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Abstract
Hazardous weather conditions can pose a threat to the functioning of transportation systems. While the impacts of extreme weather events (e.g., hurricanes/tornadoes and flooding) on transportation disruptions have received significant attention, minor transient disturbances in traffic and transport systems due to rainfall events have remained understudied. Given that a road network experiences rainfall events on a regular basis, which in turn likely reduces its efficiency through short-term disruptions, it is imperative to assess the influence of variations in rainfall intensity on the traffic speed. By synergistically using crowdsourced probe vehicle speed data and spatially explicit meteorological data, this study quantifies the sensitivity of traffic speed to rainfall events of different intensities over 1151 road sections within Alabama. It is observed that instead of variations in the rainfall intensity, traffic speed sensitivity is primarily influenced by a road section’s free-flow speed (uninterrupted speed during dry pavement conditions) and antecedent traffic volume. Relative sensitivity of road sections exhibits high consistency over different rainfall intensities across all road sections, thus underscoring the possibility of assessing sensitivities based only on speed data collected during rainfall intensities that are much more frequent. These results may be used to identify road sections and time periods with high sensitivity to rainfall, thus helping in prioritization of mitigation measures.
Significance Statement
To safeguard against hazardous driving conditions during rainfall events, from either compromised visibility or reduced friction between tires and pavement, drivers often reduce vehicle speed. However, the influence of rainfall intensity on traffic speed reduction remains unclear. This study analyzes the sensitivity of traffic speed to rainfall intensity. Our results indicate that, while rainfall indeed leads to traffic speed reductions, the extent of reduction is predominantly influenced by free-flow speed (uninterrupted vehicle speed) of the road section and the traffic volume on it instead of the rainfall intensity. These results may be used to identify high-sensitivity time periods and locations and guide prioritization of mitigation measures.
Abstract
Hazardous weather conditions can pose a threat to the functioning of transportation systems. While the impacts of extreme weather events (e.g., hurricanes/tornadoes and flooding) on transportation disruptions have received significant attention, minor transient disturbances in traffic and transport systems due to rainfall events have remained understudied. Given that a road network experiences rainfall events on a regular basis, which in turn likely reduces its efficiency through short-term disruptions, it is imperative to assess the influence of variations in rainfall intensity on the traffic speed. By synergistically using crowdsourced probe vehicle speed data and spatially explicit meteorological data, this study quantifies the sensitivity of traffic speed to rainfall events of different intensities over 1151 road sections within Alabama. It is observed that instead of variations in the rainfall intensity, traffic speed sensitivity is primarily influenced by a road section’s free-flow speed (uninterrupted speed during dry pavement conditions) and antecedent traffic volume. Relative sensitivity of road sections exhibits high consistency over different rainfall intensities across all road sections, thus underscoring the possibility of assessing sensitivities based only on speed data collected during rainfall intensities that are much more frequent. These results may be used to identify road sections and time periods with high sensitivity to rainfall, thus helping in prioritization of mitigation measures.
Significance Statement
To safeguard against hazardous driving conditions during rainfall events, from either compromised visibility or reduced friction between tires and pavement, drivers often reduce vehicle speed. However, the influence of rainfall intensity on traffic speed reduction remains unclear. This study analyzes the sensitivity of traffic speed to rainfall intensity. Our results indicate that, while rainfall indeed leads to traffic speed reductions, the extent of reduction is predominantly influenced by free-flow speed (uninterrupted vehicle speed) of the road section and the traffic volume on it instead of the rainfall intensity. These results may be used to identify high-sensitivity time periods and locations and guide prioritization of mitigation measures.
Abstract
This research examines tornadoes and their fatalities by light condition (i.e., daytime and nighttime) for the United States. The study has two primary objectives: 1) to catalog and reassess differences in daytime and nighttime, or nocturnal, tornadoes and their fatalities from spatial and temporal perspectives and 2) to employ a spatially explicit Monte Carlo simulation technique to calculate differences in daytime and nocturnal tornado–population impact potential by combining climatological tornado risk data with fine-scale, gridded estimates of day and night population density. Results reveal that nocturnal tornadoes remain a substantial impediment to mitigating tornado casualties despite long-term improvements in detection and warning of these events. Nocturnal tornadoes are nearly 2 times as deadly as daytime events, with fatalities stemming from overnight (i.e., from local midnight to sunrise) tornadoes increasing fourfold since the late nineteenth century. The proportion of all tornado fatalities that occurred during daytime hours has decreased 20% over the last 140 years while the nocturnal fatality proportion has increased 20%. The stall, or even slight growth, in U.S. tornado mortality rates over the last 30 years has, at least in part, been driven by increasing nocturnal tornado fatalities. Overall, nocturnal tornadoes affect 13% more people on average than daytime tornadoes, revealing the importance of time of day in mitigating tornado–population impacts and disasters. Emergency managers, forecasters, first responders, policy makers, and researchers should continue to focus efforts on understanding nocturnal tornadoes, especially with regard to how populations receive warnings and respond to these nocturnal threats.
Abstract
This research examines tornadoes and their fatalities by light condition (i.e., daytime and nighttime) for the United States. The study has two primary objectives: 1) to catalog and reassess differences in daytime and nighttime, or nocturnal, tornadoes and their fatalities from spatial and temporal perspectives and 2) to employ a spatially explicit Monte Carlo simulation technique to calculate differences in daytime and nocturnal tornado–population impact potential by combining climatological tornado risk data with fine-scale, gridded estimates of day and night population density. Results reveal that nocturnal tornadoes remain a substantial impediment to mitigating tornado casualties despite long-term improvements in detection and warning of these events. Nocturnal tornadoes are nearly 2 times as deadly as daytime events, with fatalities stemming from overnight (i.e., from local midnight to sunrise) tornadoes increasing fourfold since the late nineteenth century. The proportion of all tornado fatalities that occurred during daytime hours has decreased 20% over the last 140 years while the nocturnal fatality proportion has increased 20%. The stall, or even slight growth, in U.S. tornado mortality rates over the last 30 years has, at least in part, been driven by increasing nocturnal tornado fatalities. Overall, nocturnal tornadoes affect 13% more people on average than daytime tornadoes, revealing the importance of time of day in mitigating tornado–population impacts and disasters. Emergency managers, forecasters, first responders, policy makers, and researchers should continue to focus efforts on understanding nocturnal tornadoes, especially with regard to how populations receive warnings and respond to these nocturnal threats.
Abstract
Evidence exists that exposure to weather hazards, particularly in cities subject to heat island and climate change impacts, strongly affects individuals’ physical and mental health. Personal exposure to and sentiments about warm conditions can currently be expressed on social media, and recent research noted that the geotagged, time-stamped, and accessible social media databases can potentially be indicative of the public mood and health for a region. This study attempts to understand the relationships between weather and social media sentiments via Twitter and weather data from 2012 to 2019 for two cities in hot climates: Singapore and Phoenix, Arizona. We first detected weather-related tweets, and subsequently extracted keywords describing weather sensations. Furthermore, we analyzed frequencies of most used words describing weather sensations and created graphs of commonly occurring bigrams to understand connections between them. We further explored the annual trends between keywords describing heat and heat-related thermal discomfort and temperature profiles for two cities. Results showed significant relationships between frequency of heat-related tweets and temperature. For Twitter users exposed to no strong temperature seasonality, we noticed an overall negative cluster around hot sensations. Seasonal variability was more apparent in Phoenix, with more positive weather-related sentiments during the cooler months. This demonstrates the viability of Twitter data as a rapid indicator for periods of higher heat experienced by public and greater negative sentiment toward the weather, and its potential for effective tracking of real-time urban heat stress.
Significance Statement
Social media such as Twitter allow individuals to broadcast their opinions in real time, including perceptions and sensations related to weather events. Evidence from two cities exposed to hot weather—one equatorial and one desert subtropical—indicates that tweets were sensitive to seasonal temperature differences even within a small range. For Twitter users exposed to no strong temperature seasonality, generally negative sentiments to hot weather were seen year-round. In Phoenix with more pronounced seasonality, tweets were more positive in sentiment during the cooler months. This result shows promise for the medium as a rapid real-time indicator—or a snapshot—for societal sentiment to weather events.
Abstract
Evidence exists that exposure to weather hazards, particularly in cities subject to heat island and climate change impacts, strongly affects individuals’ physical and mental health. Personal exposure to and sentiments about warm conditions can currently be expressed on social media, and recent research noted that the geotagged, time-stamped, and accessible social media databases can potentially be indicative of the public mood and health for a region. This study attempts to understand the relationships between weather and social media sentiments via Twitter and weather data from 2012 to 2019 for two cities in hot climates: Singapore and Phoenix, Arizona. We first detected weather-related tweets, and subsequently extracted keywords describing weather sensations. Furthermore, we analyzed frequencies of most used words describing weather sensations and created graphs of commonly occurring bigrams to understand connections between them. We further explored the annual trends between keywords describing heat and heat-related thermal discomfort and temperature profiles for two cities. Results showed significant relationships between frequency of heat-related tweets and temperature. For Twitter users exposed to no strong temperature seasonality, we noticed an overall negative cluster around hot sensations. Seasonal variability was more apparent in Phoenix, with more positive weather-related sentiments during the cooler months. This demonstrates the viability of Twitter data as a rapid indicator for periods of higher heat experienced by public and greater negative sentiment toward the weather, and its potential for effective tracking of real-time urban heat stress.
Significance Statement
Social media such as Twitter allow individuals to broadcast their opinions in real time, including perceptions and sensations related to weather events. Evidence from two cities exposed to hot weather—one equatorial and one desert subtropical—indicates that tweets were sensitive to seasonal temperature differences even within a small range. For Twitter users exposed to no strong temperature seasonality, generally negative sentiments to hot weather were seen year-round. In Phoenix with more pronounced seasonality, tweets were more positive in sentiment during the cooler months. This result shows promise for the medium as a rapid real-time indicator—or a snapshot—for societal sentiment to weather events.
Abstract
Extreme heat events are one of the deadliest weather-related hazards in the United States and are increasing in frequency and severity as a result of anthropogenic greenhouse gas emissions. Further, some subpopulations may be more vulnerable than others because of social, economic, and political factors that create disparities in hazard impacts and responses. Vulnerability is also affected by risk perceptions, which can influence protective behaviors. In this study, we use national survey data to investigate the association of key sociodemographic factors with public risk perceptions of heatwaves. We find that risk perceptions are most associated with income, race/ethnicity, gender, and disability status. Age, an important predictor of heat mortality, had smaller associations with heat risk perceptions. Low-income, nonwhite, and disabled individuals tend to perceive themselves to be at greater risks from heatwaves than other subpopulations, corresponding to their elevated risk. Men have lower risk perceptions than women despite their higher mortality and morbidity from heat. This study helps to identify subpopulations in the United States who see themselves as at risk from extreme heat and can inform heat risk communication and other risk reduction practices.
Abstract
Extreme heat events are one of the deadliest weather-related hazards in the United States and are increasing in frequency and severity as a result of anthropogenic greenhouse gas emissions. Further, some subpopulations may be more vulnerable than others because of social, economic, and political factors that create disparities in hazard impacts and responses. Vulnerability is also affected by risk perceptions, which can influence protective behaviors. In this study, we use national survey data to investigate the association of key sociodemographic factors with public risk perceptions of heatwaves. We find that risk perceptions are most associated with income, race/ethnicity, gender, and disability status. Age, an important predictor of heat mortality, had smaller associations with heat risk perceptions. Low-income, nonwhite, and disabled individuals tend to perceive themselves to be at greater risks from heatwaves than other subpopulations, corresponding to their elevated risk. Men have lower risk perceptions than women despite their higher mortality and morbidity from heat. This study helps to identify subpopulations in the United States who see themselves as at risk from extreme heat and can inform heat risk communication and other risk reduction practices.
Abstract
Although many studies have linked complex social processes with climate change, few have examined the connections between changes in environmental factors, resources, or energy and the evolution of civilizations on the Tibetan Plateau. The Chiefdom of Lijiang was a powerful chiefdom located on the eastern Tibetan Plateau during the Ming Dynasty; it began expanding after the 1460s. Although many studies have analyzed the political and economic motivations responsible for this expansion, no high-resolution climate records representing this period of the Chiefdom of Lijiang were available until now. Here, we obtain a 621-yr reconstruction of the April–July normalized difference vegetation index (NDVI) values derived from moisture-sensitive tree rings from the eastern Tibetan Plateau. Our NDVI reconstruction accounts for 40.4% of the variability in instrumentally measured NDVI values and can effectively represent the historical changes in regional vegetation productivity that occurred on the eastern Tibetan Plateau. In combination with a reconstruction of summer temperatures on the eastern Tibetan Plateau, these results reveal that the regional climate was relatively warm and persistently wet during the period 1466–1630. This period was characterized by long periods of above-mean vegetation productivity on the eastern Tibetan Plateau that coincided with the expansion of the Chiefdom of Lijiang. We therefore propose that the NDVI anomaly and associated favorable political environment may have affected the expansion of the Chiefdom of Lijiang. Instrumental climate data and tree rings also reveal that the early twenty-first-century drought on the eastern Tibetan Plateau was the hottest drought recorded over the past six centuries, in accordance with projections of warming over the Tibetan Plateau. Future climate warming may lead to the occurrence of similar droughts, with potentially severe consequences for modern Asia.
Abstract
Although many studies have linked complex social processes with climate change, few have examined the connections between changes in environmental factors, resources, or energy and the evolution of civilizations on the Tibetan Plateau. The Chiefdom of Lijiang was a powerful chiefdom located on the eastern Tibetan Plateau during the Ming Dynasty; it began expanding after the 1460s. Although many studies have analyzed the political and economic motivations responsible for this expansion, no high-resolution climate records representing this period of the Chiefdom of Lijiang were available until now. Here, we obtain a 621-yr reconstruction of the April–July normalized difference vegetation index (NDVI) values derived from moisture-sensitive tree rings from the eastern Tibetan Plateau. Our NDVI reconstruction accounts for 40.4% of the variability in instrumentally measured NDVI values and can effectively represent the historical changes in regional vegetation productivity that occurred on the eastern Tibetan Plateau. In combination with a reconstruction of summer temperatures on the eastern Tibetan Plateau, these results reveal that the regional climate was relatively warm and persistently wet during the period 1466–1630. This period was characterized by long periods of above-mean vegetation productivity on the eastern Tibetan Plateau that coincided with the expansion of the Chiefdom of Lijiang. We therefore propose that the NDVI anomaly and associated favorable political environment may have affected the expansion of the Chiefdom of Lijiang. Instrumental climate data and tree rings also reveal that the early twenty-first-century drought on the eastern Tibetan Plateau was the hottest drought recorded over the past six centuries, in accordance with projections of warming over the Tibetan Plateau. Future climate warming may lead to the occurrence of similar droughts, with potentially severe consequences for modern Asia.
Abstract
Density altitude (DA) is an aviation parameter that helps determine specific aircraft performance characteristics for the expected atmospheric conditions. However, there are currently no detailed graphical tools for general aviation (GA) pilot education demonstrating the spatial and temporal variation of DA to help improve situational awareness. In this study, the fifth-generation European Centre for Medium-Range Weather Forecasts atmospheric reanalysis of the global climate (ERA5) dataset is used to construct a 30-yr monthly climatology of DA for the conterminous United States. Several DA characteristics are also investigated, including the effect of humidity on DA, the determination of reasonable worst-case conditions, and the applicability of two DA rules of thumb (ROTs). Maximum values of DA (worst aircraft performance) occur during July, reaching 3600 m over areas with high surface elevations. Humidity, while tertiary to the effects of temperature and pressure, causes the DA to increase from their dry values by more than 140 m as far north as the U.S.-Canada border. The dry DA ROT performs well for all conditions outside of strong tropical cyclones, where GA flights would not be expected. The ROT to correct for the effects of humidity performs well except in high elevations or when the dewpoint temperatures fall outside the applicable range of ≥5°C. When applied outside this range, in some situations, DA errors can be greater than if no humidity correction were applied. Therefore, a new ROT to correct for humidity is introduced here that extends the applicable dewpoint temperature range to ≥−28°C and reduces errors in estimated DA.
Significance Statement
The impacts of density altitude on aircraft performance have led to numerous general aviation (GA) accidents. This study helps GA pilots better understand the spatial and temporal variability in density altitude, thereby increasing their situational awareness during flight planning. This study also evaluates commonly used approximations to estimate density altitude, so pilots can understand the situations where these approximations are (in)applicable. Results suggest the need for a humidity correction approximation when dewpoint temperatures are <5°C, which is introduced in this study.
Abstract
Density altitude (DA) is an aviation parameter that helps determine specific aircraft performance characteristics for the expected atmospheric conditions. However, there are currently no detailed graphical tools for general aviation (GA) pilot education demonstrating the spatial and temporal variation of DA to help improve situational awareness. In this study, the fifth-generation European Centre for Medium-Range Weather Forecasts atmospheric reanalysis of the global climate (ERA5) dataset is used to construct a 30-yr monthly climatology of DA for the conterminous United States. Several DA characteristics are also investigated, including the effect of humidity on DA, the determination of reasonable worst-case conditions, and the applicability of two DA rules of thumb (ROTs). Maximum values of DA (worst aircraft performance) occur during July, reaching 3600 m over areas with high surface elevations. Humidity, while tertiary to the effects of temperature and pressure, causes the DA to increase from their dry values by more than 140 m as far north as the U.S.-Canada border. The dry DA ROT performs well for all conditions outside of strong tropical cyclones, where GA flights would not be expected. The ROT to correct for the effects of humidity performs well except in high elevations or when the dewpoint temperatures fall outside the applicable range of ≥5°C. When applied outside this range, in some situations, DA errors can be greater than if no humidity correction were applied. Therefore, a new ROT to correct for humidity is introduced here that extends the applicable dewpoint temperature range to ≥−28°C and reduces errors in estimated DA.
Significance Statement
The impacts of density altitude on aircraft performance have led to numerous general aviation (GA) accidents. This study helps GA pilots better understand the spatial and temporal variability in density altitude, thereby increasing their situational awareness during flight planning. This study also evaluates commonly used approximations to estimate density altitude, so pilots can understand the situations where these approximations are (in)applicable. Results suggest the need for a humidity correction approximation when dewpoint temperatures are <5°C, which is introduced in this study.
Abstract
The winter season in many U.S. states includes snowfall, and with it comes comments about how drivers always seem to “forget” how to drive in snow when the first snowfall of the season occurs. This study assesses the accuracy of this popular sentiment during Indiana winters from 2007 to 2020. The number of motor vehicle crashes, injuries, and fatalities during the first snowfall of the season was compared with those during subsequent snow events. A grid of 46 cells was constructed to subdivide the state, and instances of snowfall and crashes were aggregated within each cell each day during the study period. Daily crash, injury, and fatality totals in each cell were normalized by their respective means and standard deviations, allowing for data from all cells to be combined into a single dataset. Four snow accumulation thresholds were examined: 1, 13, 25, and 51 mm. Distributions at each threshold show that more crashes occur on average on days with the first snowfall of the winter season than on other days with snowfall, regardless of the accumulation threshold used. Statistical tests support this result, showing significant differences between the mean numbers of crashes at each of the four snowfall thresholds. There were also significantly more injuries on the first snowfall day and more fatalities, although fatalities were only significant for the 13-mm snowfall threshold.
Significance Statement
The purpose of my research is to answer the question: are there more motor vehicle crashes on the first day with snow each winter when compared with the number of crashes on other days with snowfall in the state of Indiana? Using four snowfall thresholds of increasing amounts, statistical tests comparing daily crashes on first snowfall and other snowfall days showed that there were significantly more crashes on average on the first day with snowfall each winter, regardless of the amount of snow accumulation. This supports the popular notion that crashes occur more frequently the first time it snows each year, although it is more likely attributed to drivers reacclimating to snowy road conditions than to forgetfulness.
Abstract
The winter season in many U.S. states includes snowfall, and with it comes comments about how drivers always seem to “forget” how to drive in snow when the first snowfall of the season occurs. This study assesses the accuracy of this popular sentiment during Indiana winters from 2007 to 2020. The number of motor vehicle crashes, injuries, and fatalities during the first snowfall of the season was compared with those during subsequent snow events. A grid of 46 cells was constructed to subdivide the state, and instances of snowfall and crashes were aggregated within each cell each day during the study period. Daily crash, injury, and fatality totals in each cell were normalized by their respective means and standard deviations, allowing for data from all cells to be combined into a single dataset. Four snow accumulation thresholds were examined: 1, 13, 25, and 51 mm. Distributions at each threshold show that more crashes occur on average on days with the first snowfall of the winter season than on other days with snowfall, regardless of the accumulation threshold used. Statistical tests support this result, showing significant differences between the mean numbers of crashes at each of the four snowfall thresholds. There were also significantly more injuries on the first snowfall day and more fatalities, although fatalities were only significant for the 13-mm snowfall threshold.
Significance Statement
The purpose of my research is to answer the question: are there more motor vehicle crashes on the first day with snow each winter when compared with the number of crashes on other days with snowfall in the state of Indiana? Using four snowfall thresholds of increasing amounts, statistical tests comparing daily crashes on first snowfall and other snowfall days showed that there were significantly more crashes on average on the first day with snowfall each winter, regardless of the amount of snow accumulation. This supports the popular notion that crashes occur more frequently the first time it snows each year, although it is more likely attributed to drivers reacclimating to snowy road conditions than to forgetfulness.
Abstract
Climate change is threatening forest ecosystem services, but people who manage their own forestland are in a unique position to observe these threats and take steps to reduce their impacts, especially if they believe that climate change is a contributing factor. We investigate the nature of the relationship between small woodland owner experiences of drought and severe storms and climate change belief in the upper midwestern United States using survey data and structural equation modeling. We find for both events that experience has a modest, positive effect on climate change belief, but only indirectly through perceptions of changing trends in these types of events. In addition, we find that trend perception and climate change belief have an important reciprocal relationship. Our findings suggest that experience as well as cognitive biases are related to believing in climate change, and that greater attention should be given to the potential of bidirectional relationships between key concepts related to climate change belief.
Significance Statement
Belief in climate change increases the likelihood of supporting and participating in climate change mitigation actions. We wanted to better understand the relationships between experiencing severe weather events, believing in global climate change, and noticing changes in the local patterns of severe weather events. Using data from a survey of individual and family forestland owners, also known as small woodland owners, in the upper Midwest, we found that severe weather experience increases climate change belief by increasing the perception that severe weather event trends are changing. The nature of this relationship is also important for informing how future analyses are constructed to avoid misleading findings that overestimate the influence that severe weather experience has on climate change belief.
Abstract
Climate change is threatening forest ecosystem services, but people who manage their own forestland are in a unique position to observe these threats and take steps to reduce their impacts, especially if they believe that climate change is a contributing factor. We investigate the nature of the relationship between small woodland owner experiences of drought and severe storms and climate change belief in the upper midwestern United States using survey data and structural equation modeling. We find for both events that experience has a modest, positive effect on climate change belief, but only indirectly through perceptions of changing trends in these types of events. In addition, we find that trend perception and climate change belief have an important reciprocal relationship. Our findings suggest that experience as well as cognitive biases are related to believing in climate change, and that greater attention should be given to the potential of bidirectional relationships between key concepts related to climate change belief.
Significance Statement
Belief in climate change increases the likelihood of supporting and participating in climate change mitigation actions. We wanted to better understand the relationships between experiencing severe weather events, believing in global climate change, and noticing changes in the local patterns of severe weather events. Using data from a survey of individual and family forestland owners, also known as small woodland owners, in the upper Midwest, we found that severe weather experience increases climate change belief by increasing the perception that severe weather event trends are changing. The nature of this relationship is also important for informing how future analyses are constructed to avoid misleading findings that overestimate the influence that severe weather experience has on climate change belief.
Abstract
Climate change (CC) is a topical issue of profound social interest. This paper aims to analyze the sentiments expressed in Twitter interactions in relation to CC. The study is performed considering the geographical and gender perspectives as well as different user typologies (individual users or companies). A total of 92 474 Twitter messages were utilized for the study. These are characterized by analyzing sentiment polarity and identifying the underlying topics related to climate change. Polarity is examined utilizing different commercial algorithms such as Valence Aware Dictionary and Sentiment Reasoner (VADER) and TextBlob, in conjunction with a procedure that uses word embedding and clustering techniques in an unsupervised machine learning approach. In addition, hypothesis testing is applied to inspect whether a gender independence exists or not. The topics are identified using latent Dirichlet allocation (LDA) and the usage of n-grams is explored. The topics identified are (in descending order of importance) CC activism, biodiversity, CC evidence, sustainability, CC awareness, pandemic, net zero, CC policies and finances, government action, and climate emergency. Moreover, globally speaking, it is found that the interactions on all topics are predominantly negative, and they are maintained as such for both men and women. If the polarity by topic and country is considered, it is also negative in most countries, although there are several notable exceptions. Finally, the presence of organizations and their perspective is studied, and results suggest that organizations post with more frequency when addressing topics such as sustainability, CC awareness, and net zero topics.
Significance Statement
The purpose of this research is to gain a better understanding of the perception of Twitter users in relation to climate change. To do so, Twitter interactions are characterized by analyzing polarity (positive or negative sentiment) and identifying underlying topics that, with greater or lesser intensity, were discussed during the period analyzed. Then, to contextualize the information retrieved, several classifications are performed: by gender, location, and account typology (individual users and companies). Interesting differences and commonalities are found both by geographic dimension and by gender. Similarly, some dissimilarities exist between interactions from individuals and companies. The findings of this work are significant because they can help institutions and governments to properly target public awareness efforts on climate change.
Abstract
Climate change (CC) is a topical issue of profound social interest. This paper aims to analyze the sentiments expressed in Twitter interactions in relation to CC. The study is performed considering the geographical and gender perspectives as well as different user typologies (individual users or companies). A total of 92 474 Twitter messages were utilized for the study. These are characterized by analyzing sentiment polarity and identifying the underlying topics related to climate change. Polarity is examined utilizing different commercial algorithms such as Valence Aware Dictionary and Sentiment Reasoner (VADER) and TextBlob, in conjunction with a procedure that uses word embedding and clustering techniques in an unsupervised machine learning approach. In addition, hypothesis testing is applied to inspect whether a gender independence exists or not. The topics are identified using latent Dirichlet allocation (LDA) and the usage of n-grams is explored. The topics identified are (in descending order of importance) CC activism, biodiversity, CC evidence, sustainability, CC awareness, pandemic, net zero, CC policies and finances, government action, and climate emergency. Moreover, globally speaking, it is found that the interactions on all topics are predominantly negative, and they are maintained as such for both men and women. If the polarity by topic and country is considered, it is also negative in most countries, although there are several notable exceptions. Finally, the presence of organizations and their perspective is studied, and results suggest that organizations post with more frequency when addressing topics such as sustainability, CC awareness, and net zero topics.
Significance Statement
The purpose of this research is to gain a better understanding of the perception of Twitter users in relation to climate change. To do so, Twitter interactions are characterized by analyzing polarity (positive or negative sentiment) and identifying underlying topics that, with greater or lesser intensity, were discussed during the period analyzed. Then, to contextualize the information retrieved, several classifications are performed: by gender, location, and account typology (individual users and companies). Interesting differences and commonalities are found both by geographic dimension and by gender. Similarly, some dissimilarities exist between interactions from individuals and companies. The findings of this work are significant because they can help institutions and governments to properly target public awareness efforts on climate change.
