292 research outputs found

    Sequential Voting Promotes Collective Discovery in Social Recommendation Systems

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    One goal of online social recommendation systems is to harness the wisdom of crowds in order to identify high quality content. Yet the sequential voting mechanisms that are commonly used by these systems are at odds with existing theoretical and empirical literature on optimal aggregation. This literature suggests that sequential voting will promote herding---the tendency for individuals to copy the decisions of others around them---and hence lead to suboptimal content recommendation. Is there a problem with our practice, or a problem with our theory? Previous attempts at answering this question have been limited by a lack of objective measurements of content quality. Quality is typically defined endogenously as the popularity of content in absence of social influence. The flaw of this metric is its presupposition that the preferences of the crowd are aligned with underlying quality. Domains in which content quality can be defined exogenously and measured objectively are thus needed in order to better assess the design choices of social recommendation systems. In this work, we look to the domain of education, where content quality can be measured via how well students are able to learn from the material presented to them. Through a behavioral experiment involving a simulated massive open online course (MOOC) run on Amazon Mechanical Turk, we show that sequential voting systems can surface better content than systems that elicit independent votes.Comment: To be published in the 10th International AAAI Conference on Web and Social Media (ICWSM) 201

    The Socio-Psychological Dynamics of Conspiracy Theories: Is "Q" a Warning Sign for the Future?

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    Conspiracy theories have been surging worldwide since the beginning of the COVID-19 pandemic. Not only can they have considerable negative impact on a societal level, they are also capable of disrupting individual lives. Along commonly asked questions, this extended factsheet provides an overview of socio-psychological theories that explain belief in conspiracy theories in general. This framework is then applied to empirical data on the QAnon conspiracy movement in order to illustrate theoretical assumptions. After a brief introduction of the concept of conspiracy mindset and related demographic groups, the focus is on the fulfillment of epistemic, existential and social motives from a multitude of perspectives: media landscapes, communities, ideological structures, addiction, and gamification. The factsheet is concluded with a variety of options for prevention and mitigation, and a discussion on the implications for the future of society in the context of deep fakes and the post-truth world

    Quantifying the Impact of Cognitive Biases in Question-Answering Systems

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    Crowdsourcing can identify high-quality solutions to problems; however, individual decisions are constrained by cognitive biases. We investigate some of these biases in an experimental model of a question-answering system. In both natural and controlled experiments, we observe a strong position bias in favor of answers appearing earlier in a list of choices. This effect is enhanced by three cognitive factors: the attention an answer receives, its perceived popularity, and cognitive load, measured by the number of choices a user has to process. While separately weak, these effects synergistically amplify position bias and decouple user choices of best answers from their intrinsic quality. We end our paper by discussing the novel ways we can apply these findings to substantially improve how high-quality answers are found in question-answering systems.Comment: 9 pages, 5 figure
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