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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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. 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. Scholars are increasingly citing electronic “web references” which are not preserved in libraries or full text archives. WebCite is a new standard for citing web references. To “webcite” a document involves archiving the cited Web page through www.webcitation.org and citing the WebCite permalink instead of (or in addition to) the unstable live Web page. This journal has amended its “instructions for authors” accordingly, asking authors to archive cited Web pages before submitting a manuscript. Almost 200 other journals are already using the system. We discuss the rationale for WebCite, its technology, and how scholars, editors, and publishers can benefit from the service. Citing scholars initiate an archiving process of all cited Web references, ideally before they submit a manuscript. Authors of online documents and websites which are expected to be cited by others can ensure that their work is permanently available by creating an archived copy using WebCite and providing the citation information including the WebCite link on their Web document(s). Editors should ask their authors to cache all cited Web addresses (Uniform Resource Locators, or URLs) “prospectively” before submitting their manuscripts to their journal. Editors and publishers should also instruct their copyeditors to cache cited Web material if the author has not done so already. Finally, WebCite can process publisher submitted “citing articles” (submitted for example as eXtensible Markup Language [XML] documents) to automatically archive all cited Web pages shortly before or on publication. Finally, WebCite can act as a focussed crawler, caching retrospectively references of already published articles. Copyright issues are addressed by honouring respective Internet standards (robot exclusion files, no-cache and no-archive tags). Long-term preservation is ensured by agreements with libraries and digital preservation organizations. The resulting WebCite Index may also have applications for research assessment exercises, being able to measure the impact of Web services and published Web documents through access and Web citation metrics. @PARASPLIT [J Med Internet Res 2005;7(5):e60] 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.