Weather impacts expressed sentiment

We conduct the largest ever investigation into the relationship between meteorological conditions and the sentiment of human expressions. To do this, we employ over three and a half billion social media posts from tens of millions of individuals from both Facebook and Twitter between 2009 and 2016. We find that cold temperatures, hot temperatures, precipitation, narrower daily temperature ranges, humidity, and cloud cover are all associated with worsened expressions of sentiment, even when excluding weather-related posts. We compare the magnitude of our estimates with the effect sizes associated with notable historical events occurring within our data.This work was supported by Ministerio de Economía y Competitividad: FIS2013-47532-C3-3-P, FIS2016-78904-C3-3-P (http://www.mineco.gob.es/); and National Science Foundation DGE0707423, TG-SES130013, 0903551 (https://www.nsf.gov/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Using social media to measure impacts of named storm events in the United Kingdom and Ireland

This is the final version. Available on open access from Wiley via the DOI in this recordDespite increasing use of impact-based weather warnings, the social impacts of extreme weather events lie beyond the reach of conventional meteorological observations and remain difficult to quantify. This presents a challenge for validation of warnings and weather impact models. This study considers the application of social sensing, the systematic analysis of unsolicited social media data to observe real-world events, to determine the impacts of named storms in the United Kingdom and Ireland during the winter storm season 2017–2018. User posts on Twitter are analysed to show that social sensing can robustly detect and locate storm events. Comprehensive filtering of tweets containing weather keywords reveals that ~3% of tweets are relevant to severe weather events and, for those, locations could be derived for about 75%. Impacts of storms on Twitter users are explored using the text content of storm-related tweets to assess changes in sentiment and topics of discussion over the period before, during and after each storm event. Sentiment shows a consistent response to storms, with an increase in expressed negative emotion. Topics of discussion move from warnings as the storm approaches, to local observations and reportage during the storm, to accounts of damage/disruption and sharing of news reports following the event. There is a high level of humour expressed throughout. This study demonstrates a novel methodology for identifying tweets which can be used to assess the impacts of storms and other extreme weather events. Further development could lead to improved understanding of social impacts of storms and impact model validation.Economic and Social Research Council (ESRC)Engineering and Physical Sciences Research Council (EPSRC)Natural Environment Research Council (NERC

A linguistically-driven methodology for detecting impending and unfolding emergencies from social media messages

Natural disasters have demonstrated the crucial role of social media before, during and after emergencies (Haddow & Haddow 2013). Within our EU project Sland \ub4 ail, we aim to ethically improve \ub4 the use of social media in enhancing the response of disaster-related agen-cies. To this end, we have collected corpora of social and formal media to study newsroom communication of emergency management organisations in English and Italian. Currently, emergency management agencies in English-speaking countries use social media in different measure and different degrees, whereas Italian National Protezione Civile only uses Twitter at the moment. Our method is developed with a view to identifying communicative strategies and detecting sentiment in order to distinguish warnings from actual disasters and major from minor disasters. Our linguistic analysis uses humans to classify alert/warning messages or emer-gency response and mitigation ones based on the terminology used and the sentiment expressed. Results of linguistic analysis are then used to train an application by tagging messages and detecting disaster- and/or emergency-related terminology and emotive language to simulate human rating and forward information to an emergency management system

Big Questions, Little Answers: Terrorism Activity, Investor Sentiment and Stock Returns

Motivated by the investor sentiment literature and assuming that terrorist activity influences investor mood the paper explores whether terrorism exerts a significant negative impact on daily stock market returns for a sample of 22 countries. The employed empirical specifications are based on flexible versions of the World CAPM allowing for autoregressive conditional heteroscedasticity. The results suggest that terrorist activity leads to significantly lower returns on the day of terrorist attack occurrence. In addition, the negative effect of terrorist activity is substantially amplified as the level of psychosocial effects increases. On the one hand this evidence sheds light to the underlying mechanism via which terrorism affects stock markets while on the other hand provides further empirical support for the sentiment effect.Sentiment, Terrorism, Stock Market, Panel, Pooled Panel ARCH

Developing drought resilience in irrigated agriculture in the face of increasing water scarcity

In many countries, drought is the natural hazard that causes the greatest agronomic impacts. After recurrent droughts, farmers typically learn from experience and implement changes in management to reduce their future drought risks and impacts. This paper aims to understand how irrigated agriculture in a humid climate has been affected by past droughts and how different actors have adapted their activities and strategies over time to increase their resilience. After examining recent drought episodes from an agroclimatic perspective, information from an online survey was combined with evidence from semi-structured interviews with farmers to assess: drought risk perceptions, impacts of past drought events, management strategies at different scales (regional to farm level) and responses to future risks. Interviews with the water regulatory agency were also conducted to explore their attitudes and decision-making processes during drought events. The results highlight how agricultural drought management strategies evolve over time, including how specific aspects have helped to reduce future drought risks. The importance of adopting a vertically integrated drought management approach in the farming sector coupled with a better understanding of past drought impacts and management options is shown to be crucial for improving decision-making during future drought events

Islamic Calendar Anomalies: Pakistani Practitioners' Perspective

Studies on Islamic calendar anomalies in financial markets tend to apply quantitative analysis to historic share prices. Surprisingly, there is a lack of research investigating whether the participants of such markets are aware of these anomalies and whether these anomalies affect their investment practice. Or is it a case that these practitioners are completely unaware of the anomalies present in these markets and are missing out on profitable opportunities? The purpose of this paper is to analyse the views of influential participants within the Pakistani stock market

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

Machine Learning-Based Models for Assessing Impacts Before, During and After Hurricane Events

Social media provides an abundant amount of real-time information that can be used before, during, and after extreme weather events. Government officials, emergency managers, and other decision makers can use social media data for decision-making, preparation, and assistance. Machine learning-based models can be used to analyze data collected from social media. Social media data and cloud cover temperature as physical sensor data was analyzed in this study using machine learning techniques. Data was collected from Twitter regarding Hurricane Florence from September 11, 2018 through September 20, 2018 and Hurricane Michael from October 1, 2018 through October 18, 2018. Natural language processing models were developed to demonstrate sentiment among the data. Forecasting models for future events were developed for better emergency management during extreme weather events. Relationships among data were explored using social media data and physical sensor data to analyze extreme weather events as these events become more prevalent in our lives. In this study, social media sentiment analysis was performed that can be used by emergency managers, government officials, and decision makers. Different machine learning algorithms and natural language processing techniques were used to examine sentiment classification. The approach is multi-modal, which will help stakeholders develop a more comprehensive understanding of the social impacts of a storm and how to help prepare for future storms. Of all the classification algorithms used in this study to analyze sentiment, the naive Bayes classifier displayed the highest accuracy for this data. The results demonstrate that machine learning and natural language processing techniques, using Twitter data, are a practical method for sentiment analysis. The data can be used for correlation analysis between social sentiment and physical data and can be used by decision makers for better emergency management decisions