Quantifying Social Influence in an Online Cultural Market

Krumme, Coco; Cebrián, Manuel; Pickard, Galen; Pentland, Sandy

doi:10.1371/journal.pone.0033785

Cited by 58 publications

(96 citation statements)

References 14 publications

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“…Salganik et al [38] designed the "MusicLab" experiment, in which they explored how social influence and inherent quality affect a product's market share. In a follow-up study, Krumme et al [26] conceptualized user behaviors as two steps for characterizing how users consume digital items. The first step is based on the appeal of the product, measured by the number of clicks; the second step is based on the quality of the product, measured by post-clicking metrics, e.g., dwell time, comments or shares.…”

Section: Measuring and Predicting Online Attentionmentioning

confidence: 99%

Estimating Attention Flow in Online Video Networks

Rizoiu

Xie

2019

Proc. ACM Hum.-Comput. Interact.

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Online videos have shown tremendous increase in Internet traffic. Most video hosting sites implement recommender systems, which connect the videos into a directed network and conceptually act as a source of pathways for users to navigate. At present, little is known about how human attention is allocated over such large-scale networks, and about the impacts of the recommender systems. In this paper, we first construct the Vevo network -a YouTube video network with 60,740 music videos interconnected by the recommendation links, and we collect their associated viewing dynamics. This results in a total of 310 million views every day over a period of 9 weeks. Next, we present large-scale measurements that connect the structure of the recommendation network and the video attention dynamics. We use the bow-tie structure to characterize the Vevo network and we find that its core component (23.1% of the videos), which occupies most of the attention (82.6% of the views), is made out of videos that are mainly recommended among themselves. This is indicative of the links between video recommendation and the inequality of attention allocation. Finally, we address the task of estimating the attention flow in the video recommendation network. We propose a model that accounts for the network effects for predicting video popularity, and we show it consistently outperforms the baselines. This model also identifies a group of artists gaining attention because of the recommendation network. Altogether, our observations and our models provide a new set of tools to better understand the impacts of recommender systems on collective social attention.

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Section: Measuring and Predicting Online Attentionmentioning

confidence: 99%

Estimating Attention Flow in Online Video Networks

Rizoiu

Xie

2019

Proc. ACM Hum.-Comput. Interact.

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“…sampling) decisions based on popularity. But their evaluations of the songs were not driven by social influence: there was no effect of popularity once sampling was controlled for (Hendricks, Sorensen & Wiseman, 2012;Krumme et al, 2012).…”

Section: Majority Influence and Information Samplingmentioning

confidence: 99%

Information Sampling, Belief Synchronization, and Collective Illusions

Denrell

Mens

2017

Management Science

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We demonstrate that a sampling-based mechanism can offer an alternative explanation for belief synchronization in social groups and the persistence of collective illusions. Our model assumes that people are more likely to sample popular alternatives than unpopular alternatives.We show that this mechanism is sufficient to explain belief synchronization: a strong majority of opinions will likely emerge in favor of one alternative. The reason is that the group is unlikely to move away from a state in which one alternative is very unpopular. If by chance most people come to dislike alternative A, they are all unlikely to sample it again and their opinions of A remain the same. When A is in fact the best alternative, a collective illusion has emerged because people mistakenly believe that a suboptimal alternative is the best. Our model implies that such a collective illusion is persistent. The model thus offers an existence proof that a collective illusion can occur even in settings where people do not infer that popular alternatives are better. The model also casts new light on the effect of online recommendation systems on attitude homogenization and the effect of majority voting on beliefs and attitudes.

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“…Statistical [34], [33], [59], Develop statistical model for quantifying bias: Pólya Urn [34,57], Model [54], [57], [9] nonparametric significance test [33], additive generative model [59], Poisson regression [54], logistic regression [9].…”

Section: Socialmentioning

confidence: 99%

“…There is a plethora of research on detecting and quantifying voter biases in online platforms [51,38,34,43,36,33,59,28,54,57,9,1,23]. Broadly, researchers have adopted one of the following two approaches: 1) conduct experiments to create different voting conditions for studying participants [51,38,43,36,28,1,23]; 2) develop statistical models to analyze historical voting data [34,33,59,54,57,9]. Both approaches have limitations.…”

Section: Introductionmentioning

confidence: 99%

Quantifying Voter Biases in Online Platforms

Dev

Karahalios

Sundaram

2019

Proc. ACM Hum.-Comput. Interact.

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In content-based online platforms, use of aggregate user feedback (say, the sum of votes) is commonplace as the "gold standard" for measuring content quality. Use of vote aggregates, however, is at odds with the existing empirical literature, which suggests that voters are susceptible to different biases-reputation (e.g., of the poster), social influence (e.g., votes thus far), and position (e.g., answer position). Our goal is to quantify, in an observational setting, the degree of these biases in online platforms. Specifically, what are the causal effects of different impression signals-such as the reputation of the contributing user, aggregate vote thus far, and position of content-on a participant's vote on content? We adopt an instrumental variable (IV) framework to answer this question. We identify a set of candidate instruments, carefully analyze their validity, and then use the valid instruments to reveal the effects of the impression signals on votes. Our empirical study using log data from Stack Exchange websites shows that the bias estimates from our IV approach differ from the bias estimates from the ordinary least squares (OLS) method. In particular, OLS underestimates reputation bias (1.6-2.2x for gold badges) and position bias (up to 1.9x for the initial position) and overestimates social influence bias (1.8-2.3x for initial votes). The implications of our work include: redesigning user interface to avoid voter biases; making changes to platforms' policy to mitigate voter biases; detecting other forms of biases in online platforms.

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Quantifying Social Influence in an Online Cultural Market

Cited by 58 publications

References 14 publications

Estimating Attention Flow in Online Video Networks

Estimating Attention Flow in Online Video Networks

Information Sampling, Belief Synchronization, and Collective Illusions

Quantifying Voter Biases in Online Platforms

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