scholarly journals Debates about vaccines and climate change on social media networks: a study in contrasts

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Justin Schonfeld ◽  
Edward Qian ◽  
Jason Sinn ◽  
Jeffrey Cheng ◽  
Madhur Anand ◽  
...  
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
Common Pool Resources ◽  
Machine Learning Algorithms ◽  
Community Networks ◽  
Human Populations ◽  
Human Environment ◽  
Social Media Networks ◽  
Twitter User ◽  
Isolated Communities

AbstractVaccines and climate change have much in common. In both cases, a scientific consensus contrasts with a divided public opinion. They also exemplify coupled human–environment systems involving common pool resources. Here we used machine learning algorithms to analyze the sentiment of 87 million tweets on climate change and vaccines in order to characterize Twitter user sentiment and the structure of user and community networks. We found that the vaccine conversation was characterized by much less interaction between individuals with differing sentiment toward vaccines. Community-level interactions followed this pattern, showing less interaction between communities of opposite sentiment toward vaccines. Additionally, vaccine community networks were more fragmented and exhibited numerous isolated communities of neutral sentiment. Finally, pro-vaccine individuals overwhelmingly believed in anthropogenic climate change, but the converse was not true. We propose mechanisms that might explain these results, pertaining to how the spatial scale of an environment system can structure human populations.

scholarly journals Debates about vaccines and climate change on social media networks: a study in contrasts

2020 ◽  
Author(s):  
Justin Schonfeld ◽  
Edward Qian ◽  
Jason Sinn ◽  
Jeffery Cheng ◽  
Madhur Anand ◽  
...  
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
Common Pool Resources ◽  
Machine Learning Algorithms ◽  
Community Networks ◽  
Human Populations ◽  
Human Environment ◽  
Social Media Networks ◽  
Twitter User ◽  
Isolated Communities

ABSTRACTVaccines and climate change have much in common. In both cases, a scientific consensus contrasts with a divided public opinion. They also exemplify coupled human-environment systems involving common pool resources. Here we used machine learning algorithms to analyze the sentiment of 87 million tweets on climate change and vaccines in order to characterize Twitter user sentiment and the structure of user and community networks. We found that the vaccine conversation was characterized by much less interaction between individuals with differing sentiment toward vaccines. Community-level interactions followed this pattern, showing less interaction between communities of opposite sentiment toward vaccines. Additionally, vaccine community networks were more fragmented and exhibited numerous isolated communities of neutral sentiment. Finally, pro-vaccine individuals overwhelmingly believed in anthropogenic climate change, but the converse was not true. We propose mechanisms that might explain these results, pertaining to how the spatial scale of an environment system can structure human populations.

scholarly journals Exploratory Analysis of Driving Force of Wildfires in Australia: An Application of Machine Learning within Google Earth Engine

2020 ◽  
Vol 13 (1) ◽  
pp. 10
Author(s):  
Andrea Sulova ◽  
Jamal Jokar Arsanjani
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
Random Forest ◽  
Google Earth ◽  
Summer Season ◽  
Driving Factors ◽  
Machine Learning Algorithms ◽  
Classification And Regression Tree ◽  
Training Dataset ◽  
Google Earth Engine

Recent studies have suggested that due to climate change, the number of wildfires across the globe have been increasing and continue to grow even more. The recent massive wildfires, which hit Australia during the 2019–2020 summer season, raised questions to what extent the risk of wildfires can be linked to various climate, environmental, topographical, and social factors and how to predict fire occurrences to take preventive measures. Hence, the main objective of this study was to develop an automatized and cloud-based workflow for generating a training dataset of fire events at a continental level using freely available remote sensing data with a reasonable computational expense for injecting into machine learning models. As a result, a data-driven model was set up in Google Earth Engine platform, which is publicly accessible and open for further adjustments. The training dataset was applied to different machine learning algorithms, i.e., Random Forest, Naïve Bayes, and Classification and Regression Tree. The findings show that Random Forest outperformed other algorithms and hence it was used further to explore the driving factors using variable importance analysis. The study indicates the probability of fire occurrences across Australia as well as identifies the potential driving factors of Australian wildfires for the 2019–2020 summer season. The methodical approach and achieved results and drawn conclusions can be of great importance to policymakers, environmentalists, and climate change researchers, among others.

The Emergence Of A Climate Anthropology In Northeast Brazil

2000 ◽  
Vol 22 (4) ◽  
pp. 6-10 ◽  
Author(s):  
Donald Nelson ◽  
Timothy Finan
Keyword(s):  
Climate Change ◽  
Social Science ◽  
Climate Variability ◽  
Global Climate ◽  
Empirical Studies ◽  
Natural Sciences ◽  
Human Populations ◽  
Theoretical Ground ◽  
Social Scientists ◽  
Human Environment

Climate studies have traditionally fallen within the purview of the natural sciences where cause and predictable pattern are sought for such phenomena as climate change and climate variability. In the past, social scientists had little occasion to cross disciplinary paths with atmospheric or oceanographic scientists. Not that social science has ignored climate, for anthropology and geography claim a rich literature on the impacts of climate variability, particularly drought, on human populations (e.g., Franke and Chasin 1980; Watts 1983; Langworthy and Finan 1997). New theoretical ground, fertilized by an increasing number of empirical studies, now promises to bear the fruit we call climate anthropology. The expanding social science agenda has responded to two relatively recent advances in the natural sciences. The first has been the widening scientific consensus regarding global climate change and its anthropogenic causes. Global change cannot be adequately characterized without understanding the human-environment interactions that have contributed to the phenomenon, forcing social and natural scientists to pursue common research objectives. The second influence on climate anthropology has been the improvement in scientific understanding of oceanic/atmospheric interactions, thus allowing for more refined predictability of climatic events, particularly extreme ones. It is with this advance in climate predictability that climate anthropology is beginning to reap an exceedingly bountiful harvest in both theory and application.

scholarly journals Modelling hydrological responses under climate change using machine learning algorithms – semi-arid river basin of peninsular India

2020 ◽  
Vol 3 (1) ◽  
pp. 481-498
Author(s):  
G. Sireesha Naidu ◽  
M. Pratik ◽  
S. Rehana
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
Random Forest ◽  
River Basin ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Peninsular India ◽  
Hydrological Responses ◽  
Semi Arid

Abstract Catchment scale conceptual hydrological models apply calibration parameters entirely based on observed historical data in the climate change impact assessment. The study used the most advanced machine learning algorithms based on Ensemble Regression and Random Forest models to develop dynamically calibrated factors which can form as a basis for the analysis of hydrological responses under climate change. The Random Forest algorithm was identified as a robust method to model the calibration factors with limited data for training and testing with precipitation, evapotranspiration and uncalibrated runoff based on various performance measures. The developed model was further used to study the runoff response under climate change variability of precipitation and temperatures. A statistical downscaling model based on K-means clustering, Classification and Regression Trees and Support Vector Regression was used to develop the precipitation and temperature projections based on MIROC GCM outputs with the RCP 4.5 scenario. The proposed modelling framework has been demonstrated on a semi-arid river basin of peninsular India, Krishna River Basin (KRB). The basin outlet runoff was predicted to decrease (13.26%) for future scenarios under climate change due to an increase in temperature (0.6 °C), compared to a precipitation increase (13.12%), resulting in an overall reduction in water availability over KRB.

scholarly journals Application of machine learning algorithms for flood susceptibility assessment and risk management

2021 ◽  
Author(s):  
R. Madhuri ◽  
S. Sistla ◽  
K. Srinivasa Raju
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
Influencing Factors ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Support Vector ◽  
Climate Change Scenarios ◽  
Adaptive Boosting ◽  
Extreme Gradient Boosting ◽  
Normalised Difference Vegetation Index

Abstract Assessing floods and their likely impact in climate change scenarios will enable the facilitation of sustainable management strategies. In this study, five machine learning (ML) algorithms, namely (i) Logistic Regression, (ii) Support Vector Machine, (iii) K-nearest neighbor, (iv) Adaptive Boosting (AdaBoost) and (v) Extreme Gradient Boosting (XGBoost), were tested for Greater Hyderabad Municipal Corporation (GHMC), India, to evaluate their clustering abilities to classify locations (flooded or non-flooded) for climate change scenarios. A geo-spatial database, with eight flood influencing factors, namely, rainfall, elevation, slope, distance from nearest stream, evapotranspiration, land surface temperature, normalised difference vegetation index and curve number, was developed for 2000, 2006 and 2016. XGBoost performed the best, with the highest mean area under curve score of 0.83. Hence, XGBoost was adopted to simulate the future flood locations corresponding to probable highest rainfall events under four Representative Concentration Pathways (RCPs), namely, 2.6, 4.5, 6.0 and 8.5 along with other flood influencing factors for 2040, 2056, 2050 and 2064, respectively. The resulting ranges of flood risk probabilities are predicted as 39–77%, 16–39%, 42–63% and 39–77% for the respective years.

scholarly journals A Systematic Review of Machine Learning Algorithms in Cyberbullying Detection: Future Directions and Challenges

2021 ◽  
Vol 4 (1) ◽  
pp. 01-26
Author(s):  
Muhammad Arif
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Social Media ◽  
Language Processing ◽  
Machine Learning Algorithms ◽  
Future Directions ◽  
Social Media Networks ◽  
Current State ◽  
Art Research ◽  
Cyberbullying Detection

Social media networks are becoming an essential part of life for most of the world’s population. Detecting cyberbullying using machine learning and natural language processing algorithms is getting the attention of researchers. There is a growing need for automatic detection and mitigation of cyberbullying events on social media. In this study, research directions and the theoretical foundation in this area are investigated. A systematic review of the current state-of-the-art research in this area is conducted. A framework considering all possible actors in the cyberbullying event must be designed, including various aspects of cyberbullying and its effect on the participating actors. Furthermore, future directions and challenges are also discussed.

scholarly journals Machine Learning for Projecting Extreme Precipitation Intensity for Short Durations in a Changing Climate

Geosciences ◽  
2019 ◽  
Vol 9 (5) ◽  
pp. 209
Author(s):  
Huiling Hu ◽  
Bilal M. Ayyub
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
Extreme Precipitation ◽  
Circulation Model ◽  
Machine Learning Algorithms ◽  
Precipitation Intensity ◽  
Potential Threat ◽  
Drainage Systems ◽  
Idf Curves ◽  
Intensity Duration Frequency

Climate change is one of the prominent factors that causes an increased severity of extreme precipitation which, in turn, has a huge impact on drainage systems by means of flooding. Intensity–duration–frequency (IDF) curves play an essential role in designing robust drainage systems against extreme precipitation. It is important to incorporate the potential threat from climate change into the computation of IDF curves. Most existing works that have achieved this goal were based on Generalized Extreme Value (GEV) analysis combined with various circulation model simulations. Inspired by recent works that used machine learning algorithms for spatial downscaling, this paper proposes an alternative method to perform projections of precipitation intensity over short durations using machine learning. The method is based on temporal downscaling, a downscaling procedure performed over the time scale instead of the spatial scale. The method is trained and validated using data from around two thousand stations in the US. Future projection of IDF curves is calculated and discussed.

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