While it has long been believed in psychology that weather somehow influences human's mood, the debates have been going on for decades about how they are correlated. In this paper, we try to study this long-lasting topic by harnessing a new source of data compared from traditional psychological researches: Twitter. We analyze 2 years' twitter data collected by twitter API which amounts to @math of all postings and try to reveal the correlations between multiple dimensional structure of human mood with meteorological effects. Some of our findings confirm existing hypotheses, while others contradict them. We are hopeful that our approach, along with the new data source, can shed on the long-going debates on weather-mood correlation.
Despite the on-going debates in psychology, most physiologists believe that the weather indeed has significant influence on psychological processes based on strong physiological evidence. For example, @cite_10 spotted rising and dipping production of serotonin, a neurotransmitter in human central nervous system, which is generally thought to be a contributor to feelings of well-being and happiness, as sunlight increased or decreased. Another line of evidence is provided by population-wide behavior studies in social psychology. A good example is seasonal affective disorder (SAD), which refers to the mood disorder in which people who have normal mental health throughout most of the year experience depressive symptoms in the winter http: en.wikipedia.org wiki Seasonal_affective_disorder . Researchers suggest that exposure to sunlight would immediately improve mood and diminishes SAD @cite_35 @cite_10 @cite_55 @cite_8 and that rainy or cloudy days can aggravate the symptoms @cite_55 . Additionally, reliable and significant associations are found between weather and mood-related social behaviors. For example, low barometric pressure @cite_13 and more snow are evidently linked with higher suicide rates @cite_21 and high temperature is associated with violent behavior @cite_3 @cite_12 .
@cite_35 @cite_8 @cite_10 @cite_55 @cite_21 @cite_3 @cite_13 @cite_12
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Ten mood variables were related to eight weather variables in a multidimensional study. Data were collected from 24 male subjects over 11 consecutive days. The mood variables were as follows: concentration, cooperation, anxiety, potency, aggression, depression, sleepiness, scepticism, control, and optimism. The weather variables included: hours of sunshine, precipitation, temperature, wind direction, wind velocity, humidity, change in barometric pressure and absolute barometric pressure. Humidity, temperature and hours of sunshine had the greatest effect on mood. High levels of humidity lowered scores on concentration while increasing reports of sleepiness. Rising temperatures lowered anxiety and scepticism mood scores. Humidity was the most significant predictor in regression and canonical correlation analysis. Implications for school and office performance are discussed which highlight the importance of humidity as a weather variable. Summary Alterations in monoaminergic neurotransmission in the brain are thought to underlie seasonal variations in mood, behaviour, and affective disorders. We took blood samples from internal jugular veins in 101 healthy men, to assess the relation between concentration of serotonin metabolite in these samples and weather conditions and season. We showed that turnover of serotonin by the brain was lowest in winter (p=0·013). Moreover, the rate of production of serotonin by the brain was directly related to the prevailing duration of bright sunlight (r=0·294, p=0·010), and rose rapidly with increased luminosity. Our findings are further evidence for the notion that changes in release of serotonin by the brain underlie mood seasonality and seasonal affective disorder. Correlations were calculated between daily self-evaluated mood reports (4 reports day) of 10 student subjects and 10 meteorological-geophysical variables over a 90-day period. The variables included mean and or change measures of temperature, barometric pressure, relative humidity, sunshine hours, wind speeds and global geomagnetic activity. Multiple regression analyses indicated that the weather matrix used could account for not more than 35 of the mood variance on the day of the evaluation. Lag correlations over the previous week clearly indicated a greater number of significant correlations between mood reports and weather of the previous two days. The mean correlation coefficient was 0.27. In general, "lower moods" were associated with fewer sunshine hours, higher relative humidity, and smaller humidity variations than "higher moods". It was concluded that mood reports can show weak responsivity to antecedent weather fluctuations. Research has shown that greater degrees of sunshine experienced directly are associated with greater amounts of compliance and tipping. This paper describes two studies that investigated beliefs about sunshine and tipping. The studies were conducted at a casino hotel in Atlantic City; a male server who delivered food and drinks to guests' rooms acted as the confederate. In Study 1, the server reported to guests the actual sky conditions: sunny, partly sunny, cloudy, or rainy. Tip percentages increased linearly from the worst to the best conditions. Study 2 used an experimental design. The server informed guests that the weather was either warm and sunny, cold and sunny, warm and rainy, or cold and rainy. Guests were gullible because their rooms shielded them from the actual conditions. Belief in sunny skies produced greater tip percentages. The temperature belief manipulation had no effect. This research extended previous research by showing that beliefs about weather, in addition to actual weather, can affect behavior. Abstract Background: Physical exercise alleviates depressive symptoms, as does exposure to bright light, especially in those with seasonal variation. Our objective was to compare the effect of exercise alone or combined with morning bright light on mood and the health-related quality of life in healthy subjects. Methods: Study subjects were working-age adults, randomized in two groups ( n =80): exercise in bright light (group A), or exercise in normal indoor illumination (group B). Intervention lasted for 8 weeks and questionnaire data on mood and the health-related quality of life were collected at study entry, and at weeks 4 and 8. Results: Physical exercise both in normal indoor illumination and in bright light was effective at alleviating depressive symptoms. The exercise was significantly more effective at alleviating so-called atypical depressive symptoms when combined with bright-light exposure. Limitations: There was no active placebo condition, but a comparative, randomized trial was executed. Conclusions: Physical exercise in bright light had a positive effect on mood and health-related quality of life in a sample of healthy, working-age people. Further research is needed to explore the mechanisms of the apparent additive effect of exercise and light. Behavioral economics tells us that emotions can profoundly affect individual behavior and decision-making. Does this also apply to societies at large, i.e. can societies experience mood states that affect their collective decision making? By extension is the public mood correlated or even predictive of economic indicators? Here we investigate whether measurements of collective mood states derived from large-scale Twitter feeds are correlated to the value of the Dow Jones Industrial Average (DJIA) over time. We analyze the text content of daily Twitter feeds by two mood tracking tools, namely OpinionFinder that measures positive vs. negative mood and Google-Profile of Mood States (GPOMS) that measures mood in terms of 6 dimensions (Calm, Alert, Sure, Vital, Kind, and Happy). We cross-validate the resulting mood time series by comparing their ability to detect the public's response to the presidential election and Thanksgiving day in 2008. A Granger causality analysis and a Self-Organizing Fuzzy Neural Network are then used to investigate the hypothesis that public mood states, as measured by the OpinionFinder and GPOMS mood time series, are predictive of changes in DJIA closing values. Our results indicate that the accuracy of DJIA predictions can be significantly improved by the inclusion of specific public mood dimensions but not others. We find an accuracy of 87.6 in predicting the daily up and down changes in the closing values of the DJIA and a reduction of the Mean Average Percentage Error by more than 6 . Index Terms—stock market prediction — twitter — mood analysis. We identified individual-level diurnal and seasonal mood rhythms in cultures across the globe, using data from millions of public Twitter messages. We found that individuals awaken in a good mood that deteriorates as the day progresses—which is consistent with the effects of sleep and circadian rhythm—and that seasonal change in baseline positive affect varies with change in daylength. People are happier on weekends, but the morning peak in positive affect is delayed by 2 hours, which suggests that people awaken later on weekends. How do emotions and moods color cognition? In this article, we examine how such reactions influence both judgments and cognitive performance. We argue that many affective influences are due, not to affective reactions themselves, but to the information they carry about value. The specific kind of influence that occurs depends on the focus of the agent at the time. When making evaluative judgments, for example, an agent's positive affect may emerge as a positive attitude toward a person or object. But when an agent focuses on a cognitive task, positive affect may act like feedback about the value of one's approach. As a result, positive affect tends to promote cognitive, relational processes, whereas negative affect tends to inhibit relational processing, resulting in more perceptual, stimulus-specific processing. As a consequence, many textbook phenomena from cognitive psychology occur readily in happy moods, but are inhibited or even absent in sad moods (149).