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      The potential use of social media and other internet-related data and communications for child maltreatment surveillance and epidemiological research: Scoping review and recommendations

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          Abstract

          Collecting child maltreatment data is a complicated undertaking for many reasons. As a result, there is an interest by child maltreatment researchers to develop methodologies that allow for the triangulation of data sources. To better understand how social media and internet-based technologies could contribute to these approaches, we conducted a scoping review to provide an overview of social media and internet-based methodologies for health research, to report results of evaluation and validation research on these methods, and to highlight studies with potential relevance to child maltreatment research and surveillance. Many approaches were identified in the broad health literature; however, there has been limited application of these approaches to child maltreatment. The most common use was recruiting participants or engaging existing participants using online methods. From the broad health literature, social media and internet-based approaches to surveillance and epidemiologic research appear promising. Many of the approaches are relatively low cost and easy to implement without extensive infrastructure, but there are also a range of limitations for each method. Several methods have a mixed record of validation and sources of error in estimation are not yet understood or predictable. In addition to the problems relevant to other health outcomes, child maltreatment researchers face additional challenges, including the complex ethical issues associated with both internet-based and child maltreatment research. If these issues are adequately addressed, social media and internet-based technologies may be a promising approach to reducing some of the limitations in existing child maltreatment data.

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          Most cited references154

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          Google trends: a web-based tool for real-time surveillance of disease outbreaks.

          Google Flu Trends can detect regional outbreaks of influenza 7-10 days before conventional Centers for Disease Control and Prevention surveillance systems. We describe the Google Trends tool, explain how the data are processed, present examples, and discuss its strengths and limitations. Google Trends shows great promise as a timely, robust, and sensitive surveillance system. It is best used for surveillance of epidemics and diseases with high prevalences and is currently better suited to track disease activity in developed countries, because to be most effective, it requires large populations of Web search users. Spikes in search volume are currently hard to interpret but have the benefit of increasing vigilance. Google should work with public health care practitioners to develop specialized tools, using Google Flu Trends as a blueprint, to track infectious diseases. Suitable Web search query proxies for diseases need to be established for specialized tools or syndromic surveillance. This unique and innovative technology takes us one step closer to true real-time outbreak surveillance.
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            Is Open Access

            National and Local Influenza Surveillance through Twitter: An Analysis of the 2012-2013 Influenza Epidemic

            Social media have been proposed as a data source for influenza surveillance because they have the potential to offer real-time access to millions of short, geographically localized messages containing information regarding personal well-being. However, accuracy of social media surveillance systems declines with media attention because media attention increases “chatter” – messages that are about influenza but that do not pertain to an actual infection – masking signs of true influenza prevalence. This paper summarizes our recently developed influenza infection detection algorithm that automatically distinguishes relevant tweets from other chatter, and we describe our current influenza surveillance system which was actively deployed during the full 2012-2013 influenza season. Our objective was to analyze the performance of this system during the most recent 2012–2013 influenza season and to analyze the performance at multiple levels of geographic granularity, unlike past studies that focused on national or regional surveillance. Our system’s influenza prevalence estimates were strongly correlated with surveillance data from the Centers for Disease Control and Prevention for the United States (r = 0.93, p < 0.001) as well as surveillance data from the Department of Health and Mental Hygiene of New York City (r = 0.88, p < 0.001). Our system detected the weekly change in direction (increasing or decreasing) of influenza prevalence with 85% accuracy, a nearly twofold increase over a simpler model, demonstrating the utility of explicitly distinguishing infection tweets from other chatter.
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              HealthMap: Global Infectious Disease Monitoring through Automated Classification and Visualization of Internet Media Reports

              Objective Unstructured electronic information sources, such as news reports, are proving to be valuable inputs for public health surveillance. However, staying abreast of current disease outbreaks requires scouring a continually growing number of disparate news sources and alert services, resulting in information overload. Our objective is to address this challenge through the HealthMap.org Web application, an automated system for querying, filtering, integrating and visualizing unstructured reports on disease outbreaks. Design This report describes the design principles, software architecture and implementation of HealthMap and discusses key challenges and future plans. Measurements We describe the process by which HealthMap collects and integrates outbreak data from a variety of sources, including news media (e.g., Google News), expert-curated accounts (e.g., ProMED Mail), and validated official alerts. Through the use of text processing algorithms, the system classifies alerts by location and disease and then overlays them on an interactive geographic map. We measure the accuracy of the classification algorithms based on the level of human curation necessary to correct misclassifications, and examine geographic coverage. Results As part of the evaluation of the system, we analyzed 778 reports with HealthMap, representing 87 disease categories and 89 countries. The automated classifier performed with 84% accuracy, demonstrating significant usefulness in managing the large volume of information processed by the system. Accuracy for ProMED alerts is 91% compared to Google News reports at 81%, as ProMED messages follow a more regular structure. Conclusion HealthMap is a useful free and open resource employing text-processing algorithms to identify important disease outbreak information through a user-friendly interface.
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                Author and article information

                Contributors
                Journal
                Child Abuse Negl
                Child Abuse Negl
                Child Abuse & Neglect
                The Author(s). Published by Elsevier Ltd.
                0145-2134
                1873-7757
                1 February 2018
                November 2018
                1 February 2018
                : 85
                : 187-201
                Affiliations
                [a ]The Kempe Center for The Prevention and Treatment of Child Abuse and Neglect, University of Colorado, Anschutz Medical Campus, 13123 E 16th Ave., Aurora, CO 80045, USA
                [b ]Public Health Agency of Canada, 785 Carling Ave., Ottawa, ON, K1A 0K9, Canada
                Author notes
                [* ]Corresponding author. Present address: Purdue University, Department of Health & Kinesiology, 800 W Stadium Ave., Lafayette, IN 47907, USA. lschwabr@ 123456purdue.edu
                Article
                S0145-2134(18)30026-7
                10.1016/j.chiabu.2018.01.014
                7112406
                29366596
                10be6f3b-8e60-4b80-b35b-08e95c06e0d3
                © 2018 The Author(s)

                Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

                History
                : 15 August 2017
                : 6 December 2017
                : 12 January 2018
                Categories
                Article

                child abuse,social media,internet,epidemiology,surveillance,scoping review

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