User incentive mechanism in blockchain-based online community: An empirical study of steemit

The rise of blockchain technology has brought innovations in various business domains and spawned a new form of organization—the decentralized autonomous organization (DAO). Steemit is recognized as one of the earliest blockchain-based online communities, and a typical example of DAO. By endowing community members with new roles, the decentralization of blockchain-based communities brings changes to the design of user incentive mechanisms. However, few studies have paid attention to the user incentive mechanism when users play the dual roles of social participant and community owner. On the basis of social capital theory and psychological ownership theory, this study explores Steemit's incentive mechanism by evaluating the impact of these dual roles on user active participation behavior. The study adopts a two-way fixed effect negative binomial regression to test the research model. The results show that users’ social capital, share capital, social feedback, and economic feedback positively affect their active participation behavior. At the same time, social feedback and economic feedback play moderating roles on the effects of the dual capitals. Overall, this research provides both theoretical insights and practical implications for understanding, designing, and governing blockchain-based online communities.     

Reflection-through-performance: personal implications of documenting health behaviors for the collective

Previous work has examined how technology can support health behavior monitoring in social contexts. These tools incentivize behavior documentation through the promise of virtual rewards, rich visualizations, and improved co-management of disease. Social influence is leveraged to motivate improved behaviors through friendly competition and the sharing of emotional and informational support. Prior work has described how by documenting and sharing behaviors in these tools, people engage in performances of the self. This performance happens as users selectively determine what information to share and hide, crafting a particular portrayal of their identity. Much of the prior work in this area has examined the implications of systems that encourage people to share their behaviors with friends, family, and geographically distributed strangers. In this paper, we report upon the performative nature of behavior sharing in a system created for a different social group: the local neighborhood. We designed Community Mosaic (CM), a system with a collectivistic focus: this tool asks users to document their behaviors using photographs and text, but not for their own benefit—for the benefit of others in their community. Through a 6-week deployment of CM, we evaluated the nature of behavior sharing in this system, including participants’ motivations for sharing, the way in which this sharing happened, and the reflexive impact of sharing. Our findings highlight the performative aspects of photograph staging and textual narration and how sharing this content led participants to become more aware and evaluative of their behaviors, and led them to try to eat more healthfully. We conclude with recommendations for behavior monitoring tools, specifically examining the implications of users’ perceived audience and automated behavioral tracking on opportunities for reflection-through-performance.     

考虑信誉更新的移动群智感知在线激励机制研究

激励更多用户参与感知任务并提供高质量数据是移动群智感知研究的热点问题之一。针对在线到达的激励机制场景中,参与用户提供数据的质量以及其信誉值没有得到足够重视等问题,本文提出用户在线参与感知任务的信誉评价方法并构建其信誉评价模型。综合考虑用户历史和现实的信誉记录,建立信誉更新算法模型,设计基于信誉更新的多阶段在线激励机制(Reputation-updated online mechanism,ROM)。仿真结果表明,该算法能够帮助平台获得更好的效用,提高收集数据的质量从而提高雇佣效率。     

BFF: A tool for eliciting tie strength and user communities in social networking services

The use of social networking services (SNSs) such as Facebook has explosively grown in the last few years. Users see these SNSs as useful tools to find friends and interact with them. Moreover, SNSs allow their users to share photos, videos, and express their thoughts and feelings. However, users are usually concerned about their privacy when using SNSs. This is because the public image of a subject can be affected by photos or comments posted on a social network. In this way, recent studies demonstrate that users are demanding better mechanisms to protect their privacy. An appropriate approximation to solve this could be a privacy assistant software agent that automatically suggests a privacy policy for any item to be shared on a SNS. The first step for developing such an agent is to be able to elicit meaningful information that can lead to accurate privacy policy predictions. In particular, the information needed is user communities and the strength of users’ relationships, which, as suggested by recent empirical evidence, are the most important factors that drive disclosure in SNSs. Given the number of friends that users can have and the number of communities they may be involved on, it is infeasible that users are able to provide this information without the whole eliciting process becoming confusing and time consuming. In this work, we present a tool called Best Friend Forever (BFF) that automatically classifies the friends of a user in communities and assigns a value to the strength of the relationship ties to each one. We also present an experimental evaluation involving 38 subjects that showed that BFF can significantly alleviate the burden of eliciting communities and relationship strength.     

User Roles in Online Communities and Their Moderating Effect on Online Community Usage Intention: An Integrated Approach

Recently, it has been evident that the analysis of user data and content in online environments allows practitioners to understand how to motivate online community members and keep them frequently involved in the community, and so to manage these communities successfully. In this sense, practitioners should comprehend community members’ usage intentions to give a better service and to motivate them. However, different user types engage in such communities, so understanding their diverse needs is also essential for practitioners. In parallel, this article addresses the problem of the different user types existing in online communities, and each of them requires different strategies to be motivated and involved in the community. Thus, unlike previous studies, this study firstly identifies user roles in an online community based on the structural role theory, social network analysis, and community members’ contribution behavior. After that, it investigates members’ usage intentions based on the technology acceptance model and examines the moderating effect of identified user roles on their usage intentions. The study also guides practitioners to develop motivational strategies to keep each type of member continually satisfied.     

Mining User Knowledge for Investigating the Facebook Business Model: The Case of Taiwan Users

Social network sites (SNS), as web-based services, allow users to make open or semi-open profiles within the systems they are part of, to see lists of other people in the group and to see the relations of people within different groups. Facebook is essentially an online social network site in which individuals can share photographs, personal information, and join groups of friends. This study investigates the experiences on Facebook of various users in Taiwan. Their degrees of confidence were often demonstrated by word-of-mouth disseminations about the social network site. Further, this research looks at how the reputations of Facebook proprietors and their affiliates were disseminated through relationship marketing for formulated social network marketing in its business model concerns. Therefore, this study uses the a priori algorithm as an association rules approach, and cluster analysis for data mining. We divide Facebook users into two groups of contributors and lurkers by their profiles and then find each group’s social network community information utilization and online purchase behaviors for investigating the Facebook business models.     

Multi-relational reinforcement for computing credibility of nodes

Nowadays, growing number of social networks are available on the internet, with which users can conveniently make friends, share information, and exchange ideas with each other. As the result, large amount of data are generated from activities of those users. Such data are regarded as valuable resources to support different mining tasks, such as predicting friends for a user, ranking users in terms of their influence on the social network, or identifying communities with common interests. Traditional algorithms for those tasks are often designed under the assumption that a user selects another user as his friend based on their common interests. As a matter of fact, users on a social network may not always develop their friends with common interest. For example, a user may randomly select other users as his friends just in order to attract more links reversely from them. Therefore, such links may not indicate his influence. In this paper, we study the user rank problem in terms of their ‘real’ influences. For this sake, common interest relationships among users are established besides their friend relationships. Then, the credible trust link from one node to another is on account of their similarities, which means the more similar the two users, the more credible their trust relation. So the credibility of a node is high if its trust inlinks are credible enough. In this work, we propose a framework that computes the credibility of nodes on a multi-relational network using reinforcement techniques. To the best of our knowledge, this is the first work to assess credibility exploited knowledge on multi-relational social networks. The experimental results on real data sets show that our framework is effective.     

EduXs: multilayer educational services platforms

How to use the online social learning communities to improve quality and quantity of interactions in physical social learning communities is an important issue. This work describes the design and implementation of multilayer educational services platforms that enable learners to establish their own online social learning communities and integrate their online social learning communities into a large public social learning portal site—EduCities. Multilayer educational services platforms were designed to integrate various individual online social learning communities, and to map these communities into physical social learning communities. This work proposes and implements an architecture called EduXs, and integrates it with K-12 social learning communities. One year after the EduXs system was released on the Internet, 1,849 schools, 15,772 classes, and 130,908 individuals in Taiwan had registered to use the system to construct their own online social learning communities. Among these registered users, 18.8% of registered schools, and 24.7% of registered classes continue to use the system. Evaluation results indicate that the system is accepted by teachers and students.     

How lonely people use and perceive Facebook

This study examined the relationship between loneliness and various aspects of Facebook use including use activity, self-disclosure, attitudes, and satisfaction. Data were collected through an online survey among adult Facebook users (N = 536). Results revealed that loneliness was associated with a fewer number of Facebook friends and a less overlapping between Facebook and offline friends. Loneliness was inversely related to communicating activities but not significantly to presenting activities. Also, lonely people tended to engage in positive self-disclosure less but negative self-disclosure more. Although lonely people viewed Facebook as more useful for self-disclosure and social connection, their satisfaction of Facebook use was lower than their counterparts.     

Exploring sharing patterns for video recommendation on YouTube-like social media

YouTube-like video sharing sites (VSSes) have gained increasing popularity in recent years. Meanwhile, Face-book-like online social networks (OSNs) have seen their tremendous success in connecting people of common interests. These two new generation of networked services are now bridged in that many users of OSNs share video contents originating from VSSes with their friends, and it has been shown that a significant portion of views of VSS videos are attributed to this sharing scheme of social networks. To understand how the video sharing behavior, which is largely based on social relationship, impacts users’ viewing pattern, we have conducted a long-term measurement with RenRen and YouKu, the largest online social network and the largest video sharing site in China, respectively. We show that social friends have higher common interest and their sharing behaviors provide guidance to enhance recommended video lists. In this paper, we take a first step toward learning OSN video sharing patterns for video recommendation. An autoencoder model is developed to learn the social similarity of different videos in terms of their sharing in OSNs. We, therefore, propose a similarity-based strategy to enhance video recommendation for YouTube-like social media. Evaluation results demonstrate that this strategy can remarkably improve the precision and recall of recommendations, as compared to other widely adopted strategies without social information.     

Moral obligation in online social interaction: Clicking the “like” button

Social media greatly facilitate online social interaction among friends, and user behavior in social interaction may be influenced by interpersonal relationships. While individuals sometimes do enjoy the content shared by friends, they may also feel that they have a moral obligation to like what their friends share. Drawing on the stimulus-organism-response model, this paper examines whether the characteristics of interpersonal relationships are associated with moral obligation as well as whether moral obligation is associated with “like” intention. By using data from 348 users of WeChat Moments, we provide empirical evidence that the stimuli of interpersonal relationships (perceived authority, perceived closeness, and peer referent) are positively associated with the sense of moral obligation, which in turn, is positively associated with user intention to click the Like button on their friends’ postings on social media. Theoretical and practical implications of the findings are discussed.     

SocialTrust: Enabling long-term social cooperation in peer-to-peer services

Peer-to-peer(P2P) services heavily rely on users’ cooperation to achieve desired performance. However, most current P2P systems only encourage short-term and direct cooperation between peers. The lack of incentives for long term and indirect cooperation has severely limited the performance of P2P systems. On the other hand, recent measurements on large-scale networks show that peers’ behavior often demonstrates strong social patterns. In this paper, we design and implement a social P2P network, named SocialTrust, based on peers’ common interests. In SocialTrust, each peer tries to find a small number of friends and maintains long term social links with them. We also propose a distributed trust mechanism. The trust between two friends reflects their cooperation level and serves as the credit limit between them. A peer with higher trust can download data from its friends more efficiently. The trust can be propagated among friends to support indirect reciprocity. We formally prove that the proposed distributed trust mechanism is secure and can defend against various forms of attacks. By adding asmall number of long term social links to the existing P2P network, SocialTrust relaxes the constraint of direct incentive mechanisms and encourages peers to perform various forms of long-term cooperation. Both trace-driven simulation and real Internet experiments show that SocialTrust can significantly improve file availability and download performance of current P2P file sharing systems.     

Private discovery of common social contacts

Digital services that are offered, and consumed, on the basis of social relationships form the backbone of social clouds—an emerging new concept that finds its roots in online social networks. The latter have already taken an essential role in people’s daily life, helping users to build and reflect their social relationships to other participants. A key step in establishing new links entails the reconciliation of shared contacts and friends. However, for many individuals, personal relationships belong to the private sphere, and, as such, should be concealed from potentially prying eyes of strangers. Consequently, the transition toward social clouds cannot set aside mechanisms to control the disclosure of social links. This paper motivates and introduces the concept of Private Discovery of Common Social Contacts, which allows two users to assess their social proximity through interaction and learn the set of contacts (e.g., friends) that are common to both users, while hiding contacts that they do not share. We realize private contact discovery using a new cryptographic primitive, called contact discovery scheme (CDS), whose functionality and privacy is formalized in this work. To this end, we define a novel privacy feature, called contact-hiding, that captures our strong privacy goals. We also propose the concept of contact certification and show that it is essential to thwart impersonation attacks on social relationships. We build provably private and realistically efficient CDS protocols for private discovery of mutual contacts. Our constructions do not rely on a trusted third party (TTP)—all contacts are managed independently by the users. The practicality of our proposals is confirmed both analytically and experimentally on different computing platforms. We show that they can be efficiently deployed on smartphones, thus allowing ad hoc and ubiquitous contact discovery outside of existing social networks. Our CDS constructions allow users to select their (certified) contacts to be included in individual protocol executions. That is, users may perform context-dependent contact discovery using any subset (circle) of their contacts.     

A Generative Model Approach for Geo-Social Group Recommendation

With the development and prevalence of online social networks, there is an obvious tendency that people are willing to attend and share group activities with friends or acquaintances. This motivates the study on group recommendation, which aims to meet the needs of a group of users, instead of only individual users. However, how to aggregate different preferences of different group members is still a challenging problem: 1) the choice of a member in a group is influenced by various factors, e.g., personal preference, group topic, and social relationship; 2) users have different influences when in different groups. In this paper, we propose a generative geo-social group recommendation model (GSGR) to recommend points of interest (POIs) for groups. Specifically, GSGR well models the personal preference impacted by geographical information, group topics, and social influence for recommendation. Moreover, when making recommendations, GSGR aggregates the preferences of group members with different weights to estimate the preference score of a group to a POI. Experimental results on two datasets show that GSGR is effective in group recommendation and outperforms the state-of-the-art methods.     

Already up? using mobile phones to track & share sleep behavior

Users share a lot of personal information with friends, family members, and colleagues via social networks. Surprisingly, some users choose to share their sleeping patterns, perhaps both for awareness as well as a sense of connection to others. Indeed, sharing basic sleep data, whether a person has gone to bed or waking up, informs others about not just one's sleeping routines but also indicates physical state, and reflects a sense of wellness. We present Somnometer, a social alarm clock for mobile phones that helps users to capture and share their sleep patterns. While the sleep rating is obtained from explicit user input, the sleep duration is estimated based on monitoring a user's interactions with the app. Observing that many individuals currently utilize their mobile phone as an alarm clock revealed behavioral patterns that we were able to leverage when designing the app. We assess whether it is possible to reliably monitor one's sleep duration using such apps. We further investigate whether providing users with the ability to track their sleep behavior over a long time period can empower them to engage in healthier sleep habits. We hypothesize that sharing sleep information with social networks impacts awareness and connectedness among friends. The result from a controlled study reveals that it is feasible to monitor a user's sleep duration based just on her interactions with an alarm clock app on the mobile phone. The results from both an in-the-wild study and a controlled experiment suggest that providing a way for users to track their sleep behaviors increased user awareness of sleep patterns and induced healthier habits. However, we also found that, given the current broadcast nature of existing social networks, users were concerned with sharing their sleep patterns indiscriminately.     

Social capital expectation and usage of social media: the moderating role of social capital susceptibility

This study aims to identify the motivations of social media users who click ‘like’ to the post of their friends. We posit that this behaviour is not solely based on an instant feeling or reaction to a post, but a more complicated action that involves calculation and expectation of the future social media use. We first apply social capital theories to identify the types of expectations, and then differentiate these expectations based on the communication styles of social media where private and public relationships coexist. From these, we develop a social capital expectation matrix in the context of social media. In the research model, we discuss how these social capital expectations motivate people to use social media, including the moderating effects of social capital susceptibilities, based on expectancy theory of motivation. To validate our model, data collected from 291 social media users are analysed. This result confirms that people click likes to share their interests and display their network to others. Conversely, the relational dimensions of social capital including capital recompense and social inclusion exerted significant interaction effects only when they were considered along with capital susceptibilities.     

Personality traits and echo chambers on facebook

In online social networks, users tend to select information that adhere to their system of beliefs and to form polarized groups of like minded people. Polarization as well as its effects on online social interactions have been extensively investigated. Still, the relation between group formation and personality traits remains unclear. A better understanding of the cognitive and psychological determinants of online social dynamics might help to design more efficient communication strategies and to challenge the digital misinformation threat. In this work, we focus on users commenting posts published by US Facebook pages supporting scientific and conspiracy-like narratives, and we classify the personality traits of those users according to their online behavior. We show that different and conflicting communities are populated by users showing similar psychological profiles, and that the dominant personality model is the same in both scientific and conspiracy echo chambers. Moreover, we observe that the permanence within echo chambers slightly shapes users' psychological profiles. Our results suggest that the presence of specific personality traits in individuals lead to their considerable involvement in supporting narratives inside virtual echo chambers.     

Zero is hero: Round number effects on knowledge-sharing platforms

With the inherent public goods problem embedded in knowledge-sharing platforms, various incentive mechanisms have been implemented, most of which are in the form of gamified elements. Among those motivating elements, reputation points are the most direct feedback about individuals’ contribution effort, which use numerical units indicating progress. Although some research has found that points can incentivize users to contribute, empirical evidence regarding the influential patterns of such numerical units remains limited. Drawing on numerical cognition literature that an individual's evaluation and judgments may be influenced by certain numerical cues, we particularly focus on the round number bias on knowledge-sharing platforms. Several hypotheses regarding users’ behavioral changes when their accumulated points approach round numbers have been proposed, including their contribution level, contribution quality, and writing style. By analyzing data collected from StackOverflow.com, we find that users perceive round numbers as category boundaries or endpoints and crossing such boundaries can motivate aspirational behaviors. Concretely, users significantly increase their post frequency and length, and write answers with more function words and second-person pronouns. Meanwhile, their posts will be more likely to be accepted as the best answers and gain more votes. We also explore the moderating effects of advanced explicit incentives and numbers’ magnitude. Theoretically, our research contributes to a body of literature on knowledge-sharing platform incentive mechanisms to motivate users’ contributions and sheds light on the utilization of numerical cues to guide individuals’ behaviors in user-generated-content (UGC) provision context.     

Comparative case studies of open source software peer review practices

ContextThe power of open source software peer review lies in the involvement of virtual communities, especially users who typically do not have a formal role in the development process. As communities grow to a certain extent, how to organize and support the peer review process becomes increasingly challenging. A universal solution is likely to fail for communities with varying characteristics.ObjectiveThis paper investigates differences of peer review practices across different open source software communities, especially the ones engage distinct types of users, in order to offer contextualized guidance for developing open source software projects.MethodComparative case studies were conducted in two well-established large open source communities, Mozilla and Python, which engage extremely different types of users. Bug reports from their bug tracking systems were examined primarily, complemented by secondary sources such as meeting notes, blog posts, messages from mailing lists, and online documentations.ResultsThe two communities differ in the key activities of peer review processes, including different characteristics with respect to bug reporting, design decision making, to patch development and review. Their variances also involve the designs of supporting technology. The results highlight the emerging role of triagers, who bridge the core and peripheral contributors and facilitate the peer review process. The two communities demonstrate alternative designs of open source software peer review and their tradeoffs were discussed.ConclusionIt is concluded that contextualized designs of social and technological solutions to open source software peer review practices are important. The two cases can serve as learning resources for open source software projects, or other types of large software projects in general, to cope with challenges of leveraging enormous contributions and coordinating core developers. It is also important to improve support for triagers, who have not received much research effort yet.     

Virtual Support Communities and Psychological Well‐Being: The Role of Optimistic and Pessimistic Social Comparison Strategies

Do the beneficial or detrimental effects of CMC activity depend on the specific social comparison strategy individuals use? The present study aimed to answer this question by examining social comparison strategies, different measures of online activity within the community, and psychological well‐being of users of online breast cancer support communities. Results showed that the relationship between online activity (i.e., length of visits and frequency of posts) and psychological well‐being (i.e., breast cancer related concerns and depression) was determined by users' pessimistic social comparison strategy; downward identification influenced especially highly active users. Findings suggest that active CMC users should be careful not to become entrapped by negative social comparison processes.