Collective Intelligence and Crowds

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Now showing 1 - 5 of 5
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    Sharing Open Deep Learning Models
    ( 2019-01-08) DALGALI, Ayse ; Crowston, Kevin
    We examine how and why trained deep learning (DL) models are shared, and by whom, and why some developers share their models while others do not. Prior research has examined sharing of data and software code, but DL models are a hybrid of the two. The results from a Qualtrics survey administered to GitHub users and academics who publish on DL show that a diverse population shares DL models, from students to computer/data scientists. We find that motivations for sharing include: increasing citation rates; contributing to the collaboration of developing new DL models; encouraging to reuse; establishing a good reputation; receiving feedback to improve the model; and personal enjoyment. Reasons for not sharing include: lack of time; thinking that their models would not be interesting for others; and not having permission for sharing. The study contributes to our understanding of motivations for participating in a novel form of peer-production.
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    It is Not All Fun and Games: 
Breaking News Consumption on Snapchat
    ( 2019-01-08) Bipat, Taryn ; Wilson, Tom ; Kurniawan, Ostin ; Choi, Yoon Jae (Stephanie) ; Starbird, Kate
    Snapchat is a camera and ephemeral messaging application popular among young adults. Due to its self-destructing content and playful features, Snapchat is often associated with more trivial uses. However, the platform has added functionality to support consumption of news. To understand how users perceive and interact with news content on Snapchat, we conducted semi-structured interviews with 19 users of the platform, focusing on their use of Snapchat during breaking news events, including the 2016/2017 US presidential election and inauguration. Through the lens of Network Gatekeeping, our research explains how users consume breaking news content on Snapchat. We unpack users’ ambiguous perceptions of news reliability on Snapchat, and demonstrate how this contrasts with traditional news consumption. Our research also describes how users’ mental models of how Snapchat works—specifically their theories about how the platform curates news content—shape their judgments of reliability, media bias and authenticity.
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    Crowdsourcing Aimed at Value Innovation
    ( 2019-01-08) Martins, Teresa ; Zambalde, André Luiz ; De Souza Bermejo, Paulo Henrique
    We aims relate the theories of the "Blue Ocean" and "Wisdom of the crowds" to answer: "Can crowdsourcing contribute to the generation of innovation of value?". For this purpose, information on the four businesses used by Howe (2006) to propose the term crowdsourcing: iStockphoto, Web Junk 20, InnoCentive and Amazon Mechanichal Turk - AMT was searched in literature and internet. For each business, we identified the characteristics that allow us to identify them as crowdsourcing nowadays. In this first analysis, it was concluded that, currently, the Web Junk 2J0 would not be classified as crowdsourcing. In a second analysis, we look for the attributes of these businesses capable of generating innovation of value. It was concluded that iStockphoto, Innocentive and AMT have common features that generate value innovation and can be grouped into the Reduce, Eliminate, Rise and Create matrix, according to the "Blue Ocean"
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    Mapping Accessible Paths in the City Using Collective Intelligence
    ( 2019-01-08) Marques, Valmir ; Graeml, Alexandre
    New information and communication technologies (ICTs) have an increasingly stronger role in people's lives, especially after the commoditization of smartphones. They affect many aspects of everyday life, including urban mobility. Some applications, including Waze, benefit from the collective intelligence (CI) of the crowds to gather the information they need to provide users with good advice on the routes to follow. But they are mainly focused on roads and streets, giving little information on the quality of sidewalks, which are essential to pedestrians, people on wheelchairs and blind people. With the intention to improve the mobility of citizens with special needs, we developed the prototype of an application that allows users themselves to update accessibility maps, tagging obstacles and also indicating the existence of resources that contribute to improve the mobility of people with special needs in urban spaces. Tests in a controlled environment helped to debug the application’s functionalities, before members of the intended target group of users were finally exposed to it. Results are promising, as users were able to include relevant data by themselves and seem motivated to keep doing so, due a sense of utility, social facilitation or simply due to altruism, as anticipated by the CI literature. One unexpected outcome was that impaired users are more excited about the potential the application has to give visibility to the challenges they face than with the actual improvement it can bring to their mobility.
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    Introduction to the Minitrack on Collective Intelligence and Crowds
    ( 2019-01-08) Fichman, Pnina ; Nickerson, Jeffrey ; Steiny, Donald