Crowdbreaks: Tracking Health Trends using Public Social Media Data and Crowdsourcing

In the past decade, tracking health trends using social media data has shown great promise, due to a powerful combination of massive adoption of social media around the world, and increasingly potent hardware and software that enables us to work with these new big data streams. At the same time, many challenging problems have been identified. First, there is often a mismatch between how rapidly online data can change, and how rapidly algorithms are updated, which means that there is limited reusability for algorithms trained on past data as their performance decreases over time. Second, much of the work is focusing on specific issues during a specific past period in time, even though public health institutions would need flexible tools to assess multiple evolving situations in real time. Third, most tools providing such capabilities are proprietary systems with little algorithmic or data transparency, and thus little buy-in from the global public health and research community. Here, we introduce Crowdbreaks, an open platform which allows tracking of health trends by making use of continuous crowdsourced labelling of public social media content. The system is built in a way which automatizes the typical workflow from data collection, filtering, labelling and training of machine learning classifiers and therefore can greatly accelerate the research process in the public health domain. This work introduces the technical aspects of the platform and explores its future use cases

Crowdsourcing applications addressing diseases and public health: a perspective on COVID-19 infestation

The spread of the COVID-19 disease with an unprecedented speed into humans, and the global scale of its occurrence over multiple geographic locations, since December 2019, in Wuhan, China, has sparked off extensive confusion and debate in public health, giving it the status of a pandemic. The inability of restraining the outbreak in the early stages, has multiplied the disease risk to fatal complications. Crowdsourcing technique can conglomerate crowd knowledge for solving problems revolutionizing health care by use of internet sources, data mining, e-health trackers, etc. to collect and assess data faster to the rate of spread of infection, directly from a point source (individual-level). The present study provides perspectives on crowdsourcing in alignment with health care and public health services by critically comparing strengths and challenges with traditional methods. For the same 3 models have been designed by the authors, for improvement in public health care, in the wake of the COVID-19 infestation

Technology for Good: Innovative Use of Technology by Charities

Technology for Good identifies ten technologies being used by charitable organizations in innovative ways. The report briefly introduces each technology and provides examples of how those technologies are being used.Examples are drawn from a broad spectrum of organizations working on widely varied issues around the globe. This makes Technology for Good a unique repository of inspiration for the public and private sectors, funders, and other change makers who support the creation and use of technology for social good

Systematic review on the prevalence, frequency and comparative value of adverse events data in social media

Aim: The aim of this review was to summarize the prevalence, frequency and comparative value of information on the adverse events of healthcare interventions from user comments and videos in social media. Methods: A systematic review of assessments of the prevalence or type of information on adverse events in social media was undertaken. Sixteen databases and two internet search engines were searched in addition to handsearching, reference checking and contacting experts. The results were sifted independently by two researchers. Data extraction and quality assessment were carried out by one researcher and checked by a second. The quality assessment tool was devised in-house and a narrative synthesis of the results followed. Results: From 3064 records, 51 studies met the inclusion criteria. The studies assessed over 174 social media sites with discussion forums (71%) being the most popular. The overall prevalence of adverse events reports in social media varied from 0.2% to 8% of posts. Twenty-nine studies compared the results from searching social media with using other data sources to identify adverse events. There was general agreement that a higher frequency of adverse events was found in social media and that this was particularly true for ‘symptom’ related and ‘mild’ adverse events. Those adverse events that were under-represented in social media were laboratory-based and serious adverse events. Conclusions: Reports of adverse events are identifiable within social media. However, there is considerable heterogeneity in the frequency and type of events reported, and the reliability or validity of the data has not been thoroughly evaluated

Leveraging Social Media and Web of Data for Crisis Response Coordination

There is an ever increasing number of users in social media (1B+ Facebook users, 500M+ Twitter users) and ubiquitous mobile access (6B+ mobile phone subscribers) who share their observations and opinions. In addition, the Web of Data and existing knowledge bases keep on growing at a rapid pace. In this scenario, we have unprecedented opportunities to improve crisis response by extracting social signals, creating spatio-temporal mappings, performing analytics on social and Web of Data, and supporting a variety of applications. Such applications can help provide situational awareness during an emergency, improve preparedness, and assist during the rebuilding/recovery phase of a disaster. Data mining can provide valuable insights to support emergency responders and other stakeholders during crisis. However, there are a number of challenges and existing computing technology may not work in all cases. Therefore, our objective here is to present the characterization of such data mining tasks, and challenges that need further research attention

Evidence and future potential of mobile phone data for disease disaster management

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.Global health threats such as the recent Ebola and Zika virus outbreaks require rapid and robust responses to prevent, reduce and recover from disease dispersion. As part of broader big data and digital humanitarianism discourses, there is an emerging interest in data produced through mobile phone communications for enhancing the data environment in such circumstances. This paper assembles user perspectives and critically examines existing evidence and future potential of mobile phone data derived from call detail records (CDRs) and two-way short message service (SMS) platforms, for managing and responding to humanitarian disasters caused by communicable disease outbreaks. We undertake a scoping review of relevant literature and in-depth interviews with key informants to ascertain the: (i) information that can be gathered from CDRs or SMS data; (ii) phase(s) in the disease disaster management cycle when mobile data may be useful; (iii) value added over conventional approaches to data collection and transfer; (iv) barriers and enablers to use of mobile data in disaster contexts; and (v) the social and ethical challenges. Based on this evidence we develop a typology of mobile phone data sources, types, and end-uses, and a decision-tree for mobile data use, designed to enable effective use of mobile data for disease disaster management. We show that mobile data holds great potential for improving the quality, quantity and timing of selected information required for disaster management, but that testing and evaluation of the benefits, constraints and limitations of mobile data use in a wider range of mobile-user and disaster contexts is needed to fully understand its utility, validity, and limitations.A portion of this research was funded as part of the Science for Humanitarian Emergencies and Resilience (SHEAR) programme, by the UK Department for International Development (DFID), the Natural Environment Research Council (NERC) and the Economic and Social Research Council (ESRC)

Crowdbreaks: Tracking Health Trends Using Public Social Media Data and Crowdsourcing

In the past decade, tracking health trends using social media data has shown great promise, due to a powerful combination of massive adoption of social media around the world, and increasingly potent hardware and software that enables us to work with these new big data streams. At the same time, many challenging problems have been identified. First, there is often a mismatch between how rapidly online data can change, and how rapidly algorithms are updated, which means that there is limited reusability for algorithms trained on past data as their performance decreases over time. Second, much of the work is focusing on specific issues during a specific past period in time, even though public health institutions would need flexible tools to assess multiple evolving situations in real time. Third, most tools providing such capabilities are proprietary systems with little algorithmic or data transparency, and thus little buy-in from the global public health and research community. Here, we introduce Crowdbreaks, an open platform which allows tracking of health trends by making use of continuous crowdsourced labeling of public social media content. The system is built in a way which automatizes the typical workflow from data collection, filtering, labeling and training of machine learning classifiers and therefore can greatly accelerate the research process in the public health domain. This work describes the technical aspects of the platform, thereby covering the functionalities at its current state and exploring its future use cases and extensions

Crowdbreaks: Tracking Health Trends Using Public Social Media Data and Crowdsourcing

In the past decade, tracking health trends using social media data has shown great promise, due to a powerful combination of massive adoption of social media around the world, and increasingly potent hardware and software that enables us to work with these new big data streams. At the same time, many challenging problems have been identified. First, there is often a mismatch between how rapidly online data can change, and how rapidly algorithms are updated, which means that there is limited reusability for algorithms trained on past data as their performance decreases over time. Second, much of the work is focusing on specific issues during a specific past period in time, even though public health institutions would need flexible tools to assess multiple evolving situations in real time. Third, most tools providing such capabilities are proprietary systems with little algorithmic or data transparency, and thus little buy-in from the global public health and research community. Here, we introduce Crowdbreaks, an open platform which allows tracking of health trends by making use of continuous crowdsourced labeling of public social media content. The system is built in a way which automatizes the typical workflow from data collection, filtering, labeling and training of machine learning classifiers and therefore can greatly accelerate the research process in the public health domain. This work describes the technical aspects of the platform, thereby covering the functionalities at its current state and exploring its future use cases and extensions