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      Big Data Sensors of Organic Advocacy: The Case of Leonardo DiCaprio and Climate Change

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          Abstract

          The strategies that experts have used to share information about social causes have historically been top-down, meaning the most influential messages are believed to come from planned events and campaigns. However, more people are independently engaging with social causes today than ever before, in part because online platforms allow them to instantaneously seek, create, and share information. In some cases this “organic advocacy” may rival or even eclipse top-down strategies. Big data analytics make it possible to rapidly detect public engagement with social causes by analyzing the same platforms from which organic advocacy spreads. To demonstrate this claim we evaluated how Leonardo DiCaprio’s 2016 Oscar acceptance speech citing climate change motivated global English language news (Bloomberg Terminal news archives), social media (Twitter postings) and information seeking (Google searches) about climate change. Despite an insignificant increase in traditional news coverage (54%; 95%CI: -144 to 247), tweets including the terms “climate change” or “global warming” reached record highs, increasing 636% (95%CI: 573–699) with more than 250,000 tweets the day DiCaprio spoke. In practical terms the “DiCaprio effect” surpassed the daily average effect of the 2015 Conference of the Parties (COP) and the Earth Day effect by a factor of 3.2 and 5.3, respectively. At the same time, Google searches for “climate change” or “global warming” increased 261% (95%CI, 186–335) and 210% (95%CI 149–272) the day DiCaprio spoke and remained higher for 4 more days, representing 104,190 and 216,490 searches. This increase was 3.8 and 4.3 times larger than the increases observed during COP’s daily average or on Earth Day. Searches were closely linked to content from Dicaprio’s speech (e.g., “hottest year”), as unmentioned content did not have search increases (e.g., “electric car”). Because these data are freely available in real time our analytical strategy provides substantial lead time for experts to detect and participate in organic advocacy while an issue is salient. Our study demonstrates new opportunities to detect and aid agents of change and advances our understanding of communication in the 21st century media landscape.

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          Most cited references 29

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          A twenty-first century science.

           Giles Watts (2007)
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            Climate change: challenges and opportunities for global health.

            Health is inextricably linked to climate change. It is important for clinicians to understand this relationship in order to discuss associated health risks with their patients and to inform public policy. To provide new US-based temperature projections from downscaled climate modeling and to review recent studies on health risks related to climate change and the cobenefits of efforts to mitigate greenhouse gas emissions. We searched PubMed and Google Scholar from 2009 to 2014 for articles related to climate change and health, focused on governmental reports, predictive models, and empirical epidemiological studies. Of the more than 250 abstracts reviewed, 56 articles were selected. In addition, we analyzed climate data averaged over 13 climate models and based future projections on downscaled probability distributions of the daily maximum temperature for 2046-2065. We also compared maximum daily 8-hour average ozone with air temperature data taken from the National Oceanic and Atmospheric Administration, National Climate Data Center. By 2050, many US cities may experience more frequent extreme heat days. For example, New York and Milwaukee may have 3 times their current average number of days hotter than 32°C (90°F). High temperatures are also strongly associated with ozone exceedance days, for example, in Chicago, Illinois. The adverse health aspects related to climate change may include heat-related disorders, such as heat stress and economic consequences of reduced work capacity; respiratory disorders, including those exacerbated by air pollution and aeroallergens, such as asthma; infectious diseases, including vectorborne diseases and waterborne diseases, such as childhood gastrointestinal diseases; food insecurity, including reduced crop yields and an increase in plant diseases; and mental health disorders, such as posttraumatic stress disorder and depression, that are associated with natural disasters. Substantial health and economic cobenefits could be associated with reductions in fossil fuel combustion. For example, greenhouse gas emission policies may yield net economic benefit, with health benefits from air quality improvements potentially offsetting the cost of US and international carbon policies. Evidence over the past 20 years indicates that climate change can be associated with adverse health outcomes. Health care professionals have an important role in understanding and communicating the related potential health concerns and the cobenefits from policies to reduce greenhouse gas emissions.
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              What can digital disease detection learn from (an external revision to) Google Flu Trends?

              Google Flu Trends (GFT) claimed to generate real-time, valid predictions of population influenza-like illness (ILI) using search queries, heralding acclaim and replication across public health. However, recent studies have questioned the validity of GFT.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                2 August 2016
                2016
                : 11
                : 8
                Affiliations
                [1 ]University of California San Diego School of Medicine, San Diego, CA, United States of America
                [2 ]Institute for Disease Modeling, Bellevue, WA, United States of America
                [3 ]University of Washington, Seattle, WA, United States of America
                [4 ]Santa Fe Institute, Santa Fe, NM, United States of America
                [5 ]New Mexico State University, Las Cruces, NM, United States of America
                [6 ]Human Language Technology Center of Excellence, Johns Hopkins University, Baltimore, MD, United States of America
                [7 ]Bloomberg LP, New York, New York, United States of America
                [8 ]Scripps Institution of Oceanography, University of California San Diego, San Diego, CA, United States of America
                [9 ]Department of Political Science, University of California San Diego, San Diego, CA, United States of America
                [10 ]School of Media and Journalism, University of North Carolina, Chapel Hill, NC, United States of America
                [11 ]Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC, United States of America
                [12 ]Keck School of Medicine, University of Southern California, Los Angeles, CA, United States of America
                [13 ]Graduate School of Public Health, San Diego State University, San Diego, CA, United States of America
                New York City Department of Health and Mental Hygiene, UNITED STATES
                Author notes

                Competing Interests: Mark Dredze is commercially employed by Bloomberg LP. There are no other relevant competing interests. This does not alter the authors' adherence to PLOS ONE policies on sharing data and materials.

                • Conceived and designed the experiments: ECL JWA.

                • Analyzed the data: ECL BMA JWA.

                • Contributed reagents/materials/analysis tools: ECL BMA MD NO JHF SMN JPA JWA.

                • Wrote the paper: ECL BMA MD NO JHF SMN JPA JWA.

                Article
                PONE-D-16-18178
                10.1371/journal.pone.0159885
                4970768
                27482907
                © 2016 Leas et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                Page count
                Figures: 2, Tables: 0, Pages: 9
                Product
                Funding
                The authors received no specific funding for this work. Bloomberg LP provided support in the form of salaries for authors [MD], but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Earth Sciences
                Atmospheric Science
                Climatology
                Climate Change
                Social Sciences
                Sociology
                Communications
                Social Communication
                Social Media
                Twitter
                Computer and Information Sciences
                Network Analysis
                Social Networks
                Social Media
                Twitter
                Social Sciences
                Sociology
                Social Networks
                Social Media
                Twitter
                Social Sciences
                Linguistics
                Speech
                Earth Sciences
                Atmospheric Science
                Climatology
                Climate Change
                Global Warming
                Social Sciences
                Sociology
                Communications
                Social Communication
                Social Media
                Computer and Information Sciences
                Network Analysis
                Social Networks
                Social Media
                Social Sciences
                Sociology
                Social Networks
                Social Media
                Social Sciences
                Sociology
                Communications
                Social Communication
                People and Places
                Population Groupings
                Age Groups
                Children
                People and Places
                Population Groupings
                Families
                Children
                Physical Sciences
                Mathematics
                Probability Theory
                Probability Distribution
                Normal Distribution
                Custom metadata
                All data are publicly available. News coverage data can be accessed using the Bloomberg Terminal {NT GO} function ( bloomberg.com/professional), Twitter postings can be accessed using the public API ( dev.twitter.com/streaming/overview), and Google searches can be downloaded from Google Trends ( google.com/trends). Please contact the corresponding author if further assistance is required.

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