Curiosity Killed the Cat, but Makes Crowdwork Better

Crowdsourcing systems are designed to elicit help from humans to accomplish tasks that are still difficult for computers. How to motivate workers to stay longer and/or perform better in crowdsourcing systems is a critical question for designers. Previous work have explored different motivational frameworks, both extrinsic and intrinsic. In this work, we examine the potential for curiosity as a new type of intrinsic motivational driver to incentivize crowd workers. We design crowdsourcing task interfaces that explicitly incorporate mechanisms to induce curiosity and conduct a set of experiments on Amazon’s Mechanical Turk. Our experiment results show that curiosity interventions improve worker retention without degrading performance, and the magnitude of the effects are influenced by both the personal characteristics of the worker and the nature of the task.Engineering and Applied Science

A Glimpse Far into the Future: Understanding Long-term Crowd Worker Quality

Microtask crowdsourcing is increasingly critical to the creation of extremely large datasets. As a result, crowd workers spend weeks or months repeating the exact same tasks, making it necessary to understand their behavior over these long periods of time. We utilize three large, longitudinal datasets of nine million annotations collected from Amazon Mechanical Turk to examine claims that workers fatigue or satisfice over these long periods, producing lower quality work. We find that, contrary to these claims, workers are extremely stable in their quality over the entire period. To understand whether workers set their quality based on the task's requirements for acceptance, we then perform an experiment where we vary the required quality for a large crowdsourcing task. Workers did not adjust their quality based on the acceptance threshold: workers who were above the threshold continued working at their usual quality level, and workers below the threshold self-selected themselves out of the task. Capitalizing on this consistency, we demonstrate that it is possible to predict workers' long-term quality using just a glimpse of their quality on the first five tasks.Comment: 10 pages, 11 figures, accepted CSCW 201

Friendly Hackers to the Rescue: How Organizations Perceive Crowdsourced Vulnerability Discovery

Over the past years, crowdsourcing has increasingly been used for the discovery of vulnerabilities in software. While some organizations have extensively used crowdsourced vulnerability discovery, other organizations have been very hesitant in embracing this method. In this paper, we report the results of a qualitative study that reveals organizational concerns and fears in relation to crowdsourced vulnerability discovery. The study is based on 36 key informant interviews with various organizations. The study reveals a set of pre-adoption fears (i.e., lacking managerial expertise, low quality submissions, distrust in security professionals, cost escalation, lack of motivation of security professionals) as well as the post-adoption issues actually experienced. The study also identifies countermeasures that adopting organizations have used to mitigate fears and minimize issues. Implications for research and practice are discussed

Toward the Optimized Crowdsourcing Strategy for OCR Post-Correction

Digitization of historical documents is a challenging task in many digital humanities projects. A popular approach for digitization is to scan the documents into images, and then convert images into text using Optical Character Recognition (OCR) algorithms. However, the outcome of OCR processing of historical documents is usually inaccurate and requires post-processing error correction. This study investigates how crowdsourcing can be utilized to correct OCR errors in historical text collections, and which crowdsourcing methodology is the most effective in different scenarios and for various research objectives. A series of experiments with different micro-task's structures and text lengths was conducted with 753 workers on the Amazon's Mechanical Turk platform. The workers had to fix OCR errors in a selected historical text. To analyze the results, new accuracy and efficiency measures have been devised. The analysis suggests that in terms of accuracy, the optimal text length is medium (paragraph-size) and the optimal structure of the experiment is two-phase with a scanned image. In terms of efficiency, the best results were obtained when using longer text in the single-stage structure with no image. The study provides practical recommendations to researchers on how to build the optimal crowdsourcing task for OCR post-correction. The developed methodology can also be utilized to create golden standard historical texts for automatic OCR post-correction. This is the first attempt to systematically investigate the influence of various factors on crowdsourcing-based OCR post-correction and propose an optimal strategy for this process.Comment: 25 pages, 12 figures, 1 tabl

Towards measuring states of epistemic curiosity through electroencephalographic signals

International audienceUnderstanding the neurophysiological mechanisms underlying curiosity and therefore being able to identify the curiosity level of a person, would provide useful information for researchers and designers in numerous fields such as neuroscience, psychology, and computer science. A first step to uncovering the neural correlates of curiosity is to collect neurophysiological signals during states of curiosity, in order to develop signal processing and machine learning (ML) tools to recognize the curious states from the non-curious ones. Thus, we ran an experiment in which we used electroencephalography (EEG) to measure the brain activity of participants as they were induced into states of curiosity, using trivia question and answer chains. We used two ML algorithms, i.e. Filter Bank Common Spatial Pattern (FBCSP) coupled with a Linear Discriminant Algorithm (LDA), as well as a Filter Bank Tangent Space Classifier (FBTSC), to classify the curious EEG signals from the non-curious ones. Global results indicate that both algorithms obtained better performances in the 3-to-5s time windows, suggesting an optimal time window length of 4 seconds (63.09% classification accuracy for the FBTSC, 60.93% classification accuracy for the FBCSP+LDA) to go towards curiosity states estimation based on EEG signals

Characterizing Novelty as a Motivator in Online Citizen Science

Citizen science projects rely on the voluntary contribution of nonscientists to take part in scientific research projects. Projects taking place exclusively over the Internet face significant challenges, chief among them is the attracting and keeping the critical mass of volunteers needed to conduct the work outlined by the science team. The extent to which platforms can design experiences that positively influence volunteers’ motivation can help address the contribution challenges. Consequently, project organizers need to develop strategies to attract new participants and keep existing ones. One strategy to encourage participation is implementing features, which re-enforce motives known to change people’s attitudes towards contributing positively. The literature in psychology noted that novelty is an attribute of objects and environments that occasion curiosity in humans leading to exploratory behaviors, e.g., prolonged engagement with the object or environment. This dissertation described the design, implementation, and evaluation of an experiment conducted in three online citizen science projects. Volunteers received novelty cues when they classified data objects that no other volunteer had previously seen. The hypothesis was that exposure to novelty cues while classifying data positively influences motivational attitudes leading to increased engagement in the classification task and increased retention. The experiments resulted in mixed results. In some projects, novelty cues were universally salient, and in other projects, novelty cues had no significant impact on volunteers’ contribution behaviors. The results, while mixed, are promising since differences in the observed behaviors arise because of individual personality differences and the unique attributes found in each project setting. This research contributes to empirically grounded studies on motivation in citizen science with analyses that produce new insights and questions into the functioning of novelty and its impact on volunteers’ behaviors

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Design for Curiosity: A Study of Visual Design Elements, Interaction, and Motivation

The field of Human Computer Interaction (HCI) has traditionally focused on the usability of a system, but as increasing numbers of interactive products become entwined in our daily lives, so does the opportunity to understand user impacts that reach beyond usability. In particular, interaction design, a subdomain of HCI, expands the focus of HCI by looking at the aesthetic impacts a system may have on user emotions. Curiosity is one such emotion that tends to induce information-seeking and motivational behaviors. An experimental study was undertaken to determine whether an interactive, front-end graphic that incorporated curiosity principles in its design would sufficiently pique a participating university faculty’s curiosity to interact with the graphic, and thereafter, with an existing platform named George that was developed to motivate faculty to engage in collaborative behavior. George includes capabilities for creating and storing individual faculty trading cards that include the faculty’s photograph, personal interests, research interests, and publication domains. The experimental graphic provided interactive capabilities to incrementally reveal segments of the photograph and to acquire information about the faculty’s research profile. The number of a study participant’s interactions with the graphic was limited by software. The data collected included the location and frequency of interactions with the graphic, and whether participants ultimately accessed the George platform. Statistically significant evidence demonstrated that the curiosity-provoking principles motivated interaction with the graphic, and that participants were also motivated to access George