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). Depression is one of the most common mental health disorders with strong adverse effects on personal and social functioning. The absence of any objective diagnostic aid for depression leads to a range of subjective biases in initial diagnosis and ongoing monitoring. Psychologists use various visual cues in their assessment to quantify depression such as facial expressions, eye contact and head movements. This paper studies the contribution of (upper) body expressions and gestures for automatic depression analysis. A framework based on space-time interest points and bag of words is proposed for the analysis of upper body and facial movements. Salient interest points are selected using clustering. The major contribution of this paper lies in the creation of a bag of body expressions and a bag of facial dynamics for assessing the contribution of different body parts for depression analysis. Head movement analysis is performed by selecting rigid facial fiducial points and a new histogram of head movements is proposed. The experiments are performed on real-world clinical data where video clips of patients and healthy controls are recorded during interactive interview sessions. The results show the effectiveness of the proposed system to evaluate the contribution of various body parts in depression analysis. Abstract We investigated platelet 14 C-serotonin uptake and platelet [ 3 H]LSD and [ 3 H]paroxetine binding in 11 patients with seasonal affective disorder (SAD). Patients were reinvestigated after light therapy, applied at 07.00–09.00 h for 10 consecutive days. The degree of depression was rated before and after light therapy using the Comprehensive Psychopathological Rating Scale (CPRS). Baseline data in patients were compared with data from a control group consisting of 11 age- and sex-matched healthy volunteers. Seven patients responded to light therapy with a>50 reduction in CPRS scores. In non-responders, the reduction in CPRS was 24.7±5.5 . There was a significant inverse correlation ( P =0.014) between K m for platelet 14 C-serotonin uptake and CPRS scores. Patients had significantly higher B max for platelet [ 3 H]LSD binding ( P =0.04) and significantly lower B max for platelet [ 3 H]paroxetine binding ( P =0.016). There was a strong, multiple correlation between B max for [ 3 H]LSD, as the dependent variable, and K m , V max and B max for [ 3 H]paroxetine binding in patients ( P K m ( P =0.023) and significantly lower B max for [ 3 H]paroxetine binding ( P =0.028) than non-responders. B max for [ 3 H]paroxetine binding increased significantly to normal levels after light therapy. The results indicate that SAD is associated with aberrations in the serotonin uptake mechanism. The enhanced 5-HT 2 -receptor density may reflect a consequential up-regulation. Depression is a major cause of disability world-wide. The present paper reports on the results of our participation to the depression sub-challenge of the sixth Audio Visual Emotion Challenge (AVEC 2016), which was designed to compare feature modalities (audio, visual, interview transcript-based) in gender-based and gender-independent modes using a variety of classification algorithms. In our approach, both high and low level features were assessed in each modality. Audio features were extracted from the low-level descriptors provided by the challenge organizers. Several visual features were extracted and assessed including dynamic characteristics of facial elements (using Landmark Motion History Histograms and Landmark Motion Magnitude), global head motion, and eye blinks. These features were combined with statistically derived features from pre-extracted features (emotions, action units, gaze, and pose). Both speech rate and word-level semantic content were also evaluated. Classification results are reported using four different classification schemes: i) gender-based models for each individual modality, ii) the feature fusion model, ii) the decision fusion model, and iv) the posterior probability classification model. Proposed approaches outperforming the reference classification accuracy include the one utilizing statistical descriptors of low-level audio features. This approach achieved f1-scores of 0.59 for identifying depressed and 0.87 for identifying not-depressed individuals on the development set and 0.52 0.81, respectively for the test set. Compared the effects of 2 psychotherapies based on divergent conceptualizations of depression in later life. Seventy-five older adults diagnosed with major depressive disorder were assigned randomly to problem-solving therapy (PST), reminiscence therapy (RT), or a waiting-list control (WLC) condition. Participants in PST and RT were provided with 12 weekly sessions of group treatment. Dependent measures, taken at baseline, posttreatment, and 3-month follow-up, included self-report and observer-based assessments of depressive symptomatology. At posttreatment, both the PST and the RT conditions produced significant reductions in depressive symptoms, compared with the WLC group, and PST participants experienced significantly less depression than RT subjects. Moreover, a significantly greater proportion of participants in PST versus RT demonstrated sufficient positive change to warrant classification of their depression as improved or in remission at the posttreatment and follow-up evaluations. Depression constitutes the most common emotional disorder found in older people (Butler, Lewis, & Sunderland, 1991). Estimates of the prevalence of major depressive disorder in the elderly range from 2 to 10 (Blazer, Hughes, & George, 1987), with milder forms of depression such as dysthymia and dysphoria affecting 20 to 30 of older adults (, 1991). Moreover, the clinical significance of depression in the elderly is underscored by the consistent finding that suicide occurs more frequently in the elderly than in any other age group (Rich, Young, & Fowler, 1986). Recently, a consensus development panel of the National Institutes of Health (NIH; 1992) urged vigorous treatment of depressed elderly with somatic therapies (i.e., drugs, electroconvulsive therapy [ECT], or both); psychosocial therapies were recommended as secondary or supplemental interventions. In justifying these recommendations, members of the NIH panel noted that, in comparison with the substantial research base supportive of somatic therapies (for a review, see Gerson, Plotkin, & Jarvik, 1988), there exists a relative dearth of research Major depressive disorder (MDD) is known to result in neurophysiological and neurocognitive changes that affect control of motor, linguistic, and cognitive functions. MDD's impact on these processes is reflected in an individual's communication via coupled mechanisms: vocal articulation, facial gesturing and choice of content to convey in a dialogue. In particular, MDD-induced neurophysiological changes are associated with a decline in dynamics and coordination of speech and facial motor control, while neurocognitive changes influence dialogue semantics. In this paper, biomarkers are derived from all of these modalities, drawing first from previously developed neurophysiologically-motivated speech and facial coordination and timing features. In addition, a novel indicator of lower vocal tract constriction in articulation is incorporated that relates to vocal projection. Semantic features are analyzed for subject avatar dialogue content using a sparse coded lexical embedding space, and for contextual clues related to the subject's present or past depression status. The features and depression classification system were developed for the 6th International Audio Video Emotion Challenge (AVEC), which provides data consisting of audio, video-based facial action units, and transcribed text of individuals communicating with the human-controlled avatar. A clinical Patient Health Questionnaire (PHQ) score and binary depression decision are provided for each participant. PHQ predictions were obtained by fusing outputs from a Gaussian staircase regressor for each feature set, with results on the development set of mean F1=0.81, RMSE=5.31, and MAE=3.34. These compare favorably to the challenge baseline development results of mean F1=0.73, RMSE=6.62, and MAE=5.52. On test set evaluation, our system obtained a mean F1=0.70, which is similar to the challenge baseline test result. Future work calls for consideration of joint feature analyses across modalities in an effort to detect neurological disorders based on the interplay of motor, linguistic, affective, and cognitive components of communication. To test the hypothesis that activation asymmetries of the most anterior parts of the prefrontal cortex may be related to state-dependent regulation of emotion, spontaneous changes of cortical activation asymmetries from one session to a second one were related to spontaneous mood changes in two large samples (ns = 56 and 128). The interval between sessions was 2 to 4 weeks. Results show that mood changes specifically covary with changes of EEG asymmetry at the frontopolar electrode positions, but not with changes at other locations (dorsolateral frontal, temporal, and pariet al). Anxiety, tension, and depression were found to decrease when frontopolar activation asymmetry shifted to the right. Taking the new findings into account may contribute to the refinement and extension of theories on EEG laterality and emotion. Depression is a serious mental disorder that affects millions of people all over the world. Traditional clinical diagnosis methods are subjective, complicated and need extensive participation of experts. Audio-visual automatic depression analysis systems predominantly base their predictions on very brief sequential segments, sometimes as little as one frame. Such data contains much redundant information, causes a high computational load, and negatively affects the detection accuracy. Final decision making at the sequence level is then based on the fusion of frame or segment level predictions. However, this approach loses longer term behavioural correlations, as the behaviours themselves are abstracted away by the frame-level predictions. We propose to on the one hand use automatically detected human behaviour primitives such as Gaze directions, Facial action units (AU), etc. as low-dimensional multi-channel time series data, which can then be used to create two sequence descriptors. The first calculates the sequence-level statistics of the behaviour primitives and the second casts the problem as a Convolutional Neural Network problem operating on a spectral representation of the multichannel behaviour signals. The results of depression detection (binary classification) and severity estimation (regression) experiments conducted on the AVEC 2016 DAIC-WOZ database show that both methods achieved significant improvement compared to the previous state of the art in terms of the depression severity estimation. We present an evaluation of standard image features in the context of long-term visual teach-and-repeat navigation of mobile robots, where the environment exhibits significant changes in appearance caused by seasonal weather variations and daily illumination changes. We argue that for long-term autonomous navigation, the viewpoint-, scale- and rotation- invariance of the standard feature extractors is less important than their robustness to the mid- and long-term environment appearance changes. Therefore, we focus our evaluation on the robustness of image registration to variable lighting and naturally-occurring seasonal changes. We combine detection and description components of different image extractors and evaluate their performance on five datasets collected by mobile vehicles in three different outdoor environments over the course of one year. Moreover, we propose a trainable feature descriptor based on a combination of evolutionary algorithms and Binary Robust Independent Elementary Features, which we call GRIEF (Generated BRIEF). In terms of robustness to seasonal changes, the most promising results were achieved by the SpG CNN and the STAR GRIEF feature, which was slightly less robust, but faster to calculate. We developed computational models to predict the emergence of depression and Post-Traumatic Stress Disorder in Twitter users. Twitter data and details of depression history were collected from 204 individuals (105 depressed, 99 healthy). We extracted predictive features measuring affect, linguistic style, and context from participant tweets (N = 279,951) and built models using these features with supervised learning algorithms. Resulting models successfully discriminated between depressed and healthy content, and compared favorably to general practitioners’ average success rates in diagnosing depression, albeit in a separate population. Results held even when the analysis was restricted to content posted before first depression diagnosis. State-space temporal analysis suggests that onset of depression may be detectable from Twitter data several months prior to diagnosis. Predictive results were replicated with a separate sample of individuals diagnosed with PTSD (Nusers = 174, Ntweets = 243,775). A state-space time series model revealed indicators of PTSD almost immediately post-trauma, often many months prior to clinical diagnosis. These methods suggest a data-driven, predictive approach for early screening and detection of mental illness. We live in a dynamic visual world where the appearance of scenes changes dramatically from hour to hour or season to season. In this work we study "transient scene attributes" -- high level properties which affect scene appearance, such as "snow", "autumn", "dusk", "fog". We define 40 transient attributes and use crowdsourcing to annotate thousands of images from 101 webcams. We use this "transient attribute database" to train regressors that can predict the presence of attributes in novel images. We demonstrate a photo organization method based on predicted attributes. Finally we propose a high-level image editing method which allows a user to adjust the attributes of a scene, e.g. change a scene to be "snowy" or "sunset". To support attribute manipulation we introduce a novel appearance transfer technique which is simple and fast yet competitive with the state-of-the-art. We show that we can convincingly modify many transient attributes in outdoor scenes. We examined whether press reports on the collective mood of investors can predict changes in stock prices. We collected data on the use of emotion words in newspaper reports on traders' affect, coded these emotion words according to their location on an affective circumplex in terms of pleasantness and activation level, and created indices of collective mood for each trading day. Then, by using time series analyses, we examined whether these mood indices, depicting investors' emotion on a given trading day, could predict the next day's opening price of the stock market. The strongest findings showed that activated pleasant mood predicted increases in NASDAQ prices, while activated unpleasant mood predicted decreases in NASDAQ prices. We conclude that both valence and activation levels of collective mood are important in predicting trend continuation in stock prices. The appearance of (outdoor) scenes changes considerably with the strength of certain transient attributes, such as "rainy", "dark" or "sunny". Obviously, this also affects the representation of an image in feature space, e.g., as activations at a certain CNN layer, and consequently impacts scene recognition performance. In this work, we investigate the variability in these transient attributes as a rich source of information for studying how image representations change as a function of attribute strength. In particular, we leverage a recently introduced dataset with fine-grain annotations to estimate feature trajectories for a collection of transient attributes and then show how these trajectories can be transferred to new image representations. This enables us to synthesize new data along the transferred trajectories with respect to the dimensions of the space spanned by the transient attributes. Applicability of this concept is demonstrated on the problem of oneshot recognition of scene locations. We show that data synthesized via feature trajectory transfer considerably boosts recognition performance, (1) with respect to baselines and (2) in combination with state-of-the-art approaches in oneshot learning.