Evolution of Cooperation in SNS-norms Game on Complex Networks and Real Social Networks

Hirahara, Yuki; Toriumi, Fujio; Sugawara, Toshiharu

doi:10.1007/978-3-319-13734-6_8

Cited by 18 publications

(8 citation statements)

References 9 publications

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“…Osaka et al [11] attempted to investigate the influence of the direct reciprocity and network structure on the lasting prosperity of social networking services. Hirahara et al [9,10] extended the model by adding feedback mechanisms with almost no cost, such as the "Like!" option/button and "read marks" feature, and investigated their effects on the user activities using Facebook data as well as on other artificial complex networks.…”

Section: Related Workmentioning

confidence: 99%

“…Substantial research has been conducted on this topic by studying these issues from viewpoints including social network analysis [4], social psychology [5][6][7], and agent-based simulation using evolutionary game theory [8][9][10][11]. For example, Zhao and Rosson [4] demonstrated the potential influence of microblogging sites, such as Twitter.…”

Section: Introductionmentioning

confidence: 99%

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Understanding how retweets influence the behaviors of social networking service users via agent-based simulation

2021

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The retweet is a characteristic mechanism of several social network services/social media, such as Facebook, Twitter, and Weibo. By retweeting tweet, users can share an article with their friends and followers. However, it is not clear how retweets affect the dominant behaviors of users. Therefore, this study investigates the impact of retweets on the behavior of social media users from the perspective of networked game theory, and how the existence of the retweet mechanism in social media promotes or reduces the willingness of users to post and comment on articles. To address these issues, we propose the retweet reward game model and quote tweet reward game model by adding the retweet and quote tweet mechanisms to a relatively simple social networking service model known as the reward game. Subsequently, we conduct simulation-based experiments to understand the influence of retweets on the user behavior on various networks. It is demonstrated that users will be more willing to post new articles with a retweet mechanism, and quote retweets are more beneficial to users, as users can expect to spread their information and their own comments on already posted articles.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Understanding how retweets influence the behaviors of social networking service users via agent-based simulation

2021

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“…Note that each duplicated network is often called world because the agents in each world have diverse experience by interacting with the same set of neighboring agents that behave differently. Therefore, the process of the SNS-norms game [8] was slightly modified to adopt the multiple-world GA to find and examine diverse strategies that are reasonable for individual agents. The overview of this process consists of the following subprocesses (SPs).…”

Section: Overview Of Multiple-world Genetic Algorithm (Multiple-worldmentioning

confidence: 99%

“…Their results show that cooperation on SNSs evolved by giving a meta reward, i.e., a reward as posting a comment on comments. Then, Hirahara et al [8,9] proposed an SNSnorms game by adding the structural interaction features of SNSs to the meta-rewards game. For example, users who respond to comments on posted articles are usually those who posted the original articles.…”

mentioning

confidence: 99%

Modeling and analyzing users’ behavioral strategies with co-evolutionary process

2021

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Social networking services (SNSs) are constantly used by a large number of people with various motivations and intentions depending on their social relationships and purposes, and thus, resulting in diverse strategies of posting/consuming content on SNSs. Therefore, it is important to understand the differences of the individual strategies depending on their network locations and surroundings. For this purpose, by using a game-theoretical model of users called agents and proposing a co-evolutionary algorithm called multiple-world genetic algorithm to evolve diverse strategy for each user, we investigated the differences in individual strategies and compared the results in artificial networks and those of the Facebook ego network. From our experiments, we found that agents did not select the free rider strategy, which means that just reading the articles and comments posted by other users, in the Facebook network, although this strategy is usually cost-effective and usually appeared in the artificial networks. We also found that the agents who mainly comment on posted articles/comments and rarely post their own articles appear in the Facebook network but do not appear in the connecting nearest-neighbor networks, although we think that this kind of user actually exists in real-world SNSs. Our experimental simulation also revealed that the number of friends was a crucial factor to identify users’ strategies on SNSs through the analysis of the impact of the differences in the reward for a comment on various ego networks.

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“…Some studies insist that information-sharing services in online networks have public goods game features [7,8,15]. Empirical studies have analyzed influences on the cooperative behaviors of reciprocity and network structures in social media [10,17,21] and some data analyses consider the effects of reputation systems on online markets [4,30].…”

Section: Introductionmentioning

confidence: 99%

A belief in rewards accelerates cooperation on consumer-generated media

2019

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Consumer-Generated Media (CGM) are useful for sharing information, but information does not come without cost. Incentives to discourage free riding (receiving information, but not providing it) are, therefore, offered to CGM users. The public goods game framework is a strong tool for analyzing and understanding CGM and users' information behaviors. Although it is well known that rewards are needed for maintaining cooperation in CGM, the existing models hypothesize three unnatural hypotheses: the linkage hypothesis, unlimited meta-rewarder, and sanction without expectation. In this study, we update the meta-reward model to identify a realistic situation through which to achieve a cooperation on CGM. Our model reveals that restricted public goods games cannot provide cooperative regimes when players are myopic and never have any strategies on their actions. Cooperative regimes emerge if players that provide the first-order rewards know whether cooperative players will give the second-order rewards to the first-order rewarders. In the context of CGM, active posting of articles occurs if potential commenters/responders can ascertain that the user posting the article will respond to their comments.

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Evolution of Cooperation in SNS-norms Game on Complex Networks and Real Social Networks

Cited by 18 publications

References 9 publications

Understanding how retweets influence the behaviors of social networking service users via agent-based simulation

Understanding how retweets influence the behaviors of social networking service users via agent-based simulation

Modeling and analyzing users’ behavioral strategies with co-evolutionary process

A belief in rewards accelerates cooperation on consumer-generated media

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