Paper by Julian Wahl, Johann Füller, and Katja Hutter: “In this research, we explore how crowdsourcing combined with text-mining can help to build a sound understanding of unstructured, complex and ill-defined problems. Therefore, we gathered 101 problem descriptions contributed to a crowdsourcing contest about the impact of COVID-19 on the tourism industry. Based on our findings we propose a five-phase process model for problem understanding consisting of: (1) information gathering, (2) information pre-structuring, (3) problem space mapping, (4) problem space exploration, and (5) problem understanding for solution search. While our study confirms that crowdsourcing and text-mining facilitate fast generation and exploration of problem spaces at limited cost, it also reveals the necessity to follow certain process steps and to deal with challenges such as information loss and human interpretation. For practitioners, our model presents a guideline for how to get a faster grasp on complex and rather unprecedented problems…(More)”.
Expecting the Unexpected: Effects of Data Collection Design Choices on the Quality of Crowdsourced User-Generated Content
Paper by Roman Lukyanenko: “As crowdsourced user-generated content becomes an important source of data for organizations, a pressing question is how to ensure that data contributed by ordinary people outside of traditional organizational boundaries is of suitable quality to be useful for both known and unanticipated purposes. This research examines the impact of different information quality management strategies, and corresponding data collection design choices, on key dimensions of information quality in crowdsourced user-generated content. We conceptualize a contributor-centric information quality management approach focusing on instance-based data collection. We contrast it with the traditional consumer-centric fitness-for-use conceptualization of information quality that emphasizes class-based data collection. We present laboratory and field experiments conducted in a citizen science domain that demonstrate trade-offs between the quality dimensions of accuracy, completeness (including discoveries), and precision between the two information management approaches and their corresponding data collection designs. Specifically, we show that instance-based data collection results in higher accuracy, dataset completeness and number of discoveries, but this comes at the expense of lower precision. We further validate the practical value of the instance-based approach by conducting an applicability check with potential data consumers (scientists, in our context of citizen science). In a follow-up study, we show, using human experts and supervised machine learning techniques, that substantial precision gains on instance-based data can be achieved with post-processing. We conclude by discussing the benefits and limitations of different information quality and data collection design choice for information quality in crowdsourced user-generated content…(More)”.
Making Space for Everyone
Amy Paige Kaminski at Issues: “The story of how NASA came to see the public as instrumental in accomplishing its mission provides insights for R&D agencies trying to create societal value, relevance, and connection….Over the decades since, NASA’s approaches to connecting with citizens have evolved with the introduction of new information and communications technologies, social change, legal developments, scientific progress, and external trends in space activities and public engagement. The result has been an increasing and increasingly accessible set of opportunities that have enabled diverse segments of society to connect more closely with NASA’s work and, in turn, boost the agency’s techno-scientific and societal value….
Another significant change in public engagement practices has been providing more people with opportunities to do space-related R&D. Through the shuttle program, the agency enabled companies, universities, high schools, and an eclectic set of participants ranging from artists to garden seed companies to develop and fly payloads. The stated purpose was to advance knowledge of the effects of the space environment—a concept that was sometimes loosely defined.
Today NASA similarly encourages a broad set of players to use the International Space Station (ISS) for R&D. While some of the shuttle and ISS programs have charged fees to payload owners, NASA has instead offered grants, primarily to the university community, for competitively selected research projects in space science. The agency also invites various groups to propose experiments and technology development projects through government-wide programs such as the Small Business Innovative Research program, which aims to foster innovation in small businesses, as well as the Established Program to Stimulate Competitive Research (better known by its EPSCoR acronym), which seeks to enhance research infrastructure and competitiveness at the state level….(More)”.
Public Crowdsourcing: Analyzing the Role of Government Feedback on Civic Digital Platforms
Paper by Lisa Schmidthuber, Dennis Hilgers, and Krithika Randhawa: “Government organizations increasingly use crowdsourcing platforms to interact with citizens and integrate their requests in designing and delivering public services. Government usually provides feedback to individual users on whether the request can be considered. Drawing on attribution theory, this study asks how the causal attributions of the government response affect continued participation in crowdsourcing platforms. To test our hypotheses, we use a 7-year dataset of both online requests from citizens to government and government responses to citizen requests. We focus on citizen requests that are denied by government, and find that stable and uncontrollable attributions of the government response have a negative effect on future participation behavior. Also, a local government’s locus of causality negatively affects continued participation. This study contributes to research on the role of responsiveness in digital interaction between citizens and government and highlights the importance of rationale transparency to sustain citizen participation…(More)”.
Information Disorder in the Glam Sector: The Challenges of Crowd Sourced Contributions
Paper by Saima Qutab, Michael David Myers and Lesley Gardner: “For some years information systems researchers have looked at crowdsourcing as a way for individuals, organizations and institutions to co-create content and generate value. Although there are many potential benefits of crowdsourcing, the quality control of crowd contributions stands out as one of the most significant challenges. Crowds can create the information contents but at the same time can facilitate information disorder: misinformation, disinformation and mal-information.
Crowd created information is a dominant element in what is sometimes called the post-truth era. A small piece of misleading information can constitute significant challenges to the information sharing group or society. This misinformation can reshape in various ways how information-driven communities make sense of their world. As information disorder and its effects have recently started to be recognised as a potential problem in IS research, we need to explore this phenomenon in more detail, to understand how it happens and why. This multiple case study is aimed at appraising information disorder through crowd-created contents in the knowledge and cultural heritage organisations such as Galleries, Libraries, Archives and Museums (GLAM). We intend to investigate the quality control mechanisms that might be used to prevent and minimise the effects of information disorder from crowdsourced contributions….(More)”.
Afyanet
About: “Afyanet is a voluntary, non-profit network of National Health Institutes and Research Centers seeking to leverage crowdsourced health data for disease surveillance and forecasting. Participation in AfyaNet for countries is free.
We aim to use technology and digital solutions to radically enhance how traditional disease surveillance systems function and the ways we can model epidemics.
Our vision is to create a common framework to collect standardized real-time data from the general population, allowing countries to leapfrog existing hurdles in disease surveillance and information sharing.
Our solution is an Early Warning System for Health based on participatory data gathering. A common, real-time framework for disease collection will help countries identify and forecast outbreaks faster and more effectively.
Crowdsourced data is gathered directly from citizens, then aggregated, anonymized, and processed in a cloud-based data lake. Our high-performance computing architecture analyzes the data and creates valuable disease spread models, which in turn provide alerts and notifications to participating countries and helps public health authorities make evidence-based decisions….(More)”
Crowdsourced Sensor Data Powers Smoke Map
OpenGov: “The Environmental Protection Agency and the U.S. Forest Service (USFS) have released updates to the AirNow Fire and Smoke Map to help protect communities from the effects of wildfire smoke. Started as a pilot project last year, the map pulls data from three sources: temporary monitors such as those the Forest Service and other agencies have deployed near fires; crowdsourced data from nearly 10,000 low-cost sensors nationwide that measure fine particle pollution, the major harmful pollutant in smoke; and monitors that regularly report to AirNow, EPA’s one-stop source for air quality data.
The agencies announced improvements to the map, including a dashboard that gives users quick access to information that can help them plan their outdoor activities, the current Air Quality Index (AQI) category at the monitor or sensor location, data showing whether air quality is improving or worsening, and information about actions to consider taking based on the AQI.
EPA and USFS developed the map-pilot to provide information on fire locations, smoke plumes and air quality in one place. It had more than 7.4 million views in its first three months. The map imports data from almost 10,000 sensors from an air quality sensor network that crowdsources data on particle pollution, providing real-time measurement of air quality on a public map. This was a logical addition to two other projects already underway.
The extra data points the sensors provided proved useful in characterising air quality during the 2020 fire season, and we had positive reception from state, local and tribal air agency partners, and from the public. The map is intended for individuals to use in making decisions about outdoor activities based on air quality, but the unique fire, smoke and concentration data can help increase awareness of the significant impacts of wildfires across all levels of government — federal, state, local and tribal — and provide a valuable tool to assist agencies as they work to protect public health from wildfire smoke during these emergencies….(More)”.
Chinese web users are writing a new playbook for disaster response
Shen Lu at Protocol: Severe floods caused by torrential rains in Central China’s Henan province have killed dozens and displaced tens of thousands of residents since last weekend. In parallel with local and central governments’ disaster relief and rescue efforts, Chinese web users have organized online, using technology in novel ways to mitigate risks and rescue those who were trapped in subway cars and neighborhoods submerged in floodwaters.
Chinese web users are no strangers to digital crowdsourcing efforts. During the COVID-19 outbreak, volunteers archived censored media reports and personal stories of suffering from disease or injustice that were scattered on social media, saving them on sharable files on GitHub and broadcasting them via Telegram. Despite pervasive censorship, in times of crisis, Chinese web users have managed to keep information and communications channels open among themselves, and with the rest of the world.
Now, people in one of the most oppressive information environments in the world might be helping write the future playbook for disaster response…
In hard-hit Zhengzhou, the capital city of Henan province, tens of thousands of residents crowdsourced relief assistance over the past 48 hours through a simple shared spreadsheet powered by the Tencent equivalent of Google Sheets (Google products are banned in China). It was created by a college student to allow those awaiting rescue to log their contact and location information.
In the 36 hours that followed, droves of volunteers have logged on, vastly expanding the breadth of information that lives on the document. It now includes contact information for official and unofficial rescue teams, relief resources, shelter locations, phone-charging stations and online medical consultations. At certain points, over 200 people have edited the sheet simultaneously.
Tencent reported that by Wednesday evening Beijing time, volunteers had entered nearly 1,000 data points. The document has received over 2.5 million visits, becoming the most visited Tencent Doc ever and one of the most efficient and powerful rescue and aid platforms started and contributed by civilians.
Similar crowdsourced documents for flooding victims live elsewhere on the internet. On Shimo Docs, a cloud-based productivity suite developed by the Beijing-based startup Shimo, volunteers have aggregated relief and rescue resources’ contacts by cities and counties. These shared documents have made the rounds on social media platforms like Weibo and WeChat in the past few days….(More)”.
Helsinki invites cyclists to collect data on street conditions and earn money
Article at the Mayor.eu: “From Saturday 10 July, cyclists in Helsinki will be able to earn money doing what they love whilst simultaneously helping the municipality repair damaged streets. This was announced on 28 June when the City of Helsinki shared that all residents are invited to take part in a game to map out 300 kilometres of cycling paths in the capital.
In a press release, the City of Helsinki reports that anyone can participate as long as they have a bicycle and a smartphone. To take part, one must simply download the free application Crowdchupa and attach their phone to their bicycle. The device will then record footage of the streets and Artificial Intelligence will be used to identify damage that must be repaired.
To make this even more interesting, the Crowdchupa application will allow participants to earn money. The application features a map which depicts various objects (such as coins and berries) on the streets. Cyclists must drive over these virtual objects to collect them and earn money….(More)”.
New Study Uses Crowdsourcing to Strengthen American Democracy
Press Release: “Americans have always disagreed about politics, but now levels of anti-democratic attitudes, support for partisan violence, and partisan animosity have reached concerning levels. While there are many ideas for tackling these problems, they have never been gathered, tested, and evaluated in a unified effort. To address this gap, the Stanford Polarization and Social Change Lab is launching a major new initiative. The Strengthening Democracy Challenge will collect and rigorously test up to 25 interventions to reduce anti-democratic attitudes, support for partisan violence, and partisan animosity in one massive online experiment with up to 30,000 participants. Interventions can be contributed by academics, practitioners, or others with interest in strengthening democratic principles in the US. The researchers who organize the challenge — a multidisciplinary team with members at Stanford, MIT, Northwestern, and Columbia Universities — believe that crowdsourcing ideas, combined with the rigor of large-scale experimentation, can help address issues as substantial and complex as these….
Researchers with diverse backgrounds and perspectives are invited to submit interventions. The proposed interventions must be short, doable in an online form, and follow the ethical guidelines of the challenge. Academic and practitioner experts will rate the submissions and an editorial board will narrow down the 25 best submissions to be tested, taking novelty and expected success of the ideas into account. Co-organizers of the challenge include James Druckman, Payson S. Wild Professor of Political Science at Northwestern University; David Rand, the Erwin H. Schell Professor and Professor of Management Science and Brain and Cognitive Sciences at MIT; James Chu, Assistant Professor of Sociology at Columbia University; and Nick Stagnaro, Post-Doctoral Fellow at MIT. The organizing team is supported by Polarization and Social Change Lab’s Chrystal Redekopp, Joe Mernyk, and Sophia Pink.
The study participants will be a large sample of up to 30,000 self-identified Republicans and Democrats, nationally representative on several major demographic benchmarks….(More)”.