is crucial for environmental governance. However, limited evidence exists for long-term effects of air quality on fine-grained emotions. Traditional surveys have the drawbacks of spatial limitations and high costs of time and money. Here, we use deep learning models to identify multiple emotions of over 10 million haze-related tweets and evaluate the effect of air quality on emotional predispositions for 160 cities from 2014 to 2019 in China. We find that sadness and joy are persistently associated with air quality, while anger and disgust are not. Surprisingly, the effects on fear vanished in the last three years. Moreover, air pollution initially had a greater impact on expressed fear in cities with higher income, poorer air quality and a greater percentage of women. Through popularity ranking and dynamic topic model, we interpretively revealed that people are no longer overly panicked and their attention is shifting toward policies and sources of haze. Our findings highlight the temporal evolution in the public's emotional response and provide significant implications for equitable public policies.

Study on Emotional Perception of Hangzhou West Lake Scenic Area in Spring under the Influence of Meteorological Environment

Human perception of the meteorological environment is an important research area in the context of global climate change. Human physical and mental health can be affected by the meteorological environment, which can manifest in emotional responses. The experiment was conducted at spring in Hangzhou West Lake Scenic Area (China). Three types of weather circumstances were examined by four emotional measures. The purpose of this study was to examine how meteorological parameters influence an individual's emotional perception, such as air temperature, ground temperature, wind direction, precipitation, and relative humidity. Box plots were used to examine the distribution of scores on each emotional scale index. Perceptual models of positive, negative, regenerative, state anxiety, trait anxiety, and subjective vitality were developed using multiple linear regressions. The results indicate that meteorological conditions have a significant impact on human emotions: (1) there are other meteorological factors that affect individual emotions, besides precipitation; (2) the meteorological factors do not affect negative emotions; and (3) on sunny days, subjective energy and positive emotions are stronger, and on rainy days, perceptions of recovery are more favorable.

Intraday adaptation to extreme temperatures in outdoor activity

Linkages between climate and human activity are often calibrated at daily or monthly resolutions, which lacks the granularity to observe intraday adaptation behaviors. Ignoring this adaptation margin could mischaracterize the health consequences of future climate change. Here, we construct an hourly outdoor leisure activity database using billions of cell phone location requests in 10,499 parks in 2017 all over China to investigate the within-day outdoor activity rhythm. We find that hourly temperatures above 30 °C and 35 °C depress outdoor leisure activities by 5% (95% confidence interval, CI 3-7%) and by 13% (95% CI 10-16%) respectively. This activity-depressing effect is larger than previous daily or monthly studies due to intraday activity substitution from noon and afternoon to morning and evening. Intraday adaptation is larger for locations and dates with time flexibility, for individuals more frequently exposed to heat, and for parks situated in urban areas. Such within-day adaptation substantially reduces heat exposure, yet it also delays the active time at night by about half an hour, with potential side effect on sleep quality. Combining empirical estimates with outputs from downscaled climate models, we show that unmitigated climate change will generate sizable activity-depressing and activity-delaying effects in summer when projected on an hourly resolution. Our findings call for more attention in leveraging real-time activity data to understand intraday adaptation behaviors and their associated health consequences in climate change research.

How weather impacts expressed sentiment in Russia: evidence from Odnoklassniki

Prior research suggests that weather conditions may substantively impact people's emotional state and mood. In Russia, the relationship between weather and mood has been studied for certain regions-usually with severe or extreme climatic and weather conditions-but with quite limited samples of up to 1,000 people. Over the past decade, partly due to the proliferation of online social networks and the development of natural language processing techniques, the relationship between weather and mood has become possible to study based on the sentiment expressed by individuals. One of the key advantages of such studies based on digital traces is that it is possible to analyze much larger samples of people in comparison with traditional survey-based studies. In this article, we investigate the relationship between historical weather conditions and sentiment expressed in seven Russian cities based on the data of one of the largest Russian social networks, Odnoklassniki. We constructed a daily city-level expressed positive sentiment metric based on 2.76 million posts published by 1.31 million unique users from Odnoklassniki and studied its dynamics relative to daily weather conditions via regression modelling. It was found that a maximum daily temperature between +20 °C and +25 °C, light breeze (between 5 and 11 km/h) and an increase in the average daily temperature by 20-25 °C compared to the previous day are all associated with higher numbers of expressions of positive sentiment, whereas the difference between the maximum and minimum daily temperatures of 15-20 °C is associated with lower numbers of expressions of positive sentiment.

Exposures and behavioural responses to wildfire smoke

Pollution from wildfires constitutes a growing source of poor air quality globally. To protect health, governments largely rely on citizens to limit their own wildfire smoke exposures, but the effectiveness of this strategy is hard to observe. Using data from private pollution sensors, cell phones, social media posts and internet search activity, we find that during large wildfire smoke events, individuals in wealthy locations increasingly search for information about air quality and health protection, stay at home more and are unhappier. Residents of lower-income neighbourhoods exhibit similar patterns in searches for air quality information but not for health protection, spend less time at home and have more muted sentiment responses. During smoke events, indoor particulate matter (PM2.5) concentrations often remain 3-4× above health-based guidelines and vary by 20× between neighbouring households. Our results suggest that policy reliance on self-protection to mitigate smoke health risks will have modest and unequal benefits.

Temperature impacts on hate speech online: evidence from 4 billion geolocated tweets from the USA

BACKGROUND

A link between weather and aggression in the offline world has been established across a variety of societal settings. Simultaneously, the rapid digitalisation of nearly every aspect of everyday life has led to a high frequency of interpersonal conflicts online. Hate speech online has become a prevalent problem that has been shown to aggravate mental health conditions, especially among young people and marginalised groups. We examine the effect of temperature on the occurrence of hate speech on the social media platform Twitter and interpret the results in the context of the interlinkage between climate change, human behaviour, and mental health.

METHODS

In this quantitative empirical study, we used a supervised machine learning approach to identify hate speech in a dataset containing around 4 billion geolocated tweets from 773 cities across the USA between May 1, 2014 and May 1, 2020. We statistically evaluated the changes in daily hate tweets against changes in local temperature, isolating the temperature influence from confounding factors using binned panel-regression models.

FINDINGS

The prevalence of hate tweets was lowest at moderate temperatures (12 to 21°C) and marked increases in the number of hate tweets were observed at hotter and colder temperatures, reaching up to 12·5% (95% CI 8·0-16·5) for cold temperature extremes (-6 to -3°C) and up to 22·0% (95% CI 20·5-23·5) for hot temperature extremes (42 to 45°C). Outside of the moderate temperature range, the hate tweets also increased as a proportion of total tweeting activity. The quasi-quadratic shape of the temperature-hate tweet curve was robust across varying climate zones, income quartiles, religious and political beliefs, and both city-level and state-level aggregations. However, temperature ranges with the lowest prevalence of hate tweets were centred around the local temperature mean and the magnitude of the increases in hate tweets for hot and cold temperatures varied across the climate zones.

INTERPRETATION

Our results highlight hate speech online as a potential channel through which temperature alters interpersonal conflict and societal aggression. We provide empirical evidence that hot and cold temperatures can aggravate aggressive tendencies online. The prevalence of the results across climatic and socioeconomic subgroups points to limitations in the ability of humans to adapt to temperature extremes.

FUNDING

Volkswagen Foundation.

The Impact of Climate Change on Mental Health and Emotional Wellbeing: A Narrative Review of Current Evidence, and its Implications

Converging global evidence highlights the dire consequences of climate change for human mental health and wellbeing. This paper summarises literature across relevant disciplines to provide a comprehensive narrative review of the multiple pathways through which climate change interacts with mental health and wellbeing. Climate change acts as a risk amplifier by disrupting the conditions known to support good mental health, including socioeconomic, cultural and environmental conditions, and living and working conditions. The disruptive influence of rising global temperatures and extreme weather events, such as experiencing a heatwave or water insecurity, compounds existing stressors experienced by individuals and communities. This has deleterious effects on people's mental health and is particularly acute for those groups already disadvantaged within and across countries. Awareness and experiences of escalating climate threats and climate inaction can generate understandable psychological distress; though strong emotional responses can also motivate climate action. We highlight opportunities to support individuals and communities to cope with and act on climate change. Consideration of the multiple and interconnected pathways of climate impacts and their influence on mental health determinants must inform evidence-based interventions. Appropriate action that centres climate justice can reduce the current and future mental health burden, while simultaneously improving the conditions that nurture wellbeing and equality. The presented evidence adds further weight to the need for decisive climate action by decision makers across all scales.

RuSentiTweet: a sentiment analysis dataset of general domain tweets in Russian

The Russian language is still not as well-resourced as English, especially in the field of sentiment analysis of Twitter content. Though several sentiment analysis datasets of tweets in Russia exist, they all are either automatically annotated or manually annotated by one annotator. Thus, there is no inter-annotator agreement, or annotation may be focused on a specific domain. In this article, we present RuSentiTweet, a new sentiment analysis dataset of general domain tweets in Russian. RuSentiTweet is currently the largest in its class for Russian, with 13,392 tweets manually annotated with moderate inter-rater agreement into five classes: Positive, Neutral, Negative, Speech Act, and Skip. As a source of data, we used Twitter Stream Grab, a historical collection of tweets obtained from the general Twitter API stream, which provides a 1% sample of the public tweets. Additionally, we released a RuBERT-based sentiment classification model that achieved F ₁ = 0.6594 on the test subset.

Gauging the happiness benefit of US urban parks through Twitter

The relationship between nature contact and mental well-being has received increasing attention in recent years. While a body of evidence has accumulated demonstrating a positive relationship between time in nature and mental well-being, there have been few studies comparing this relationship in different locations over long periods of time. In this study, we analyze over 1.5 million tweets to estimate a happiness benefit, the difference in expressed happiness between in- and out-of-park tweets, for the 25 largest cities in the US by population. People write happier words during park visits when compared with non-park user tweets collected around the same time. While the words people write are happier in parks on average and in most cities, we find considerable variation across cities. Tweets are happier in parks at all times of the day, week, and year, not just during the weekend or summer vacation. Across all cities, we find that the happiness benefit is highest in parks larger than 100 acres. Overall, our study suggests the happiness benefit associated with park visitation is on par with US holidays such as Thanksgiving and New Year’s Day.

Effect on the health of newborns caused by extreme temperature in Guangzhou

By using 64,270 daily observations from a large hospital in Guangzhou between 2017 and 2019, we analyzed the impact of extreme temperature on the health of newborns via OLS regression with time fixed effect. Given that the short-term temperature change can be regarded as exogenous and random, solving the potential endogenous problem is critical. We find that extreme temperature negatively affects the health of newborns. The Apgar score, an index for evaluating neonatal health, decreases by 0.008 (0.029%) when the duration of extreme temperature events increases by a day. A series of robustness checks verify the reliability of this negative effect. Extreme temperature also has a particularly serious effect on the health of newborns whose mothers have poor education. By gradually extending the observation period, we find that the effect of extreme temperature on neonatal health is mainly concentrated 1-6 weeks before delivery, whereas the effect of extreme temperature on hospitalization cost is mainly concentrated 4-8 weeks before delivery. This paper provides a valuable reference for evaluating the health and social costs of extreme weather, and our findings are conducive to the construction of climate-resilient health systems, especially in Guangzhou.

How seasons, weather, and part of day influence baseline affective valence in laboratory research participants?

Many people believe that weather influences their emotional state. Along similar lines, some researchers in affective science are concerned whether testing individuals at a different time of year, a different part of the day, or in different weather conditions (e.g., in a cold and rainy morning vs. a hot evening) influences how research participants feel upon entering a study; thus inflating the measurement error. Few studies have investigated the link between baseline affective levels and the research context, such as seasonal and daily weather fluctuation in temperature, air pressure, and sunshine duration. We examined whether individuals felt more positive or negative upon entering a study by clustering data across seven laboratory experiments (total N = 1108), three seasons, and daily times ranging from 9 AM to 7 PM. We accounted for ambient temperature, air pressure, humidity, cloud cover, precipitation, wind speed, and sunshine duration. We found that only ambient temperature was a significant predictor of valence. Individuals felt more positive valence on days when it was cooler outside. However, the effect was psychologically negligible with differences between participants above c.a. 30 degrees Celsius in ambient temperature needed to generate a difference in affective valence surpassing one standard deviation. Our findings have methodological implications for studying emotions by suggesting that seasons and part of the day do not matter for baseline affective valence reported by participants, and the effects of ambient temperature are unlikely to influence most research.

Can Urban Forest Settings Evoke Positive Emotion? Evidence on Facial Expressions and Detection of Driving Factors

There is increasing interest in experiences of urban forests because relevant studies have revealed that forest settings can promote mental well-being. The mental response to a forest experience can be evaluated by facial expressions, but relevant knowledge is limited at large geographical scales. In this study, a dataset of 2824 photos, detailing the evaluated age (toddler, youth, middle-age, and senior citizen) and gender of urban forest visitors, was collected from Sina Weibo (a social media application similar to Twitter in China) between 1–7 October 2018, in 12 randomly chosen cities in China. Happy and sad expressions were rated as scores by FireFACE software V1.0, and the positive response index (PRI) was calculated by subtracting sad scores from happy scores. Regional environmental factors were collected to detect driving forces using regression analyses. Happy scores were higher in forests than in urban settings, while sad scores for toddlers were lower in forests than in promenades and squares. Females showed more positive emotional expressions than males. Increases in happy scores were driven by the increase of daily minimum temperature; while PRI declined with increases in latitude. Overall, an urban forest experience can evoke positive emotions, which is likely due to comfortable feelings in warm temperatures.

The temperature of emotions

Emotions and temperature are closely related through embodied processes, and people seem to associate temperature concepts with emotions. While this relationship is often evidenced by everyday language (e.g., cold and warm feelings), what remains missing to date is a systematic study that holistically analyzes how and why people associate specific temperatures with emotions. The present research aimed to investigate the associations between temperature concepts and emotion adjectives on both explicit and implicit levels. In Experiment 1, we evaluated explicit associations between twelve pairs of emotion adjectives derived from the circumplex model of affect, and five different temperature concepts ranging from 0°C to 40°C, based on responses from 403 native speakers of four different languages (English, Spanish, Japanese, Chinese). The results of Experiment 1 revealed that, across languages, the temperatures were associated with different regions of the circumplex model. The 0°C and 10°C were associated with negative-valanced, low-arousal emotions, while 20°C was associated with positive-valanced, low-to-medium-arousal emotions. Moreover, 30°C was associated with positive-valanced, high-arousal emotions; and 40°C was associated with high-arousal and either positive- or negative-valanced emotions. In Experiment 2 (N = 102), we explored whether these temperature-emotion associations were also present at the implicit level, by conducting Implicit Association Tests (IATs) with temperature words (cold and hot) and opposing pairs of emotional adjectives for each dimension of valence (Unhappy/Dissatisfied vs. Happy/Satisfied) and arousal (Passive/Quiet vs. Active/Alert) on native English speakers. The results of Experiment 2 revealed that participants held implicit associations between the word hot and positive-valanced and high-arousal emotions. Additionally, the word cold was associated with negative-valanced and low-arousal emotions. These findings provide evidence for the existence of temperature-emotion associations at both explicit and implicit levels across languages.

Social Sensing of Heatwaves

Heatwaves cause thousands of deaths every year, yet the social impacts of heat are poorly measured. Temperature alone is not sufficient to measure impacts and “heatwaves” are defined differently in different cities/countries. This study used data from the microblogging platform Twitter to detect different scales of response and varying attitudes to heatwaves within the United Kingdom (UK), the United States of America (US) and Australia. At the country scale, the volume of heat-related Twitter activity increased exponentially as temperature increased. The initial social reaction differed between countries, with a larger response to heatwaves elicited from the UK than from Australia, despite the comparatively milder conditions in the UK. Language analysis reveals that the UK user population typically responds with concern for individual wellbeing and discomfort, whereas Australian and US users typically focus on the environmental consequences. At the city scale, differing responses are seen in London, Sydney and New York on governmentally defined heatwave days; sentiment changes predictably in London and New York over a 24-h period, while sentiment is more constant in Sydney. This study shows that social media data can provide robust observations of public response to heat, suggesting that social sensing of heatwaves might be useful for preparedness and mitigation.

Air Pollution Reduces Interpersonal Trust: The Roles of Emotion and Emotional Susceptibility

Air pollution has been shown to have detrimental effects on physical and mental health, yet little is known about how air pollution affects psychosocial functioning in everyday life. We conducted three studies that utilized experimental methods and web crawler technology to examine the effect of hazy environmental conditions on perceived interpersonal trust, and to investigate the roles of emotion and emotional susceptibility in mediating or moderating the negative impact of air pollution. In Study 1, participants were presented with landscape photos that showed either hazy scenes or clear scenes. Those who viewed photos of hazy scenes reduced their levels of interpersonal trust. In Study 2, emotion data were collected from social media with web crawler technology, in connection with meteorological monitoring data during the same period. Hazy conditions were associated with reduced expressions of positive emotion on social media, whereas clearer conditions were associated with enhanced positive emotional expressions. In Study 3, we simulated Weibo communications in the laboratory. The findings showed that emotional susceptibility moderated the negative effect of hazy conditions on interpersonal trust, and negative emotion mediated the effect of hazy conditions on interpersonal trust. The findings advance the understanding of the impact of air pollution on interpersonal trust and social relations and the associated psychological mechanisms and boundary conditions. They have important real-life implications.

A social Beaufort scale to detect high winds using language in social media posts

People often talk about the weather on social media, using different vocabulary to describe different conditions. Here we combine a large collection of wind-related Twitter posts (tweets) and UK Met Office wind speed observations to explore the relationship between tweet volume, tweet language and wind speeds in the UK. We find that wind speeds are experienced subjectively relative to the local baseline, so that the same absolute wind speed is reported as stronger or weaker depending on the typical weather conditions in the local area. Different linguistic tokens (words and emojis) are associated with different wind speeds. These associations can be used to create a simple text classifier to detect ‘high-wind’ tweets with reasonable accuracy; this can be used to detect high winds in a locality using only a single tweet. We also construct a ‘social Beaufort scale’ to infer wind speeds based only on the language used in tweets. Together with the classifier, this demonstrates that language alone is indicative of weather conditions, independent of tweet volume. However, the number of high-wind tweets shows a strong temporal correlation with local wind speeds, increasing the ability of a combined language-plus-volume system to successfully detect high winds. Our findings complement previous work in social sensing of weather hazards that has focused on the relationship between tweet volume and severity. These results show that impacts of wind and storms are found in how people communicate and use language, a novel dimension in understanding the social impacts of extreme weather.

Affective Temperaments and Meteoropathy Among Women: A Cross-sectional Study

The main goal of the study was to assess the relationship between affective temperaments and meteoropathy among women and examine meteorosensitivity as a mediator in this relationship. The issue of affective temperaments and meteoropathy has not been considered in the literature. The sample consisted of 450 Caucasian women gathered via the online recruitment platform. The participants’ ages ranged from 18 to 70 years (M = 30.01; SD = 9.10). The Polish version of the Temperament Evaluation of Memphis, Pisa, Paris and San Diego Autoquestionnaire was used to assess affective temperaments (depressive, cyclothymic, hyperthymic, irritable and anxious). Meteorosensitivity and meteoropathy were assessed using the Polish adaptation of the METEO-Q questionnaire. A large positive correlation was found between meteorosensitivity and meteoropathy. Medium positive correlations were found between meteorosensitivity/meteoropathy and cyclothymic and anxious temperaments. Small positive correlations were revealed between depressive and irritable temperaments and both meteorosensitivity and meteoropathy scales. No correlation was found between hyperthymic temperament and meteorosensitivity/meteoropathy. Mediation analyses indicated cyclothymic and anxious temperaments affected meteoropathy both directly and indirectly through meteorosensitivity as a mediator. The most severe meteoropathy symptoms in the studied sample were asthenia, an indefinite feeling of malaise and irritability. The results suggest affective temperaments may be related to meteoropathy symptoms in women.

Temperature and self-reported mental health in the United States

This study estimates the association between temperature and self-reported mental health. We match individual-level mental health data for over three million Americans between 1993 and 2010 to historical daily weather information. We exploit the random fluctuations in temperature over time within counties to identify its effect on a 30-day measure of self-reported mental health. Compared to the temperature range of 60-70°F, cooler days in the past month reduce the probability of reporting days of bad mental health while hotter days increase this probability. We also find a salience effect: cooler days have an immediate effect, whereas hotter days tend to matter most after about 10 days. Using our estimates, we calculate the willingness to pay to avoid an additional hot day in terms of its impact on self-reported mental health.

Temperature and mental health: Evidence from the spectrum of mental health outcomes

This paper characterizes the link between ambient temperatures and a broad set of mental health outcomes. We find that higher temperatures increase emergency department visits for mental illness, suicides, and self-reported days of poor mental health. Specifically, cold temperatures reduce negative mental health outcomes while hot temperatures increase them. Our estimates reveal no evidence of adaptation, instead the temperature relationship is stable across time, baseline climate, air conditioning penetration rates, accessibility of mental health services, and other factors. The character of the results suggests that temperature affects mental health very differently than physical health, and more similarly to other psychological and behavioral outcomes. We provide suggestive evidence for sleep disruption as an active mechanism behind our results and discuss the implications of our findings for the allocation of mental health services and in light of climate change.

Social Media Data-Based Sentiment Analysis of Tourists’ Air Quality Perceptions

Analyzing tourists’ perceptions of air quality is of great significance to the study of tourist experience satisfaction and the image construction of tourism destinations. In this study, using the web crawler technique, we collected 27,500 comments regarding the air quality of 195 of China’s Class 5A tourist destinations posted by tourists on Sina Weibo from January 2011 to December 2017; these comments were then subjected to a content analysis using the Gooseeker, ROST CM (Content Mining System) and BosonNLP (Natural Language Processing) tools. Based on an analysis of the proportions of sentences with different emotional polarities with ROST EA (Emotion Analysis), we measured the sentiment value of texts using the artificial neural network (ANN) machine learning method implemented through a Chinese social media data-oriented Boson platform based on the Python programming language. The content analysis results indicated that in the adaption stage in Sina Weibo, tourists’ perceptions of air quality were mainly positive and had poor air pollution crisis awareness. Objective emotion words exhibited a similarly high proportion as subjective emotion words, indicating that taking both objective and subjective emotion words into account simultaneously helps to comprehensively understand the emotional content of the comments. The sentiment analysis results showed that for the entire text, sentences with positive emotions accounted for 85.53% of the total comments, with a sentiment value of 0.786, which belonged to the positive medium level; the direction of the temporal “up-down-up” changes and the spatial pattern of high in the south and low in the north (while having little difference between the east and the west) were basically consistent with reality. A further exploration of the theoretical basis of the semi-supervised ANN approach or the introduction of other machine learning methods using different data sources will help to analyze this phenomenon in greater depth. The paper provides evidence for new data and methods for air quality research in tourist destinations and provides a new tool for air quality monitoring.

Risk of a feedback loop between climatic warming and human mobility

Human behaviours alter-and are altered by-climate. Might the impacts of warming on human behaviours amplify anthropogenic contributions to climate change? Here, we show that warmer temperatures substantially increase transportation use in the USA. To do so, we combine meteorological data with data on vehicle miles travelled (VMT) and public transit trips (PTT) between 2002 and 2018. Moving from freezing temperatures up to 30°C increases VMT by over 10% and amplifies use of public transit by nearly 15%. Temperatures beyond 30°C exert little influence on either outcome. We then examine climate model projections to highlight the possible transportation impacts of future climatic changes. We project that warming over the coming century may add over one trillion cumulative VMT and six billion PTT in the USA alone, presenting the risk of a novel feedback loop in the human-environmental system.

Seasonality Patterns of Internet Searches on Mental Health: Exploratory Infodemiology Study

BACKGROUND

The study of seasonal patterns of public interest in psychiatric disorders has important theoretical and practical implications for service planning and delivery. The recent explosion of internet searches suggests that mining search databases yields unique information on public interest in mental health disorders, which is a significantly more affordable approach than population health studies.

OBJECTIVE

This study aimed to investigate seasonal patterns of internet mental health queries in Ontario, Canada.

METHODS

Weekly data on health queries in Ontario from Google Trends were downloaded for a 5-year period (2012-2017) for the terms "schizophrenia," "autism," "bipolar," "depression," "anxiety," "OCD" (obsessive-compulsive disorder), and "suicide." Control terms were overall search results for the terms "health" and "how." Time-series analyses using a continuous wavelet transform were performed to isolate seasonal components in the search volume for each term.

RESULTS

All mental health queries showed significant seasonal patterns with peak periodicity occurring over the winter months and troughs occurring during summer, except for "suicide." The comparison term "health" also exhibited seasonal periodicity, while the term "how" did not, indicating that general information seeking may not follow a seasonal trend in the way that mental health information seeking does.

CONCLUSIONS

Seasonal patterns of internet search volume in a wide range of mental health terms were observed, with the exception of "suicide." Our study demonstrates that monitoring internet search trends is an affordable, instantaneous, and naturalistic method to sample public interest in large populations and inform health policy planners.

Rapidly declining remarkability of temperature anomalies may obscure public perception of climate change

The changing global climate is producing increasingly unusual weather relative to preindustrial conditions. In an absolute sense, these changing conditions constitute direct evidence of anthropogenic climate change. However, human evaluation of weather as either normal or abnormal will also be influenced by a range of factors including expectations, memory limitations, and cognitive biases. Here we show that experience of weather in recent years-rather than longer historical periods-determines the climatic baseline against which current weather is evaluated, potentially obscuring public recognition of anthropogenic climate change. We employ variation in decadal trends in temperature at weekly and county resolution over the continental United States, combined with discussion of the weather drawn from over 2 billion social media posts. These data indicate that the remarkability of particular temperatures changes rapidly with repeated exposure. Using sentiment analysis tools, we provide evidence for a "boiling frog" effect: The declining noteworthiness of historically extreme temperatures is not accompanied by a decline in the negative sentiment that they induce, indicating that social normalization of extreme conditions rather than adaptation is driving these results. Using climate model projections we show that, despite large increases in absolute temperature, anomalies relative to our empirically estimated shifting baseline are small and not clearly distinguishable from zero throughout the 21st century.

Air pollution lowers Chinese urbanites' expressed happiness on social media

High levels of air pollution in China may contribute to the urban population's reported low level of happiness^1-3. To test this claim, we have constructed a daily city-level expressed happiness metric based on the sentiment in the contents of 210 million geotagged tweets on the Chinese largest microblog platform Sina Weibo^4-6, and studied its dynamics relative to daily local air quality index and PM_2.5 concentrations (fine particulate matter with diameters equal or smaller than 2.5 μm, the most prominent air pollutant in Chinese cities). Using daily data for 144 Chinese cities in 2014, we document that, on average, a one standard deviation increase in the PM_2.5 concentration (or Air Quality Index) is associated with a 0.043 (or 0.046) standard deviation decrease in the happiness index. People suffer more on weekends, holidays and days with extreme weather conditions. The expressed happiness of women and the residents of both the cleanest and dirtiest cities are more sensitive to air pollution. Social media data provides real-time feedback for China's government about rising quality of life concerns.

Negative Emotion under Haze: An Investigation Based on the Microblog and Weather Records of Tianjin, China

Nowadays, many big cities are suffering from heavy air pollution and continuous haze weather. Compared with the threat on physical health, the influence of haze on people’s mental health is much less discussed in the current literature. Emotion is one of the most important indicators of mental health. To understand the negative impact of haze weather on the emotion of the people, we conducted an investigation based on historical weather records and microblog data in Tianjin, China. Specifically, an emotional thesaurus was generated with a microblog corpus collected from sample data. Based on the thesaurus, the public emotion under haze was statistically described. Then, through correlation analysis and comparative study, the relation and seasonal variation of haze and negative emotion of the public were well discussed. According to the study results, there was indeed a correlation between haze and negative emotion of the public, but the strength of this relationship varied under different conditions. The level of air pollution and weather context were both important factors that influence the mental effects of haze, and diverse patterns of negative emotion expression were demonstrated in different seasons of a year. Finally, for the benefit of people’s mental health under haze, recommendations were given for haze control from the side of government.

Real-world unexpected outcomes predict city-level mood states and risk-taking behavior

Fluctuations in mood states are driven by unpredictable outcomes in daily life but also appear to drive consequential behaviors such as risk-taking. However, our understanding of the relationships between unexpected outcomes, mood, and risk-taking behavior has relied primarily upon constrained and artificial laboratory settings. Here we examine, using naturalistic datasets, how real-world unexpected outcomes predict mood state changes observable at the level of a city, in turn predicting changes in gambling behavior. By analyzing day-to-day mood language extracted from 5.2 million location-specific and public Twitter posts or 'tweets', we examine how real-world 'prediction errors'-local outcomes that deviate positively from expectations-predict day-to-day mood states observable at the level of a city. These mood states in turn predicted increased per-person lottery gambling rates, revealing how interplay between prediction errors, moods, and risky decision-making unfolds in the real world. Our results underscore how social media and naturalistic datasets can uniquely allow us to understand consequential psychological phenomena.

Empirical evidence of mental health risks posed by climate change

Sound mental health-a critical facet of human wellbeing-has the potential to be undermined by climate change. Few large-scale studies have empirically examined this hypothesis. Here, we show that short-term exposure to more extreme weather, multiyear warming, and tropical cyclone exposure each associate with worsened mental health. To do so, we couple meteorological and climatic data with reported mental health difficulties drawn from nearly 2 million randomly sampled US residents between 2002 and 2012. We find that shifting from monthly temperatures between 25 °C and 30 °C to >30 °C increases the probability of mental health difficulties by 0.5% points, that 1°C of 5-year warming associates with a 2% point increase in the prevalence of mental health issues, and that exposure to Hurricane Katrina associates with a 4% point increase in this metric. Our analyses provide added quantitative support for the conclusion that environmental stressors produced by climate change pose threats to human mental health.

Effects of environmental stressors on daily governance

Public servants are often first responders to disasters, and the day-to-day completion of their jobs aids public health and safety. However, with respect to their individual psychological and physiological responses to environmental stressors, public sector workers may be harmed in much the same way as other citizens in society. We find that exposure to hotter temperatures reduces the activity of two groups of regulators—police officers and food safety inspectors—at times that the risks they are tasked with overseeing are highest. Given that we observe these effects in a country with high political institutionalization, our findings may have implications for the impacts of climate change on the functioning of regulatory governance in countries with lower political and economic development.

Human workers ensure the functioning of governments around the world. The efficacy of human workers, in turn, is linked to the climatic conditions they face. Here we show that the same weather that amplifies human health hazards also reduces street-level government workers’ oversight of these hazards. To do so, we employ US data from over 70 million regulatory police stops between 2000 and 2017, from over 500,000 fatal vehicular crashes between 2001 and 2015, and from nearly 13 million food safety violations across over 4 million inspections between 2012 and 2016. We find that cold and hot temperatures increase fatal crash risk and incidence of food safety violations while also decreasing police stops and food safety inspections. Added precipitation increases fatal crash risk while also decreasing police stops. We examine downscaled general circulation model output to highlight the possible day-to-day governance impacts of climate change by 2050 and 2099. Future warming may augment regulatory oversight during cooler seasons. During hotter seasons, however, warming may diminish regulatory oversight while simultaneously amplifying the hazards government workers are tasked with overseeing.

Temporal and spatiotemporal investigation of tourist attraction visit sentiment on Twitter

In this paper, we propose a sentiment-based approach to investigate the temporal and spatiotemporal effects on tourists' emotions when visiting a city's tourist destinations. Our approach consists of four steps: data collection and preprocessing from social media; visitor origin identification; visit sentiment identification; and temporal and spatiotemporal analysis. The temporal and spatiotemporal dimensions include day of the year, season of the year, day of the week, location sentiment progression, enjoyment measure, and multi-location sentiment progression. We apply this approach to the city of Chicago using over eight million tweets. Results show that seasonal weather, as well as special days and activities like concerts, impact tourists' emotions. In addition, our analysis suggests that tourists experience greater levels of enjoyment in places such as observatories rather than zoos. Finally, we find that local and international visitors tend to convey negative sentiment when visiting more than one attraction in a day whereas the opposite holds for out of state visitors.