The relationship between financial incentives and performance, long of interest to social scientists, has gained new relevance with the advent of web-based “crowd-sourcing ” models of production. Here we investigate the effect of compensation on performance in the context of two experiments, conducted on Amazon’s Mechanical Turk (AMT). We find that increased financial incentives increase the quantity, but not the quality, of work performed by participants, where the difference appears to be due to an “anchoring ” effect: workers who were paid more also perceived the value of their work to be greater, and thus were no more motivated than workers paid less. In contrast with compensation levels, we find the details of the compensation scheme do matter—specifically, a “quota ” system results in better work for less pay than an equivalent “piece rate ” system. Although counterintuitive, these findings are consistent with previous laboratory studies, and may have real-world analogs as well.

Abstract: What can happen if we combine the best ideas from the Social Web and Semantic Web? The Social Web is an ecosystem of participation, where value is created by the aggregation of many individual user contributions. The Semantic Web is an ecosystem of data, where value is created by the integration of structured data from many sources. What applications can best synthesize the strengths of these two approaches, to create a new level of value that is both rich with human participation and powered by well-structured information? This paper proposes a class of applications called collective knowledge systems, which unlock the "collective intelligence " of the Social Web with knowledge representation and reasoning techniques of the Semantic Web.

Mechanical Turk (MTurk) is an increasingly popular web service for paying people small rewards to do human computation tasks. Current uses of MTurk typically post independent parallel tasks. This paper explores an alternative iterative paradigm, in which workers build on or evaluate each other’s work. We describe TurKit, a new toolkit for deploying iterative tasks to MTurk, with a familiar imperative programming paradigm that effectively uses MTurk workers as subroutines, such as the comparison function of a sorting algorithm. The toolkit handles the latency of MTurk tasks (typically measured in minutes), supports parallel tasks, and provides fault tolerance to avoid wasting money and time. We present a variety of iterative experiments using TurKit, including image description, copy editing, handwriting recognition, and sorting. ACM Classification: H5.2 [Information interfaces and

Robust low-level image features have been proven to be effective representations for a variety of visual recognition tasks such as object recognition and scene classification; but pixels, or even local image patches, carry little semantic meanings. For high level visual tasks, such low-level image representations are potentially not enough. In this paper, we propose a high-level image representation, called the Object Bank, where an image is represented as a scale-invariant response map of a large number of pre-trained generic object detectors, blind to the testing dataset or visual task. Leveraging on the Object Bank representation, superior performances on high level visual recognition tasks can be achieved with simple off-the-shelf classifiers such as logistic regression and linear SVM. Sparsity algorithms make our representation more efficient and scalable for large scene datasets, and reveal semantically meaningful feature patterns. 1

Amazon Mechanical Turk (MTurk) is a crowdsourcing system in which tasks are distributed to a population of thousands of anonymous workers for completion. This system is increasingly popular with researchers and developers. Here we extend previous studies of the demographics and usage behaviors of MTurk workers. We describe how the worker population has changed over time, shifting from a primarily moderate-income, U.S.-based workforce towards an increasingly international group with a significant population of young, well-educated Indian workers. This change in population points to how workers may treat Turking as a full-time job, which they rely on to make ends meet.

Since its introduction at CHI 2004, the ESP Game has inspired many similar games that share the goal of gathering data from players. This paper introduces a new mechanism for collecting labeled data using “games with a purpose. ” In this mechanism, players are provided with either the same or a different object, and asked to describe that object to each other. Based on each other’s descriptions, players must decide whether they have the same object or not. We explain why this new mechanism is superior for input data with certain characteristics, introduce an enjoyable new game called “TagATune ” that collects tags for music clips via this mechanism, and present findings on the data that is collected by this game.

Query-by-semantic-description (QBSD) is a natural paradigm for retrieving content from large databases of music. A major impediment to the development of good QBSD systems for music information retrieval has been the lack of a cleanlylabeled, publicly-available, heterogeneous data set of songs and associated annotations. We have collected the Computer Audition Lab 500-song (CAL500) data set by having humans listen to and annotate songs using a survey designed to capture ‘semantic associations ’ between music and words. We adapt the supervised multi-class labeling (SML) model, which has shown good performance on the task of image retrieval, and use the CAL500 data to learn a model for music retrieval. The model parameters are estimated using the weighted mixture hierarchies expectation-maximization algorithm which has been specifically designed to handle realvalued semantic association between words and songs, rather than binary class labels. The output of the SML model, a vector of class-conditional probabilities, can be interpreted as a semantic multinomial distribution over a vocabulary. By also representing a semantic query as a query multinomial distribution, we can quickly rank order the songs in a database based on the Kullback-Leibler divergence between the query multinomial and each song’s semantic multinomial. Qualitative and quantitative results demonstrate that our SML model can both annotate a novel song with meaningful words and retrieve relevant songs given a multi-word, text-based query.

Although existing work has explored both information extraction and community content creation, most research has focused on them in isolation. In contrast, we see the greatest leverage in the synergistic pairing of these methods as two interlocking feedback cycles. This paper explores the potential synergy promised if these cycles can be made to accelerate each other by exploiting the same edits to advance both community content creation and learning-based information extraction. We examine our proposed synergy in the context of Wikipedia infoboxes and the Kylin information extraction system. After developing and refining a set of interfaces to present the verification of Kylin extractions as a non-primary task in the context of Wikipedia articles, we develop an innovative use of Web search advertising services to study people engaged in some other primary task. We demonstrate our proposed synergy by analyzing our deployment from two complementary perspectives: (1) we show we accelerate community content creation by using Kylin’s information extraction to significantly increase the likelihood that a person visiting a Wikipedia article as a part of some other primary task will spontaneously choose to help improve the article’s infobox, and (2) we show we accelerate information extraction by using contributions collected from people interacting with our designs to significantly improve Kylin’s extraction performance. ACM Classification:

Distributed Human Computation (DHC) holds great promise for using computers and humans together to scaling up the kinds of tasks that only humans do well. Currently, the literature describing DHC efforts so far is segmented. Projects that stem from different perspectives frequently do not cite each other. This can be especially problematic for researchers trying to understand the current body of work in order to push forward with new ideas. Also, as DHC matures into a standard topic within humancomputer interaction and computer science, educators will require a common vocabulary to teach from. As a starting point, we offer a taxonomy which classifies and compares DHC systems and ideas. We describe the key characteristics and compare and contrast the differing approaches.

Abstract. We present a simple game-theoretic model for the ESP game, an interactive game devised to label images on the web, and characterize the equilibrium behavior of the model. We show that a simple change in the incentive structure can lead to different equilibrium structure and suggest the possibility of formal incentive design in achieving desirable system-wide outcomes, complementing existing considerations of robustness against cheating and human factors. 1