Consumers and Curators: Browsing and Voting Patterns on Reddit

Glenski, Maria; Pennycuff, Corey; Weninger, Tim

doi:10.1109/tcss.2017.2742242

Cited by 70 publications

(58 citation statements)

References 27 publications

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“…Hessel et al [12] investigated the interactions between highly related communities and found that users engaged in a newer community tend to be more active in their original community. In [11], the authors studied the browsing and voting behavior of Reddit users and found that most users do not read the article that they vote on. An extensive survey of recent research on Reddit is provided in [26].…”

Section: Related Workmentioning

confidence: 99%

Incentivized Blockchain-based Social Media Platforms

Palanisamy

2019

Proceedings of the 10th ACM Conference on Web Science

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Advances in Blockchain and distributed ledger technologies are driving the rise of incentivized social media platforms over Blockchains, where no single entity can take control of the information and users can receive cryptocurrency as rewards for creating or curating high-quality contents. This paper presents an empirical analysis of Steemit, a key representative of these emerging platforms, to understand and evaluate the actual level of decentralization and the practical effects of cryptocurrency-driven reward system in these modern social media platforms. Similar to Bitcoin, Steemit is operated by a decentralized community, where 21 members are periodically elected to cooperatively operate the platform through the Delegated Proof-of-Stake (DPoS) consensus protocol. Our study performed on 539 million operations performed by 1.12 million Steemit users during the period 2016/03 to 2018/08 reveals that the actual level of decentralization in Steemit is far lower than the ideal level, indicating that the DPoS consensus protocol may not be a desirable approach for establishing a highly decentralized social media platform. In Steemit, users create contents as posts which get curated based on votes from other users. The platform periodically issues cryptocurrency as rewards to creators and curators of popular posts. Although such a reward system is originally driven by the desire to incentivize users to contribute to high-quality contents, our analysis of the underlying cryptocurrency transfer network on the blockchain reveals that more than 16% transfers of cryptocurrency in Steemit are sent to curators suspected to be bots and also finds the existence of an underlying supply network for the bots, both suggesting a significant misuse of the current reward system in Steemit. Our study is designed to provide insights on the current state of this emerging blockchain-based social media platform including the effectiveness of its design and the operation of the consensus protocols and the reward system.

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Section: Related Workmentioning

confidence: 99%

Incentivized Blockchain-based Social Media Platforms

Palanisamy

2019

Proceedings of the 10th ACM Conference on Web Science

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“…A widely popular example for a public forum is Reddit which is diverse in terms of topics and rich in terms of both quantity and quality and has been a popular forum for studying its features see, e.g., references [36,37,38,39]. Further examples include Naira-land which is a forum for discussion on a variety of topics from food to politics, and The Wholesale Forums (TWF) which is a marketplace for wholesalers, importers, drop shippers, retailers and trade buyers.…”

Section: Forumsmentioning

confidence: 99%

Differences in structure and dynamics of networks retrieved from dark and public web forums

Zamani

Rabbani

Horicsányi

et al. 2019

Physica A: Statistical Mechanics and its Applications

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Humans make decisions based on the information they obtain from several major sources, among which the comments of others in Internet forums play an increasing role. Such forums cover a wide spectrum of topics and represent an essential tool in choosing the best products, manipulating views or optimizing our decisions regarding a number of aspects of our everyday life. However, many forums have extremely controversial topics and contents including those which radicalize the readers or spread information about dangerous products and ideas (e.g., drugs, weapons or aggressive ideologies). These just mentioned activities are taking place mainly on the so called dark web allowing the hiding of the identity of members using dark forums. We use network theoretical approaches to analyze the data we obtained by studying the connectivity features of the members and the threads within a wide selection of forums (including dark and semi-dark) and establish several characteristic behavioral patterns. Our findings reveal both common and rather different features in the two types of behavior. In particular, we show that the various distributions of quantities, like the activity of the commenters, the dynamics of the threads (defined using their lifetime) or the degree distributions corresponding to the three major types of forums we have investigated display characteristic deviations. This knowledge can be useful, for example, in identifying an activity typical for the dark web when it appears in the public web (since the public web can be accessed and used much more easily).

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“…Voting Behavior. Recent research has made significant advancements towards the understanding of rating and voting behaviors in online platforms [19,53,56,21,22]. Gilbert [19] reported the widespread underprovisioning of votes on Reddit: the users overlooked 52% of the most popular links the first time they were submitted.…”

Section: Positionmentioning

confidence: 99%

“…Instead, the decision to vote and the polarity of vote depend on the context: a review receives more votes if it is misranked, and the polarity of votes becomes more positive/negative with the degree of misranking. Glenski et al [21] found that most Reddit users do not read the article that they vote on. In a later work, Glenski et al [22] used an Internet game called GuessTheKarma to collect independent preference judgments (free from social and ranking effects) for 400 pairs of images.…”

Section: Positionmentioning

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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Consumers and Curators: Browsing and Voting Patterns on Reddit

Cited by 70 publications

References 27 publications

Incentivized Blockchain-based Social Media Platforms

Incentivized Blockchain-based Social Media Platforms

Differences in structure and dynamics of networks retrieved from dark and public web forums

Quantifying Voter Biases in Online Platforms

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