Crowdsourced Information Research Articles

Bridging Community Engagement and Technological Innovation for Creating Smart and Resilient Cities: A Systematic Literature Review

Urbanization presents significant challenges to disaster management as cities grow and develop, hence increasing their vulnerability to disasters. Disaster resilience is crucial for protecting lives and infrastructure, ensuring economic stability, promoting equality and cohesion, and ensuring the long-term viability of metropolitan regions in these rapidly growing cities. This paper investigates contemporary approaches to creating smart and resilient urban environments through disaster management that emphasize community-based solutions in prioritizing advanced technologies. The key findings of the research include three factors to be accomplished in utilizing technology in community-based disaster management, trust in the crowd, digital divide, and cultural sensitivity. Moreover, the review highlights the significance of the use of smart technologies in improving urban resilience, including but not limited to real-time data-sharing platforms and ML algorithms. Furthermore, it emphasizes the challenges regarding reliability and accuracy in crowdsourced information, stressing the importance of user awareness.

The use of social media in plastic surgery biomedical research: scoping systematic review

Background: Social media has become ubiquitous; its uses reach beyond connecting individuals or organizations. Many biomedical researchers have found social media to be a useful tool in recruiting patients for clinical studies, crowdsourcing for cross-sectional studies, and even as a method of intervention. Social media usefulness in biomedical research has largely been in population health and non-surgical specialties, however, its usefulness in surgical specialties should not be overlooked. Specifically in plastic surgery, social media use to understand patient perceptions, identify populations, and provide care has become an important part of clinical practice. Methods: A scoping review was performed utilizing PubMed and Medline databases, and articles were screened for the use of social media as a method of recruitment to a clinical trial, as crowdsourcing (i.e., recruitment for a cross-sectional or survey-based study), or as a method of intervention. Results: A total of 28 studies were included, which focused on majority females between 18–34 years old. Despite the ability of the internet and social media to connect people worldwide, nearly all the studies focused on the researchers’ home countries. The studies largely focused on social media’s effect on self-esteem and acceptance of cosmetic surgery, but other notable trends were analyses of patient perceptions of a disease, or surgical outcomes as reported in social media posts. Discussion: Overall, social media can be a useful tool for plastic surgeons looking to recruit patients for a survey-based study or crowdsourcing of information.

‘Texting Scarlatti’: unlocking a standard edition with a digital toolkit

Editing musical texts requires a great deal of artistic, intellectual and commercial investment but, once published, an edition tends to become inert, a form of musical still life. ‘Texting Scarlatti’ explores ways of unlocking the value of a standard edition using a variety of digital tools. For as long as the editorial process in music begins and ends with analogue graphical notation, a digital music edition will inevitably be hybrid to some extent. But our project examines each stage in that process from input to output, showing how scholarly insight, performance-based musicology, data science, HCI-inspired crowdsourcing techniques and information systems can together form the basis of an open, rich digital edition for future development. Key outputs will be an extremely large machine-readable dataset consisting of more than 310,000 bars of music, together with structured, searchable apparatus for each of the 555 sonatas in the Scarlatti canon. Musicology has been a notable absentee from recent developments in artificial intelligence and machine learning (AIML) and there is a great deal of catching-up to do. In our conclusion, we urge more scholars and publishers to make similarly large, and largely hidden, datasets more openly available for machine learning and suggest some practical steps to facilitate this.

Remote sensing of artisanal and small-scale mining: A review of scalable mapping approaches

Artisanal and small-scale mining (ASM) significantly influences the socio-economic development of many low-to-middle-income countries, albeit sometimes at the expense of environmental and human health. Characterized by its labor-intensive extraction from confined (<5 ha) or peripheral mineral reserves, congregated ASM practices can rival the spatial footprint of industrial mines. The unregulated and informal nature of many ASM activities presents monitoring challenges that remote sensing (RS) methods aim to address. While local-scale ASM mapping has seen success, scaling these methods to regional or global levels remains unclear. We review literature on mapping ASM to determine: (1) if studies represent the global distribution and diversity of ASM activities, (2) how ASM's unique characteristics influence the choice of RS methods, and (3) which RS approaches are the most accurate and cost-effective. We found current studies disproportionately focused on ASM regions in Africa, which highlights the need to extend the research to other regions with unique ASM characteristics, such as coal and sand mining in India and China. The selection of RS approaches is heavily influenced by local ASM contexts, the scale of analysis, and resource constraints such as funding for high-resolution imagery and validation data availability. We argue that accurate regional-scale ASM mapping (>100,000 km2) requires innovative combinations of data and methods to overcome data management and storage challenges. Local community participation, including miners, is vital for on-ground mapping and monitoring capacity. We outline a research agenda needed to develop a range of approaches for mapping and monitoring ASM in under-studied regions. By synthesizing effective methods, we provide a foundation for generating accurate and comprehensive spatial data, addressing the issues of inaccurate and incomplete data that global ASM platforms aim to resolve. This spatial data can guide policymakers, NGOs, and businesses in making informed decisions and targeted interventions to improve ASM sector safety, sustainability, and efficiency. Leveraging cloud-based geoprocessing platforms, with regularly updated global satellite image archives, combined with crowd-sourced on-ground information offers a potential solution for sustained regional-scale monitoring.

Design of a Mobile App Interface That Engages Community Members in a Food System Pilot Study.

Supermarkets are scarce in many under-resourced urban communities, and small independently owned retail stores often carry few fresh or healthy items. The Baltimore Urban food Distribution (BUD) mobile application (app) was previously developed to address supply-side challenges in moving healthy foods from local suppliers to retailers. In-app opportunities for consumers to indicate demand for these foods are crucial, but remain absent. We sought to understand community members' perspectives on the overall role, function and features of a proposed consumer-engagement module (BUDConnect) to expand the BUD app. A series of initial high-fidelity wireframe mockups were developed based on formative research. In-depth interviews (n = 20) were conducted and thematically analyzed using ATLAS.ti Web. Participants revealed a desire for real-time crowd-sourced information to navigate their food environments safely and effectively, functionality to help build community and social networks among store owners and their customers, opportunities to share positive reviews and ratings of store quality and offerings, and interoperability with existing apps. Rewards and referral systems resulting in the discounted purchasing of promoted healthy items were suggested to increase adoption and sustained app use. Wireframe mockups were further refined for future development and integration into the BUD app, the program and policy implications of which are discussed.

Leveraging Crowdsourced Activity Information for Transit Stations Flow Estimation

Leveraging Crowdsourced Activity Information for Transit Stations Flow Estimation

“ARE WE DATING THE SAME GUY?”: COLLECTIVE SENSEMAKING AS A MORAL RESPONSIBILITY IN FACEBOOK GROUPS

Hinge to happily ever after is an arduous process in which dating apps place the burden of risk management on users. Women across the U.S. have joined regional “Are We Dating the Same Guy?” private Facebook groups to manage the ambiguities of dating, crowdsource information on men, and provide social support to other women experiencing the tribulations of modern dating. Using discourse analysis, this study analyzes the collective sensemaking practices of AWDTSG groups in relation to constructing knowledge, managing privacy boundaries, content moderation, and providing social support. Preliminary analysis reveals three major findings: Facebook groups provide a space of empowerment, however anticipated acts of moderation shape how knowledge claims are formed and legitimized. Second, group level moderation enacted by admins is understood by members as a means of community protection against platform interventions and interpersonal conflict with the outgroup. Third, social support is an integral part of knowledge creation, resisting cultural logics that socialize women to see each other as competitors in the dating sphere. These findings contribute to our understanding of how ICTs may be used in the service reclaiming gossip as a mode of resistance through the act of collective sensemaking.

The Effect of Crowdsourced Police Enforcement Data on Traffic Speed: A Case Study of The Netherlands

The proliferation of smartphones and internet connectivity has provided the opportunity to use crowdsourced data in traffic management. Nowadays, many people use navigation apps such as Google Maps, Waze, and Flitsmeister to obtain real-time travel information and provide feedback on road conditions, such as reporting police speed checks. As an accurate traffic speed prediction is of great significance for road users and traffic managers, different models have been proposed and widely used to predict traffic speed considering the spatio-temporal dependence of traffic data and external factors such as the weather, accidents and points of interest. This study investigates the impact of crowdsourced data about police enforcement from navigation apps on traffic speed. In addition, we examine whether the police enforcement report affects the accuracy of the deep learning prediction model. The authors extract crowdsourced police enforcement information from navigation apps, collect the corresponding historical traffic speed data, and predict traffic speed in several corridors in The Netherlands using a GCN-GRU traffic speed prediction model. The results show that the crowdsourced data for police enforcement cause the average vehicle speed to drop between 1 [km/h] and 3 [km/h] when passing the road segments marked with police activity. Moreover, the prediction performance of the GCN-GRU model during the periods without police enforcement is better than the periods with reported police activity, showing that police speed check reports can decrease the accuracy of speed prediction models.

Crowdwashing Surveillance; Crowdsourcing Domination

Abstract Governments regularly rely on citizens’ cooperation in exercising their authority, including the enforcement of rules. This is not only common, but also a necessary practice in a legal system. Technology makes such reliance easier, facilitating increased enforcement of law at little cost. Emergency provides an added legitimizing logic, encouraging citizens’ cooperation and leading them to uncritically follow the government’s lead to reduce the risk to the nation and to themselves. This article considers governments’ crowdsourcing citizens to monitor and surveil other citizens. One central concern this practice raises is that it allows governments to circumvent the limits of their legitimate authority and to augment their power while also obscuring the actor responsible for the surveillance and enforcement action. Consequently, accountability and public oversight over the government are diminished. Where does conventional enlisting of cooperation from law-abiding citizens end, and crowdsourcing totalitarian mass mobilization of citizens against fellow citizens begin? The article’s principal claim is that a bright line should be drawn where governments’ crowdsourcing of information from citizens serves as a means to circumvent democratic checks on their power to collect information, while also disguising the actor responsible for the surveillance. Such practice severely erodes social trust between citizens, jeopardizing their ability to organize and collaborate as engaged citizens and thus serve as a check on government. It further grants excessive power to some citizens over others, endangering the latter’s freedom, especially where the information gathered is used to symbolically or actually exclude certain individuals from the political community.

Search & Verify: Misinformation and source evaluations in Internet search results

In response to online misinformation, source rating services evaluate thousands of popular websites, mark them as reputable or disreputable, and provide detailed information explaining their evaluations. In this paper, we examine the effects of news source evaluations by experts as well as crowd-sourced information (i.e., Wikipedia) on beliefs in political news that appears in Internet search results. Through two online experiments, we investigate the effects of providing a simple source-evaluation flag or a flag with more detailed supporting information explaining the evaluation. We found that negative flags from expert fact-checkers reduced users' beliefs, and supporting information had no additional effect; in contrast, positive flags required information beyond the simple flag to have maximum effectiveness. Positive and negative crowd-sourced information had similar effects but was weaker than evaluations from experts. Thus, we conclude that providing additional information to support source evaluation flags is important to give users confidence in reputable websites.

A novel framework to avoid traffic congestion and air pollution for sustainable development of smart cities

Traffic management is crucial for the sustainable development of smart cities. There has been a continuous emphasis from the research community to predict air quality and manage traffic for congestion-free roads. This paper proposes a novel framework to identify traffic congestion and estimate air quality using crowdsourced information. Navigation Reference Spatial Data (NRSD) was created using the spatial likelihood method using GPS trajectories and OpenStreetMap. This NRSD was also used to design the traffic environment using distance, speed, and time interval mapping. After the NRSD creation, the framework used the real-time data of the registered user to compute the traffic density using the Graham scan with the k means algorithm. Eight standard air quality parameters were used to identify the air quality index of a particular node using a Bayesian Classifier. A case analysis was performed on the road network of three major cities of north India lying within a bounding box of (lat:30.518065, lon:76.659055) to (lat: 30.512501, lon:76.658808) was performed to validate the proposed framework. Based on the performed experiment, the proposed framework achieved maximum accuracy of 98 percent. Similarly, NSRD improves the framework's accuracy by 3.8% in comparison to the baseline dataset. In 87 percent of scenarios, this framework can also determine the shortest path by avoiding traffic.

Towards global scale segmentation with OpenStreetMap and remote sensing

Land Use Land Cover (LULC) segmentation is a famous application of remote sensing in an urban environment. Up-to-date and complete data are of major importance in this field. Although with some success, pixel-based segmentation remains challenging because of class variability. Due to the increasing popularity of crowd-sourcing projects, like OpenStreetMap, the need for user-generated content has also increased, providing a new prospect for LULC segmentation. We propose a deep-learning approach to segment objects in high-resolution imagery by using semantic crowdsource information. Due to satellite imagery and crowdsource database complexity, deep learning frameworks perform a significant role. This integration reduces computation and labor costs. Our methods are based on a fully convolutional neural network (CNN) that has been adapted for multi-source data processing. We discuss the use of data augmentation techniques and improvements to the training pipeline. We applied semantic (U-Net) and instance segmentation (Mask R-CNN) methods and, Mask R–CNN showed a significantly higher segmentation accuracy from both qualitative and quantitative viewpoints. The conducted methods reach 91% and 96% overall accuracy in building segmentation and 90% in road segmentation, demonstrating OSM and remote sensing complementarity and potential for city sensing applications.

Exploring Augmented Reality Affordances for Media Literacy in the EFL Context

The ever-evolving information and communication technologies (ICTs) have affected the learners’ preferences for on-demand and on-the-spot access to information that challenge the traditional classroom practices and call for a reconstruction of curricula. New educational approaches are to be encompassed so as to align with the tech-savvy Generation Z needs and the contemporary digitized world that demands competences and skills for successful and well-informed personal and professional choices. Nevertheless, consumption and creation of new knowledge in the plethora of the new crowdsourced information ecosystem have to be critically accessed, analyzed, evaluated, and leveraged so as to lead to creativity and innovation. This paper is a report of the design of an educational intervention with the intent to enhance students’ media literacy skills in the English as a Foreign Language (EFL) secondary education context leveraging immersive technologies. In the first part there is a review of related work leveraging AR affordances in the EFL context. The second part explores the instructional design and pedagogical framework that AR assets can enrich a course material on media literacy for inclusive education practices taking into consideration students’ preferences on their learning process.

GPS spoofing detection via crowd-sourced information for connected vehicles

Modern vehicular systems rely on the Global Positioning System (GPS) technology to provide accurate and timely services. However, the GPS has been proved to be characterized by an intrinsic insecure design, thus being subject to several security attacks. Current solutions can reliably detect GPS spoofing attacks leveraging the physical features of the received GPS signals or resorting to multiple antennas. However, these techniques cannot be deployed when the physical properties of the received signals cannot be accessed, which is the most general case for commercial GPS receivers. Alternative solutions in the literature rely on the cross-check of the received signal with information coming from additional sources. However, such proposals are typically limited to a single source, are rarely supported by experimental results, and do not provide insights on the impact of several parameters, such as detection accuracy, time, false-positives, and robustness to malicious information. To overcome the cited limitations, in this paper, we propose an innovative approach, resorting to combined crowd-sourced information from the mobile cellular infrastructure and the WiFi networks to detect GPS spoofing attacks. Our analysis leverages an extensive experimental dataset, available online for the research community, gathered by driving around a car in urban, suburban, and rural scenarios, for around 5 h and covering more than 196 km. Our solution allows for a tunable tradeoff between detection delay and false positive; for instance, we can detect an attack in approximately 6 s, when leveraging the information coming from only the WiFi, while the delay increases to 30 s when using the information from the mobile cellular network, still achieving a false positive probability strictly less than 0.01. We also show the limitations and trade-offs of our approach, in terms of minimum detection accuracy, time, and robustness to malicious information. The data adopted in this work are publicly released to allow results replicability and foster further research in the highlighted directions.

Enculturating a Community of Action: Health Professions Educators' Perspectives on Teaching With Wikipedia.

Health professions educators are increasingly called on to engage learners in more meaningful instruction. Many have used Wikipedia to offer an applied approach to engage learners, particularly learning related to evidence-based medicine (EBM). However, little is known about the benefits and challenges of using Wikipedia as a pedagogic tool from the collective experience of educators who have sought to improve their instructional practice with it. This study aims to synthesize the perspectives of health professions education (HPE) instructors on the incorporation of Wikipedia editing into their HPE courses. Applying a constructivist approach, the authors conducted semistructured interviews from July to December 2020, with 17 participating HPE instructors who had substantively integrated Wikipedia into their curriculum at 13 institutions. Participants were interviewed about their experiences of integrating Wikipedia editing into their courses. Thematic analysis was conducted on resulting transcripts. The authors observed 2 broad themes among participants' expressed benefits of teaching with Wikipedia. First, Wikipedia provides a meaningful instructional alternative that also helps society and develops learners' information literacy and EBM skills. Second, Wikipedia supports learners' careers and professional identity formation. Identified challenges included high effort and time, restrictive Wikipedia sourcing guidelines, and difficult interactions with stakeholders. Findings build on known benefits, such as providing a real-world collaborative project that contextualizes students' learning experiences. They also echo known challenges, such as the resource-intensive nature of teaching with Wikipedia. The findings of this study reveal the potential of Wikipedia to enculturate HPE students within a situated learning context. They also present implications for HPE programs that are considering implementing Wikipedia and faculty development needed to help instructors harness crowd-sourced information tools' pedagogic opportunities as well as anticipate their challenges.

GOLIATH: A Decentralized Framework for Data Collection in Intelligent Transportation Systems

Intelligent Transportation Systems (ITSs) technology has advanced during the past years, and it is now used for several applications that require vehicles to exchange real-time data, such as in traffic information management. Traditionally, road traffic information has been collected using on-site sensors. However, crowd-sourcing traffic information from onboard sensors or smartphones has become a viable alternative. State-of-the-art solutions currently follow a centralized model where only the service provider has complete access to the collected traffic data and represent a single point of failure and trust. In this paper, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GOLIATH</i> , a blockchain-based decentralized framework that runs on the In-Vehicle Infotainment (IVI) system to collect real-time information exchanged between the network’s participants. Our approach mitigates the limitations of existing crowd-sourcing centralized solutions by guaranteeing trusted information collection and exchange, fully exploiting the intrinsic distributed nature of vehicles. We demonstrate its feasibility in the context of vehicle positioning and traffic information management. Each vehicle participating in the decentralized network shares its position and neighbors’ ones in the form of a transaction recorded on the ledger, which uses a novel consensus mechanism to validate it. We design the consensus mechanism resilient against a realistic set of adversaries that aim to tamper or disable the communication. We evaluate the proposed framework in a simulated (but realistic) environment, which considers different threats and allows showing its robustness and safety properties.

Quora for IPEC Using MERN Stack

Abstract: In Today’s world, technology and communication networks are getting wider day by day. With time and development of technology, people (especially college students) are changing their ways to communicate and educate, they want to stay connected with their peers, seniors, teachers as well as the world too. QUORA FOR IPEC using MERN Stack is a question and answer website which allows users to get connected with people through a digital blogging platform from any distance and consisting of results, blogs, placement records and Question Answer based features to get the latest updates of the environment associated to IPEC. The main focus of this QnA platform is to connect users to their friends, teachers, seniors and peers as well as one can be notified by the Admin to get all incidents and information taking place around the college [1].It's a platform to ask questions and connect with people who contribute unique insights and quality answers [2]. That said, the biggest concern about this type of crowd-sourced information is whether the information given is actually factual, or at least, completely correct. Luckily, many of these woes are eased due to the fact that industry experts, college pass outs and teachers can often use QUORA FOR IPEC for rectifications and to provide answers.

Engaging the Public in Disaster Communication: The Effect of Message Framing on Sharing Intentions for Social Media Posts

ABSTRACT In times of emergency, organizations and members of the public have generated and shared crowdsourced information to help damaged communities. Using the 2018 California Camp Fire as a case study, this study explores how communication interventions influence people’s online message-sharing intentions. Specifically, through the lens of construal-level theory and prospect theory, this study demonstrates the direct and moderate persuasive effects of message framing on sharing intentions for Facebook posts. Using an online experiment with Amazon Mechanical Turk workers (N = 475), this study found that a gain-framed message encourages social media post-sharing intentions. The persuasive power of first-person versus third-person perspective frames differed depending on the use of gain versus loss frames. The discussion highlights the theoretical and practical implications of our findings.

Characterizing negative reviews of orthopedic spine surgeons and practices

BackgroundRecent evidence suggests that patients prefer subjective and crowd-sourced information over data-driven or quality-based outcomes when choosing a surgeon. Online physician rating and review websites continue to increase in popularity, and over half of patients use them to research physicians. Specifically, Yelp.com is the most frequently utilized online resource by patients. Data regarding the characteristics of negative reviews for spine surgeons and practices is lacking. MethodsOrthopedic Spine surgeons and practices in 8 major US metropolitan regions were surveyed for one-star reviews on Yelp.com. The factors noted in the reviews were recorded and they were classified according to their clinical or nonclinical nature. Reviews were also subclassified into nonsurgical or surgical episodes of care. ResultsA total of 6,286 Yelp reviews were discovered, 671 (10.6%) of which were rated one-star. The majority of negative reviews (76.4%) were from patients who did not report surgery by the surgeon or practice. Of all comments, 491 (77.6%) related to nonclinical complaints. The most common factors noted in negative reviews were related to bedside manner, rude or unprofessional staff, and wait time. ConclusionChoosing a surgeon is a complex process for patients. The large majority of negative reviews were related to nonclinical issues such as poor bedside manner or rude staff and most of these were written by patients that did not undergo a surgical procedure. This may explain the large discrepancy that has been observed between quality metrics and online crowd-sourced reviews. Paying attention to these nonclinical factors may represent the most feasible and valuable targets to improve a surgeon's practice and attract future patients.

Livability assessment of 101,630 communities in China's major cities: A remote sensing perspective.

Some of China’s major cities have entered the middle and late stages of urbanization, and the development focus of these cities has gradually shifted from outward expansion to inward renewal. The community, as the basic unit of a city, is undoubtedly the main object of urban renewal. In order to efficiently and effectively address the problems in current community construction, it is necessary to conduct a large-scale in-depth assessment of the community livability, which can directly imply the satisfaction of residents with their quality of life. This study achieved the first comprehensive livability assessment at the individual community scale for 42 major cities of China from the perspective of remotely sensed and crowd-sourced geographic information. Specifically, we produced abundant fine-grained datasets for 42 cities, including high-resolution land cover maps interpreted from Ziyuan-3 satellites (ZY-3, 2.1 m), building height, point-of-interest, and boundaries of 101,630 communities. As designed in our proposed framework, the community livability was evaluated by 5 level-1 indicators, 27 level-2 indicators and an integrated community livability index (CLI). A number of interesting findings were obtained from this assessment: (1) According to the expert questionnaires, living comfort was considered as the most important livability factor for residents with the highest weight, while the building environment was rated the least. The negative factors (e.g., the factories around the community) impacted more on livability than the positive ones. (2) Most communities in major Chinese cities were characterized by dense buildings and sparse green spaces. (3) In these cities, community security construction was severely insufficient, particularly in less developed regions. (4) Imbalanced community livability development was prevalent across cities, and simultaneously, the CLI distribution within cities also exhibited significant spatial aggregation and heterogeneity. This research is expected to reveal the status quo of community livability in China, and thus allow for targeted policy formulation.Electronic Supplementary MaterialSupplementary material is available for this article at 10.1007/s11430-021-9896-4 and is accessible for authorized users.