{"title":"Disentangling the Effects of Social Signals","authors":"T. Hogg, Kristina Lerman","doi":"10.15346/hc.v2i2.4","DOIUrl":"https://doi.org/10.15346/hc.v2i2.4","url":null,"abstract":"Peer recommendation is a crowdsourcing task that leverages the opinions of many to identify interesting content online, such as news, images, or videos. Peer recommendation applications often use social signals, e.g., the number of prior recommendations, to guide people to the more interesting content. How people react to social signals, in combination with content quality and its presentation order, determines the outcomes of peer recommendation, i.e., item popularity. Using Amazon Mechanical Turk, we experimentally measure the effects of social signals in peer recommendation. Specifically, after controlling for variation due to item content and its position, we find that social signals affect item popularity about half as much as position and content do. These effects are somewhat correlated, so social signals exacerbate the \"rich get richer\" phenomenon, which results in a wider variance of popularity. Further, social signals change individual preferences, creating a \"herding\" effect that biases people's judgments about the content. Despite this, we find that social signals improve the efficiency of peer recommendation by reducing the effort devoted to evaluating content while maintaining recommendation quality.","PeriodicalId":92785,"journal":{"name":"Human computation (Fairfax, Va.)","volume":"26 1","pages":"189-208"},"PeriodicalIF":0.0,"publicationDate":"2014-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91311825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Learning to Count","authors":"M. Bateson","doi":"10.15346/HC.V1I1.37","DOIUrl":"https://doi.org/10.15346/HC.V1I1.37","url":null,"abstract":"Editor’s note: this is a reworking of Mary Catherine Bateson’s original piece on \"Making a Difference”, which was the foreword to the Springer Handbook of Human Computation (2013).","PeriodicalId":92785,"journal":{"name":"Human computation (Fairfax, Va.)","volume":"75 1","pages":"95-99"},"PeriodicalIF":0.0,"publicationDate":"2014-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74803584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Toward Complexity Measures for Systems Involving Human Computation","authors":"R. J. Crouser, Benjamin J. Hescott, Remco Chang","doi":"10.15346/HC.V1I1.25","DOIUrl":"https://doi.org/10.15346/HC.V1I1.25","url":null,"abstract":"This paper introduces the Human Oracle Model as a method for characterizing and quantifying the use of human processing power as part of an algorithmic process. The utility of this model is demonstrated through a comparative algorithmic analysis of several well-known human computation systems, as well as the definition of a preliminary characterization of the space of human computation under this model. Through this research, we hope to gain insight about the challenges unique to human computation and direct the search for efficient human computation algorithms.","PeriodicalId":92785,"journal":{"name":"Human computation (Fairfax, Va.)","volume":"175 1","pages":"45-65"},"PeriodicalIF":0.0,"publicationDate":"2014-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79764730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Architecting Real-Time Crowd-Powered Systems","authors":"Walter S. Lasecki, C. Homan, Jeffrey P. Bigham","doi":"10.15346/HC.V1I1.5","DOIUrl":"https://doi.org/10.15346/HC.V1I1.5","url":null,"abstract":"Human computation allows computer systems to leverage human intelligence in computational processes. While it has primarily been used for tasks that are not time-sensitive, recent systems use crowdsourcing to get on-demand, real-time, and even interactive results. In this paper, we present techniques for building real-time crowdsourcing systems, and then discuss how and when to use them. Our goal is to provide system builders with the tools and insights they need to replicate the success of modern systems in order to further explore this new space.","PeriodicalId":92785,"journal":{"name":"Human computation (Fairfax, Va.)","volume":"5 1","pages":"67-93"},"PeriodicalIF":0.0,"publicationDate":"2014-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80993979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The Three Sides of CrowdTruth","authors":"Lora Aroyo, Chris Welty","doi":"10.15346/HC.V1I1.34","DOIUrl":"https://doi.org/10.15346/HC.V1I1.34","url":null,"abstract":"Crowdsourcing is often used to gather annotated data for training and evaluating computational systems that attempt to solve cognitive problems, such as understanding Natural Language sentences. Crowd workers are asked to perform semantic interpretation of sentences to establish a ground truth. This has always been done under the assumption that each task unit, e.g. each sentence, has a single correct interpretation that is contained in the ground truth. We have countered this assumption with CrowdTruth, and have shown that it can be better suited to tasks for which semantic interpretation is subjective. In this paper we investigate the dependence of worker metrics for detecting spam on the quality of sentences in the dataset, and the quality of the target semantics. We show that worker quality metrics can improve significantly when the quality of these other aspects of semantic interpretation are considered.","PeriodicalId":92785,"journal":{"name":"Human computation (Fairfax, Va.)","volume":"1 1","pages":"31-44"},"PeriodicalIF":0.0,"publicationDate":"2014-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89107791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Group Minds and the Case of Wikipedia","authors":"S. Dedeo","doi":"10.15346/hc.v1i1.2","DOIUrl":"https://doi.org/10.15346/hc.v1i1.2","url":null,"abstract":"Group-level cognitive states are widely observed in human social systems, but their discussion is often ruled out a priori in quantitative approaches. In this paper, we show how reference to the irreducible mental states and psychological dynamics of a group is necessary to make sense of large scale social phenomena. We introduce the problem of mental boundaries by reference to a classic problem in the evolution of cooperation. We then provide an explicit quantitative example drawn from ongoing work on cooperation and conflict among Wikipedia editors, showing how some, but not all, effects of individual experience persist in the aggregate. We show the limitations of methodological individualism, and the substantial benefits that come from being able to refer to collective intentions, and attributions of cognitive states of the form \"what the group believes\" and \"what the group values\".","PeriodicalId":92785,"journal":{"name":"Human computation (Fairfax, Va.)","volume":"45 1","pages":"5-29"},"PeriodicalIF":0.0,"publicationDate":"2014-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90908754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Letter from the Editors","authors":"Pietro Michelucci, E. Simperl","doi":"10.15346/hc.v1i2.1","DOIUrl":"https://doi.org/10.15346/hc.v1i2.1","url":null,"abstract":"","PeriodicalId":92785,"journal":{"name":"Human computation (Fairfax, Va.)","volume":"170 1","pages":"101"},"PeriodicalIF":0.0,"publicationDate":"2014-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84006801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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