Crowdsourcing as a Business Model: An Exploration of Emergent Textbooks Harnessing the Wisdom of Crowds

The process of writing textbooks is still very traditional regarding the status of authorship and expert opinions. Recently we observe the emergence of authors who follow a different approach, taping the wisdom of crowds as key resource of their own publications. In this paper, we explore business model innovation which leverages value propositions of textbooks by applying crowdsourcing. We use case study research methods to analyze four textbooks written collaboratively. Essential findings indicate occurrence of user-communities fulfilling peer-reviewing, editing or co-authoring despite a lack of monetary incentives. We further detect a tendency towards wiki software providing a community hub. This paper enters the field of partially crowdsourced textbooks and derives future questions of research

Defecation, littering and other acts of public disturbance in pandemic times – A study of a Scandinavian city

The spatiotemporal patterns of public disturbance acts are investigated in Stockholm, the capital of Sweden. Using crowdsourced data, the number of records is compared 15 months before and after the stay-at-home measures of the COVID-19 pandemic, controlling for seasonal trends. Poisson-Gamma-CAR regression models are implemented to assess the potential impact of land use on the spatial distribution of public disturbance acts, accounting for the effect of pandemic restrictions and differences in neighborhood context. Findings show that, with the exception of abandoned vehicles, there was a significant increase in records of public disturbance after the 2020 stay-at-home pandemic restrictions. Parks, transport hubs and less importantly, schools were significantly associated with public disturbances, controlling for neighborhood characteristics and reporting practices. Recommendations are made for research and practice

Human Computation and Convergence

Humans are the most effective integrators and producers of information, directly and through the use of information-processing inventions. As these inventions become increasingly sophisticated, the substantive role of humans in processing information will tend toward capabilities that derive from our most complex cognitive processes, e.g., abstraction, creativity, and applied world knowledge. Through the advancement of human computation - methods that leverage the respective strengths of humans and machines in distributed information-processing systems - formerly discrete processes will combine synergistically into increasingly integrated and complex information processing systems. These new, collective systems will exhibit an unprecedented degree of predictive accuracy in modeling physical and techno-social processes, and may ultimately coalesce into a single unified predictive organism, with the capacity to address societies most wicked problems and achieve planetary homeostasis.Comment: Pre-publication draft of chapter. 24 pages, 3 figures; added references to page 1 and 3, and corrected typ

Disagreeable Privacy Policies: Mismatches between Meaning and Users’ Understanding

Privacy policies are verbose, difficult to understand, take too long to read, and may be the least-read items on most websites even as users express growing concerns about information collection practices. For all their faults, though, privacy policies remain the single most important source of information for users to attempt to learn how companies collect, use, and share data. Likewise, these policies form the basis for the self-regulatory notice and choice framework that is designed and promoted as a replacement for regulation. The underlying value and legitimacy of notice and choice depends, however, on the ability of users to understand privacy policies. This paper investigates the differences in interpretation among expert, knowledgeable, and typical users and explores whether those groups can understand the practices described in privacy policies at a level sufficient to support rational decision-making. The paper seeks to fill an important gap in the understanding of privacy policies through primary research on user interpretation and to inform the development of technologies combining natural language processing, machine learning and crowdsourcing for policy interpretation and summarization. For this research, we recruited a group of law and public policy graduate students at Fordham University, Carnegie Mellon University, and the University of Pittsburgh (“knowledgeable users”) and presented these law and policy researchers with a set of privacy policies from companies in the e-commerce and news & entertainment industries. We asked them nine basic questions about the policies’ statements regarding data collection, data use, and retention. We then presented the same set of policies to a group of privacy experts and to a group of non-expert users. The findings show areas of common understanding across all groups for certain data collection and deletion practices, but also demonstrate very important discrepancies in the interpretation of privacy policy language, particularly with respect to data sharing. The discordant interpretations arose both within groups and between the experts and the two other groups. The presence of these significant discrepancies has critical implications. First, the common understandings of some attributes of described data practices mean that semi-automated extraction of meaning from website privacy policies may be able to assist typical users and improve the effectiveness of notice by conveying the true meaning to users. However, the disagreements among experts and disagreement between experts and the other groups reflect that ambiguous wording in typical privacy policies undermines the ability of privacy policies to effectively convey notice of data practices to the general public. The results of this research will, consequently, have significant policy implications for the construction of the notice and choice framework and for the US reliance on this approach. The gap in interpretation indicates that privacy policies may be misleading the general public and that those policies could be considered legally unfair and deceptive. And, where websites are not effectively conveying privacy policies to consumers in a way that a “reasonable person” could, in fact, understand the policies, “notice and choice” fails as a framework. Such a failure has broad international implications since websites extend their reach beyond the United States

Interpretable Classification of Wiki-Review Streams

Wiki articles are created and maintained by a crowd of editors, producing a continuous stream of reviews. Reviews can take the form of additions, reverts, or both. This crowdsourcing model is exposed to manipulation since neither reviews nor editors are automatically screened and purged. To protect articles against vandalism or damage, the stream of reviews can be mined to classify reviews and profile editors in real-time. The goal of this work is to anticipate and explain which reviews to revert. This way, editors are informed why their edits will be reverted. The proposed method employs stream-based processing, updating the profiling and classification models on each incoming event. The profiling uses side and content-based features employing Natural Language Processing, and editor profiles are incrementally updated based on their reviews. Since the proposed method relies on self-explainable classification algorithms, it is possible to understand why a review has been classified as a revert or a non-revert. In addition, this work contributes an algorithm for generating synthetic data for class balancing, making the final classification fairer. The proposed online method was tested with a real data set from Wikivoyage, which was balanced through the aforementioned synthetic data generation. The results attained near-90 % values for all evaluation metrics (accuracy, precision, recall, and F-measure).info:eu-repo/semantics/publishedVersio

"Enlisting 'Vertues Noble & Excelent': Behavior, Credit, and Knowledge Organization in the Social Edition"

A part of the special issue of DHQ on feminisms and digital humanities, this paper takes as its starting place Greg Crane’s exhortation that there is a "need to shift from lone editorials and monumental editions to editors ... who coordinate contributions from many sources and oversee living editions." In response to Crane, the exploration of the "living edition" detailed here examines the process of creating a publicly editable edition and considers what that edition, the process by which it was built, and the platform in which it was produced means for editions that support and promote gender equity. Drawing on the scholarship about the culture of the Wikimedia suite of projects, and the gendered trolling experienced by members of our team in the production of the Social Edition of the Devonshire Manuscript in Wikibooks, and interviews with our advisory group, we argue that while the Wikimedia projects are often openly hostile online spaces, the Wikimedia suite of projects are so important to the contemporary circulation of knowledge, that the key is to encourage gender equity in social behavior, credit sharing, and knowledge organization in Wikimedia, rather than abandon it for a more controlled collaborative environment for edition production and dissemination

Spam elimination and bias correction : ensuring label quality in crowdsourced tasks.

Crowdsourcing is proposed as a powerful mechanism for accomplishing large scale tasks via anonymous workers online. It has been demonstrated as an effective and important approach for collecting labeled data in application domains which require human intelligence, such as image labeling, video annotation, natural language processing, etc. Despite the promises, one big challenge still exists in crowdsourcing systems: the difficulty of controlling the quality of crowds. The workers usually have diverse education levels, personal preferences, and motivations, leading to unknown work performance while completing a crowdsourced task. Among them, some are reliable, and some might provide noisy feedback. It is intrinsic to apply worker filtering approach to crowdsourcing applications, which recognizes and tackles noisy workers, in order to obtain high-quality labels. The presented work in this dissertation provides discussions in this area of research, and proposes efficient probabilistic based worker filtering models to distinguish varied types of poor quality workers. Most of the existing work in literature in the field of worker filtering either only concentrates on binary labeling tasks, or fails to separate the low quality workers whose label errors can be corrected from the other spam workers (with label errors which cannot be corrected). As such, we first propose a Spam Removing and De-biasing Framework (SRDF), to deal with the worker filtering procedure in labeling tasks with numerical label scales. The developed framework can detect spam workers and biased workers separately. The biased workers are defined as those who show tendencies of providing higher (or lower) labels than truths, and their errors are able to be corrected. To tackle the biasing problem, an iterative bias detection approach is introduced to recognize the biased workers. The spam filtering algorithm proposes to eliminate three types of spam workers, including random spammers who provide random labels, uniform spammers who give same labels for most of the items, and sloppy workers who offer low accuracy labels. Integrating the spam filtering and bias detection approaches into aggregating algorithms, which infer truths from labels obtained from crowds, can lead to high quality consensus results. The common characteristic of random spammers and uniform spammers is that they provide useless feedback without making efforts for a labeling task. Thus, it is not necessary to distinguish them separately. In addition, the removal of sloppy workers has great impact on the detection of biased workers, with the SRDF framework. To combat these problems, a different way of worker classification is presented in this dissertation. In particular, the biased workers are classified as a subcategory of sloppy workers. Finally, an ITerative Self Correcting - Truth Discovery (ITSC-TD) framework is then proposed, which can reliably recognize biased workers in ordinal labeling tasks, based on a probabilistic based bias detection model. ITSC-TD estimates true labels through applying an optimization based truth discovery method, which minimizes overall label errors by assigning different weights to workers. The typical tasks posted on popular crowdsourcing platforms, such as MTurk, are simple tasks, which are low in complexity, independent, and require little time to complete. Complex tasks, however, in many cases require the crowd workers to possess specialized skills in task domains. As a result, this type of task is more inclined to have the problem of poor quality of feedback from crowds, compared to simple tasks. As such, we propose a multiple views approach, for the purpose of obtaining high quality consensus labels in complex labeling tasks. In this approach, each view is defined as a labeling critique or rubric, which aims to guide the workers to become aware of the desirable work characteristics or goals. Combining the view labels results in the overall estimated labels for each item. The multiple views approach is developed under the hypothesis that workers\u27 performance might differ from one view to another. Varied weights are then assigned to different views for each worker. Additionally, the ITSC-TD framework is integrated into the multiple views model to achieve high quality estimated truths for each view. Next, we propose a Semi-supervised Worker Filtering (SWF) model to eliminate spam workers, who assign random labels for each item. The SWF approach conducts worker filtering with a limited set of gold truths available as priori. Each worker is associated with a spammer score, which is estimated via the developed semi-supervised model, and low quality workers are efficiently detected by comparing the spammer score with a predefined threshold value. The efficiency of all the developed frameworks and models are demonstrated on simulated and real-world data sets. By comparing the proposed frameworks to a set of state-of-art methodologies, such as expectation maximization based aggregating algorithm, GLAD and optimization based truth discovery approach, in the domain of crowdsourcing, up to 28.0% improvement can be obtained for the accuracy of true label estimation

Opportunities and challenges of geospatial analysis for promoting urban livability in the era of big data and machine learning

Urban systems involve a multitude of closely intertwined components, which are more measurable than before due to new sensors, data collection, and spatio-temporal analysis methods. Turning these data into knowledge to facilitate planning efforts in addressing current challenges of urban complex systems requires advanced interdisciplinary analysis methods, such as urban informatics or urban data science. Yet, by applying a purely data-driven approach, it is too easy to get lost in the ‘forest’ of data, and to miss the ‘trees’ of successful, livable cities that are the ultimate aim of urban planning. This paper assesses how geospatial data, and urban analysis, using a mixed methods approach, can help to better understand urban dynamics and human behavior, and how it can assist planning efforts to improve livability. Based on reviewing state-of-the-art research the paper goes one step further and also addresses the potential as well as limitations of new data sources in urban analytics to get a better overview of the whole ‘forest’ of these new data sources and analysis methods. The main discussion revolves around the reliability of using big data from social media platforms or sensors, and how information can be extracted from massive amounts of data through novel analysis methods, such as machine learning, for better-informed decision making aiming at urban livability improvement

Social Learning Systems: The Design of Evolutionary, Highly Scalable, Socially Curated Knowledge Systems

In recent times, great strides have been made towards the advancement of automated reasoning and knowledge management applications, along with their associated methodologies. The introduction of the World Wide Web peaked academicians’ interest in harnessing the power of linked, online documents for the purpose of developing machine learning corpora, providing dynamical knowledge bases for question answering systems, fueling automated entity extraction applications, and performing graph analytic evaluations, such as uncovering the inherent structural semantics of linked pages. Even more recently, substantial attention in the wider computer science and information systems disciplines has been focused on the evolving study of social computing phenomena, primarily those associated with the use, development, and analysis of online social networks (OSN\u27s). This work followed an independent effort to develop an evolutionary knowledge management system, and outlines a model for integrating the wisdom of the crowd into the process of collecting, analyzing, and curating data for dynamical knowledge systems. Throughout, we examine how relational data modeling, automated reasoning, crowdsourcing, and social curation techniques have been exploited to extend the utility of web-based, transactional knowledge management systems, creating a new breed of knowledge-based system in the process: the Social Learning System (SLS). The key questions this work has explored by way of elucidating the SLS model include considerations for 1) how it is possible to unify Web and OSN mining techniques to conform to a versatile, structured, and computationally-efficient ontological framework, and 2) how large-scale knowledge projects may incorporate tiered collaborative editing systems in an effort to elicit knowledge contributions and curation activities from a diverse, participatory audience