ARTICONF decentralized social media platform for democratic crowd journalism

IF 2.3 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS

Social Network Analysis and Mining Pub Date : 2023-09-15 DOI:10.1007/s13278-023-01110-y

Inês Rito Lima, Vasco Filipe, Claudia Marinho, Alexandre Ulisses, Antorweep Chakravorty, Atanas Hristov, Nishant Saurabh, Zhiming Zhao, Ruyue Xin, Radu Prodan

{"title":"ARTICONF decentralized social media platform for democratic crowd journalism","authors":"Inês Rito Lima, Vasco Filipe, Claudia Marinho, Alexandre Ulisses, Antorweep Chakravorty, Atanas Hristov, Nishant Saurabh, Zhiming Zhao, Ruyue Xin, Radu Prodan","doi":"10.1007/s13278-023-01110-y","DOIUrl":null,"url":null,"abstract":"Abstract Media production and consumption behaviors are changing in response to new technologies and demands, giving birth to a new generation of social applications. Among them, crowd journalism represents a novel way of constructing democratic and trustworthy news relying on ordinary citizens arriving at breaking news locations and capturing relevant videos using their smartphones. The ARTICONF project as reported by Prodan (Euro-Par 2019: parallel processing workshops, Springer, 2019) proposes a trustworthy, resilient, and globally sustainable toolset for developing decentralized applications (DApps) to address this need. Its goal is to overcome the privacy, trust, and autonomy-related concerns associated with proprietary social media platforms overflowed by fake news. Leveraging the ARTICONF tools, we introduce a new DApp for crowd journalism called MOGPlay. MOGPlay collects and manages audiovisual content generated by citizens and provides a secure blockchain platform that rewards all stakeholders involved in professional news production. Besides live streaming, MOGPlay offers a marketplace for audiovisual content trading among citizens and free journalists with an internal token ecosystem. We discuss the functionality and implementation of the MOGPlay DApp and illustrate four pilot crowd journalism live scenarios that validate the prototype.","PeriodicalId":21842,"journal":{"name":"Social Network Analysis and Mining","volume":"20 1","pages":"0"},"PeriodicalIF":2.3000,"publicationDate":"2023-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Social Network Analysis and Mining","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s13278-023-01110-y","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}

引用次数: 1

Abstract

Abstract Media production and consumption behaviors are changing in response to new technologies and demands, giving birth to a new generation of social applications. Among them, crowd journalism represents a novel way of constructing democratic and trustworthy news relying on ordinary citizens arriving at breaking news locations and capturing relevant videos using their smartphones. The ARTICONF project as reported by Prodan (Euro-Par 2019: parallel processing workshops, Springer, 2019) proposes a trustworthy, resilient, and globally sustainable toolset for developing decentralized applications (DApps) to address this need. Its goal is to overcome the privacy, trust, and autonomy-related concerns associated with proprietary social media platforms overflowed by fake news. Leveraging the ARTICONF tools, we introduce a new DApp for crowd journalism called MOGPlay. MOGPlay collects and manages audiovisual content generated by citizens and provides a secure blockchain platform that rewards all stakeholders involved in professional news production. Besides live streaming, MOGPlay offers a marketplace for audiovisual content trading among citizens and free journalists with an internal token ecosystem. We discuss the functionality and implementation of the MOGPlay DApp and illustrate four pilot crowd journalism live scenarios that validate the prototype.

查看原文本刊更多论文

ARTICONF民主大众新闻的去中心化社交媒体平台

随着新技术和新需求的出现，媒体生产和消费行为正在发生变化，催生了新一代的社交应用。其中，大众新闻代表了一种构建民主可信新闻的新方式，依靠普通公民到达突发新闻地点，用智能手机拍摄相关视频。Prodan报告的ARTICONF项目(Euro-Par 2019:并行处理研讨会，Springer, 2019)提出了一个值得信赖的、有弹性的、全球可持续的工具集，用于开发去中心化应用程序(DApps)，以满足这一需求。它的目标是克服与假新闻泛滥的专有社交媒体平台相关的隐私、信任和自主相关担忧。利用ARTICONF工具，我们为大众新闻推出了一个名为MOGPlay的新DApp。MOGPlay收集和管理公民生成的视听内容，并提供一个安全的区块链平台，奖励所有参与专业新闻制作的利益相关者。除了直播之外，MOGPlay还通过内部代币生态系统为公民和免费记者提供视听内容交易市场。我们讨论了MOGPlay DApp的功能和实现，并举例说明了验证原型的四个试点人群新闻直播场景。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Social Network Analysis and Mining COMPUTER SCIENCE, INFORMATION SYSTEMS-

CiteScore

5.70

自引率

14.30%

发文量

141

期刊介绍： Social Network Analysis and Mining (SNAM) is a multidisciplinary journal serving researchers and practitioners in academia and industry. It is the main venue for a wide range of researchers and readers from computer science, network science, social sciences, mathematical sciences, medical and biological sciences, financial, management and political sciences. We solicit experimental and theoretical work on social network analysis and mining using a wide range of techniques from social sciences, mathematics, statistics, physics, network science and computer science. The main areas covered by SNAM include: (1) data mining advances on the discovery and analysis of communities, personalization for solitary activities (e.g. search) and social activities (e.g. discovery of potential friends), the analysis of user behavior in open forums (e.g. conventional sites, blogs and forums) and in commercial platforms (e.g. e-auctions), and the associated security and privacy-preservation challenges; (2) social network modeling, construction of scalable and customizable social network infrastructure, identification and discovery of complex, dynamics, growth, and evolution patterns using machine learning and data mining approaches or multi-agent based simulation; (3) social network analysis and mining for open source intelligence and homeland security. Papers should elaborate on data mining and machine learning or related methods, issues associated to data preparation and pattern interpretation, both for conventional data (usage logs, query logs, document collections) and for multimedia data (pictures and their annotations, multi-channel usage data). Topics include but are not limited to: Applications of social network in business engineering, scientific and medical domains, homeland security, terrorism and criminology, fraud detection, public sector, politics, and case studies.