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.
In this section, we briefly go through the related work. Mood Weather Relationship in Psychology and Physiology It has long been believed that there should be some sort of correlation between weather and individual mood state. In psychological literature, researches on this topic can be traced back to early 1950s when Nowis @cite_29 tried to explore the external factors that affect individuals' emotions. While correlational analysis between mood and weather has been conducted in multiple psychological researches @cite_52 @cite_0 @cite_38 @cite_7 @cite_15 @cite_21 @cite_25 , conclusions are amazingly diverse. Low levels of humidity @cite_48 , high levels of sunlight @cite_58 @cite_45 @cite_30 , high barometric pressure @cite_38 and high temperatures @cite_58 @cite_37 have been associated with high mood, but high temperature has been associated with low mood in some other work @cite_38 @cite_7 . Meanwhile, some studies traced no direct correlations between mood and any type of weather variables @cite_52 @cite_25 : Watson and Lee @cite_25 traced the daily mood reports of 498 students in Dallas, Texas and found no significant correlations between mood (measured by self-report using the Positive and Negative Affect Scale) and any of the assessed weather variables, including sunshine, barometric pressure, temperature, and precipitation.
@cite_30 @cite_38 @cite_37 @cite_7 @cite_48 @cite_29 @cite_21 @cite_52 @cite_0 @cite_45 @cite_15 @cite_58 @cite_25
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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. 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. In a document network such as a citation network of scientific documents, web-logs, etc., the content produced by authors exhibits their interest in certain topics. In addition some authors influence other authors' interests. In this work, we propose to model the influence of cited authors along with the interests of citing authors. Moreover, we hypothesize that apart from the citations present in documents, the context surrounding the citation mention provides extra topical information about the cited authors. However, associating terms in the context to the cited authors remains an open problem. We propose novel document generation schemes that incorporate the context while simultaneously modeling the interests of citing authors and influence of the cited authors. Our experiments show significant improvements over baseline models for various evaluation criteria such as link prediction between document and cited author, and quantitatively explaining unseen text. A citing sentence is one that appears in a scientific article and cites previous work. Citing sentences have been studied and used in many applications. For example, they have been used in scientific paper summarization, automatic survey generation, paraphrase identification, and citation function classification. Citing sentences that cite multiple papers are common in scientific writing. This observation should be taken into consideration when using citing sentences in applications. For instance, when a citing sentence is used in a summary of a scientific paper, only the fragments of the sentence that are relevant to the summarized paper should be included in the summary. In this paper, we present and compare three different approaches for identifying the fragments of a citing sentence that are related to a given target reference. Our methods are: word classification, sequence labeling, and segment classification. Our experiments show that segment classification achieves the best results.