Clustering is an essential game analysis tool for understanding There are many clustering procedures that could be used player strengths and preferences. For example, clustering to group players based upon their play styles, with k-means techniques have been used to identify player preferences clustering being the most common method. Our use of for using vehicles over direct combat (Drachen et al. 2012), a model-based semi-parametric Bayesian clustering procedure for taking time to solve puzzles over running through content has two important advantages. First, the number of (Drachen, Canossa, and Yannakakis 2009), for understanding clusters (unique player styles) does not have to be prespecified.

Community Question Answering (CQA) websites are a popular tool for internet users to fulfill diverse information needs. Posted questions can be multiple sentences long and span diverse domains. They go beyond factoid questions and can be conversational, opinion-seeking and experiential questions, that might have multiple, potentially conflicting, useful answers from different users. In this paper, we describe a large-scale formative study to collect commonsense properties of questions and answers from 18 diverse communities from stackexchange.com. We collected 50,000 human judgments on 500 question-answer pairs. Commonsense properties are features that humans can extract and characterize reliably by using their commonsense knowledge and native language skills, and no special domain expertise is assumed. We report results and suggestions for designing human computation tasks for collecting commonsense semantic judgments.

This paper examines the importance of presenting simple, intuitive tasks when conducting microtasking on crowdsourcing platforms. Most crowdsourcing platforms allow the maker of a task to present any length of instructions to crowd workers who participate in their tasks. Our experiments show, however, most workers who participate in crowdsourcing microtasks do not read the instructions, even when they are very brief. To facilitate success in microtask design, we highlight the importance of making simple, easy to grasp tasks that do not rely on instructions for explanation.

This paper examines the effect of various modalities of expression on the reliability of crowdsourced sentiment polarity judgments. A novel corpus of YouTube video reviews was created, and sentiment judgments were obtained via Amazon Mechanical Turk. We created a system for isolating text, video, and audio modalities from YouTube videos to ensure that annotators could only see the particular modality or modalities being evaluated. Reliability of judgments was assessed using Fleiss Kappa inter-annotator agreement values. We found that the audio only modality produced the most reliable judgments for video fragments and that across modalities video fragments are less ambiguous than full videos.

We address the problem of acquiring reliable ratings of items such as restaurants or movies from the crowd. We propose a crowdsourcing platform that takes into consideration the workers’ skills with respect to the items being rated and assigns workers the best items to rate. Our platform focuses on acquiring ratings from skilled workers and for items that only have a few ratings. We evaluate the effectiveness of our system using a real-world dataset about restaurants.

The present study explores a method to reduce abusive worker behavior on Amazon Mechanical Turk (AMT), namely reminding workers of moral standards. We manipulated workers’ awareness of moral standards via the presence or the absence of an honesty statement in a survey. The results showed that the honesty statement significantly improved workers’ performance during the first half of the survey. This suggests that a moral reminder is a simple and efficient way to reduce abusive worker behavior in a relatively short survey on AMT.

In this paper we propose a methodology to understand complex concepts, and which captures aspects of the contextual —and collaboratively constructed — meaning of these concepts with considerably less effort than manual coding. We use the word "quality" as one such concept to exemplify our methodology. By using unsupervised topic models along with a small corpus of human labeled data we explore the different uses of the concept "quality" in a large number of blogs. Our methodology is validated, qualitatively, by comparing our results to previous research. Finally, we note limitations and future directions of this work.

Current crowdsourcing platforms such as Amazon Mechanical Turk provide an attractive solution for processing of high-volume tasks at low cost. However, problems of quality control remain a major concern. We developed a private crowdsourcing system (PCSS) running in a intranetwork, that allow us to devise for quality control methods. In the present work, we designed a novel task allocation method to improve accuracy of task results in PCSS. PCSS analyzed relations between tasks from workers' behavior using Bayesian network, then created learning tasks according to analyzed relations. PCSS increased quality of task results by allocating learning tasks to workers before processing difficult tasks. PCSS created 8 learning tasks automatically for 2 target task categories and increased accuracy of task results by 10.77 point on average. We found that creating learning tasks according to analyzed relations is a practical method to improve the quality of workers.

This paper introduces the Tropel system which enables non-technical users to create arbitrary visual detectors without first annotating a training set. Our primary contribution is a crowd active learning pipeline that is seeded with only a single positive example and an unlabeled set of training images. We examine the crowd's ability to train visual detectors given severely limited training themselves. This paper presents a series of experiments that reveal the relationship between worker training, worker consensus and the average precision of detectors trained by crowd-in-the-loop active learning. In order to verify the efficacy of our system, we train detectors for bird species that work nearly as well as those trained on the exhaustively labeled CUB 200 dataset at significantly lower cost and with little effort from the end user. To further illustrate the usefulness of our pipeline, we demonstrate qualitative results on unlabeled datasets containing fashion images and street-level photographs of Paris.

The growing need to analyze large collections of documents has led to great developments in topic modeling. Since documents are frequently associated with other related variables, such as labels or ratings, much interest has been placed on supervised topic models. However, the nature of most annotation tasks, prone to ambiguity and noise, often with high volumes of documents, deem learning under a single-annotator assumption unrealistic or unpractical for most real-world applications. In this paper, we propose a supervised topic model that accounts for the heterogeneity and biases among different annotators that are encountered in practice when learning from crowds. We develop an efficient stochastic variational inference algorithm that is able to scale to very large datasets, and we empirically demonstrate the advantages of the proposed model over state of the art approaches.