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Data philanthropy refers to private sector companies sharing data they collected with the public, in an attempt to address social or environmental challenges for public benefit. [1] The concept was introduced through the United Nations Global Pulse initiative in 2011 to explore corporate data assets for humanitarian, academic, and societal causes. [2] For instance, anonymized mobile data has been used to track disease outbreaks. In contrast, corporate data on consumer behavior may be shared with researchers to study public health and economic trends. [3]
A significant portion of data collected from the Internet consists of user-generated content, such as blogs, social media posts, and information submitted through lead capture and data forms. In addition to this, corporations gather and analyze consumer data to gain insights into customer behavior, identify potential markets, and inform investment decisions. United Nations Global Pulse Director, Robert Kirkpatrick, has referred to this type of data as "massive passive data" or "data exhaust." [4]
Data philanthropy refers to private sector data-sharing initiatives intended to serve public purposes. [1] The term "philanthropy" suggests that data sharing may be perceived as a public good. [4]
While data philanthropy has the potential to enhance development policies, it also raises concerns regarding privacy, ownership, and the equitable use of data. [5] Making users' private data available to various organizations and institutions can raise concerns regarding internet privacy. Mathematical techniques such as differential privacy and space-time boxes have been developed to allow access to personal data while ensuring user anonymity. However, even if these algorithms work, re-identification may still be possible. [1]
Another challenge is convincing corporations to share their data. The data collected by corporations provides them with market competitiveness and insight regarding consumer behavior. Corporations may fear losing their competitive edge if they share the information they have collected to the public. [1]
Numerous moral challenges are also encountered. In 2016, Mariarosaria Taddeo, a Digital Ethics professor at the University of Oxford, proposed an ethical framework to address these moral challenges. [6]
The goal of data philanthropy is to create a global data commons, where companies, governments, and individuals can contribute anonymous, aggregated datasets. [2] The United Nations Global Pulse offers four different tactics that companies can use to share their data that preserve consumer anonymity: [1]
Through data philanthropy, corporations such as social networking sites (e.g., Facebook, Twitter), telecommunication companies (e.g., Verizon, AT&T), and search engines (e.g., Google, Bing), amongst other data-intensive organizations, collect and make anonymized and aggregated user-generated information available to a data sharing system for research, policy development, and social impact initiatives. This also allows institutions to give back to a cause widely seen as beneficial. With the onset of technological advancements, a sharing of data on a global scale and an in-depth analysis of these data structures could alter the reaction towards global events such as natural disasters and epidemics. Robert Kirkpatrick, the Director of the United Nations Global Pulse, has argued that this aggregated information is beneficial for the common good and can lead to developments in research and data production in a range of varied fields. [4]
Health researchers use digital disease detection by collecting data from various sources—such as social media platforms (e.g., Twitter, Facebook), mobile devices (e.g., cell phones, smartphones), online search queries, mobile apps, and sensor data from wearables and environmental sensors—to monitor and predict the spread of infectious diseases. This approach allows them to track and anticipate outbreaks of epidemics (e.g., COVID-19, Ebola), pandemics, vector-borne diseases (e.g., malaria, dengue fever), and respiratory illnesses (e.g., influenza, SARS), improving response and intervention strategies for the spread of diseases. [7]
In the United States, HealthMap is using data philanthropy-related tactics to track the outbreak of diseases. HealthMap analyzes data from publicly available media sources such as news websites, government alerts, and social media sites like X (formerly known as Twitter) for outbreaks of various illnesses around the world. [7] [8] Another website, Flu Near You, allows users to report their own health status on a weekly basis. Traditional flu surveillance can take up to 2 weeks to confirm outbreaks [7] , and doctors must wait for a virological test to confirm the outbreak before reporting it to the Centers for Disease Control. This form of data philanthropy allows for up-to-date information regarding various health concerns by using publicly available information gathered from news outlets, government alerts, and social media sites. HealthMap and Flu Near You are considered data philanthropy as users' data is gathered from social media sites without their knowledge. [7]
The Centers for Disease Control and Prevention collaborated with Google and launched Google Flu Trends in 2008, a website that tracks flu-related searches and user locations to track the spread of the flu. Users can visit the website to compare the amount of flu-related search activity versus the reported numbers of flu outbreaks on a graphical map. The difficulty with this method of tracking is that Google searches are sometimes performed due to curiosity rather than when an individual is suffering from the flu. According to Ashley Fowlkes, an epidemiologist in the CDC Influenza division, "The Google Flu Trends system tries to account for that type of media bias by modeling search terms over time to see which ones remain stable." [7] Google Flu Trends is no longer publishing current flu estimates on the public website; however, visitors to the site can still view and download previous estimates. Current data can be shared with verified researchers. [9]
A study from the Harvard School of Public Health (HSPH), published in the October 12, 2012 issue of Science, discussed how phone data helped curb the spread of malaria in Kenya. The researchers mapped phone calls and texts made by 14,816,521 Kenyan mobile phone subscribers. [10] When individuals left their primary living location, the destination and length of journey were calculated. This data was then compared to a 2009 malaria prevalence map to estimate the disease's commonality in each location. Combining all this information, the researchers can estimate the probability of an individual carrying malaria and map the movement of the disease. This research can be used to track the spread of similar diseases. [10]
Calling patterns of mobile phone users can determine the socioeconomic standings of the populace, which can be used to deduce "its access to housing, education, healthcare, and basic services such as water and electricity.". [4] Researchers from Columbia University and Karolinska Institute used daily SIM card location data from both before and after the 2010 Haiti earthquake to estimate the movement of people both in response to the earthquake and during the related 2010 Haiti cholera outbreak. [11] Their research suggests that mobile phone data can provide rapid and accurate estimates of population movements during disasters and outbreaks of infectious disease. Big data can also provide information on looming disasters and can assist relief organizations in rapid response and locating displaced individuals. By analyzing specific patterns within this 'big data,' we can enhance responses to disruptive events such as natural disasters, disease outbreaks, and global economic crises. Leveraging real-time information enables a deeper understanding of individual well-being, allowing for more effective interventions. Corporations utilize digital services, such as human sensor systems, to detect and solve impending problems within communities. This is a strategy implemented by the private sector in order to protect its citizens by anonymously disseminating customer information to the public sector whilst also ensuring the protection of their privacy. [4]
Poverty still remains a worldwide issue with over 2.5 billion people [12] currently impoverished. Accumulating accurate data has been a complex issue but developments in technology and utilising 'big data' [12] is one solution for improving this situation. Statistics indicate the widespread use of mobile phones, even within impoverished communities. This availability could prove vital in gathering data on populations living in poverty. Additional data can be collected through Internet access, social media, utility payments and governmental statistics. Data-driven activities can lead to the cumulation of 'big data', which in turn can assist international non-governmental organization in documenting and evaluating the needs of underprivileged populations. Through data philanthropy, NGOs can distribute information whilst cooperating with governments and private companies. [12]
Data philanthropy incorporates aspects of social philanthropy by allowing corporations to create profound impacts through the act of giving back by dispersing proprietary datasets. [13] The public sector collects and preserves information, considered an essential asset. Companies track and analyze users' online activities to gain insights into their needs related to new products and services. [14] These companies view the welfare of the population as key to business expansion and progression by using their data to highlight global citizens' issues. [4] Experts in the private sector emphasize the importance of integrating diverse data sources—such as retail, mobile, and social media data—to develop essential solutions for global challenges. In Data Philanthropy: New Paradigms for Collaborative Problem Solving (2022), authors Stefaan Verhulst and Andrew Young discuss this approach. Robert Kirkpatrick argues that, although sharing private information carries inherent risks, it ultimately yields public benefits, supporting the common good. [15] – via Harvard Business Review (subscription required) The digital revolution causes an extensive production of big data that is user-generated and available on the web. Corporations accumulate information on customer preferences through the digital services they utilize and products they purchase to gain clear insights on their clientele and future market opportunities. [4] However, the rights of individuals concerning privacy and ownership of data are controversial, as governments and other institutions can use this collective data for unethical purposes. Companies monitor and probe consumer online activities in order to better comprehend and develop tailored needs for their clientele, thereby increasing their profits. [16]
Data philanthropy plays an important role in academia. Researchers encounter countless obstacles while attempting to access data. This data is available to a limited number of researchers with sole access to restricted resources who are authorized to utilize this information, like social media streams, enabling them to produce more knowledge and develop new studies. For example, X Corp. (formerly Twitter Inc.) markets access to its real-time APIs at various prices, e.g. $5,000 for reading 1,000,000 posts per month, which often surpasses the budgets of most researchers.
Data philanthropy aids the human rights movement by assisting in dispersing evidence for truth commissions and war crimes tribunals. Advocates for human rights gather data on abuses occurring within countries, which is then used for scientific analysis to raise awareness and drive action. For example, non-profit organizations compile data from human rights monitors in war zones to assist the UN High Commissioner for Human Rights. This data uncovers inconsistencies in the number of war casualties, leading to international attention and influencing global policy discussions. [16]
Public health surveillance is, according to the World Health Organization (WHO), "the continuous, systematic collection, analysis and interpretation of health-related data needed for the planning, implementation, and evaluation of public health practice." Public health surveillance may be used to track emerging health-related issues at an early stage and find active solutions in a timely manner. Surveillance systems are generally called upon to provide information regarding when and where health problems are occurring and who is affected.
GISAID, the Global Initiative on Sharing All Influenza Data, previously the Global Initiative on Sharing Avian Influenza Data, is a global science initiative established in 2008 to provide access to genomic data of influenza viruses. The database was expanded to include the coronavirus responsible for the COVID-19 pandemic, as well as other pathogens. The database has been described as "the world's largest repository of COVID-19 sequences". GISAID facilitates genomic epidemiology and real-time surveillance to monitor the emergence of new COVID-19 viral strains across the planet.
Tele-epidemiology is the application of telecommunications to epidemiological research and application, including space-based and internet-based systems.
"Health 2.0" is a term introduced in the mid-2000s, as the subset of health care technologies mirroring the wider Web 2.0 movement. It has been defined variously as including social media, user-generated content, and cloud-based and mobile technologies. Some Health 2.0 proponents see these technologies as empowering patients to have greater control over their own health care and diminishing medical paternalism. Critics of the technologies have expressed concerns about possible misinformation and violations of patient privacy.
mHealth is an abbreviation for mobile health, a term used for the practice of medicine and public health supported by mobile devices. The term is most commonly used in reference to using mobile communication devices, such as mobile phones, tablet computers and personal digital assistants (PDAs), and wearable devices such as smart watches, for health services, information, and data collection. The mHealth field has emerged as a sub-segment of eHealth, the use of information and communication technology (ICT), such as computers, mobile phones, communications satellite, patient monitors, etc., for health services and information. mHealth applications include the use of mobile devices in collecting community and clinical health data, delivery/sharing of healthcare information for practitioners, researchers and patients, real-time monitoring of patient vital signs, the direct provision of care as well as training and collaboration of health workers.
Reality mining is the collection and analysis of machine-sensed environmental data pertaining to human social behavior, with the goal of identifying predictable patterns of behavior. In 2008, MIT Technology Review called it one of the "10 technologies most likely to change the way we live."
Urban computing is an interdisciplinary field which pertains to the study and application of computing technology in urban areas. This involves the application of wireless networks, sensors, computational power, and data to improve the quality of densely populated areas. Urban computing is the technological framework for smart cities.
Mobile technology is the technology used for cellular communication. Mobile technology has evolved rapidly over the past few years. Since the start of this millennium, a standard mobile device has gone from being no more than a simple two-way pager to being a mobile phone, GPS navigation device, an embedded web browser and instant messaging client, and a handheld gaming console. Many experts believe that the future of computer technology rests in mobile computing with wireless networking. Mobile computing by way of tablet computers is becoming more popular. Tablets are available on the 3G and 4G networks.
The social data revolution is the shift in human communication patterns towards increased personal information sharing and its related implications, made possible by the rise of social networks in the early 2000s. This phenomenon has resulted in the accumulation of unprecedented amounts of public data.
Infoveillance is a type of syndromic surveillance that specifically utilizes information found online. The term, along with the term infodemiology, was coined by Gunther Eysenbach to describe research that uses online information to gather information about human behavior.
Google Flu Trends (GFT) was a web service operated by Google. It provided estimates of influenza activity for more than 25 countries. By aggregating Google Search queries, it attempted to make accurate predictions about flu activity. This project was first launched in 2008 by Google.org to help predict outbreaks of flu.
Infodemiology was defined by Gunther Eysenbach in the early 2000s as information epidemiology. It is an area of science research focused on scanning the internet for user-contributed health-related content, with the ultimate goal of improving public health. Later, it is also defined as the science of mitigating public health problems resulting from an infodemic.
Azumio is a mobile health company that specializes in biometric mobile technology. Founded in 2011, Azumio develops Apple iOS and Android health apps and services. Azumio has released 24 apps on iOS, 5 apps on Android, and 3 apps on Windows Phone. The company is headquartered in Palo Alto, California.
Venmo is an American mobile payment service founded in 2009 and owned by PayPal since 2013. Venmo is aimed at users who wish to split their bills. Account holders can transfer funds to others via a mobile phone app; both the sender and receiver must live in the United States. Venmo also operates as a small social network, as users can observe other users' public transactions with posts and emoticons. In 2021, the company handled $230 billion in transactions and generated $850 million in revenue. Users can view transactions on the Venmo website but cannot complete transactions on the website.
Participatory surveillance is community-based monitoring of other individuals. This term can be applied to both digital media studies and ecological field studies. In the realm of media studies, it refers to how users surveil each other using the internet. Through the use of social media, search engines, people search sites and other web-based aggregators of data, one has the power to find information about the individual being searched, whether voluntarily shared by them or not. Issues of privacy emerge within this sphere of participatory surveillance, predominantly focused on how much information is available on the web that an individual does not consent to. More so, disease outbreak researchers can study social-media based patterns to decrease the time it takes to detect an outbreak, an emerging field of study called infodemiology. Within the realm of ecological fieldwork, participatory surveillance is used as an overarching term for the method in which indigenous and rural communities are used to gain greater accessibility to causes of disease outbreak. By using these communities, disease outbreak can be spotted earlier than through traditional means or healthcare institutions.
An infodemic is a rapid and far-reaching spread of both accurate and inaccurate information about certain issues. The word is a portmanteau of information and epidemic and is used as a metaphor to describe how misinformation and disinformation can spread like a virus from person to person and affect people like a disease. This term, originally coined in 2003 by David Rothkopf, rose to prominence in 2020 during the COVID-19 pandemic.
Caroline O'Flaherty Buckee is an epidemiologist. She is a Professor of Epidemiology at the Harvard T.H. Chan School of Public Health. Buckee is known for her work in digital epidemiology, where mathematical models track mobile and satellite data to understand the transmission of infectious diseases through populations in an effort to understand the spatial dynamics of disease transmission. Her work examines the implications of conducting surveillance and implementing control programs as a way to understand and predict what will happen when dealing with outbreaks of infectious diseases like malaria and COVID-19.
Data collaboratives are a form of collaboration in which participants from different sectors—including private companies, research institutions, and government agencies—can exchange data and data expertise to help solve public problems.
Mobile positioning data (MPD) is a form of big datawhich results from the high data volumes of mobile positioning – tracking the location of mobile phones.
Rumi Chunara is a computer scientist who is an associate professor of biostatistics at the New York University School of Global Public Health. She develops computational and statistical approaches to acquire, integrate and make use of data improve population-level public health.