Barbara Guidi
Laura Ricci
ABSTRACT
Online Social Networking platforms are now more than ever part of people's everyday life. They often act as the medium through which people can communicate or discover engaging media. In recent years, thanks to the massive popularity gained by blockchain technology, a new generation of social media emerged. Steemit, one of the most well-known blockchain-based social networks, is based on the blockchain Steem. It employs the blockchain in two ways: as data storage, and to implement a rewarding mechanism for pieces of content that are relevant to the users. Employing a rewarding system based on the social activity of the users can have a strong impact on how people socialise. In this work, we study the interaction among the users of Steemit in terms of incremental patterns. In detail, we propose a set of incremental patterns, by using variants of patterns proposed in the literature and by defining a new pattern specifically thought for the scenario of Blockchain Online Social Media (BOSM). This paper's findings show that social interactions in BOSMs are highly conditioned by the presence of bots, and the patterns proposed can detect previously undetected complex interactions.
- Sonja Buchegger, Doris Schiöberg, Le-Hung Vu, and Anwitaman Datta. 2009. PeerSoN: P2P social networking: early experiences and insights. In Proceedings of ACM EuroSys Workshop on Social Network Systems. 46--52.Google Scholar
Digital Library
- Carole Cadwalladr and Emma Graham-Harrison. 2018. Revealed: 50 million Facebook profiles harvested for Cambridge Analytica in major data breach. The guardian 17 (2018), 22.Google Scholar
- Jean Creusefond and Remy Cazabet. 2017. Characterising inter and intra-community interactions in link streams using temporal motifs. In Workshop on Complex Networks CompleNet. 81--92.Google ScholarCross Ref
- Leucio Antonio Cutillo, Refik Molva, and Thorsten Strufe. 2009. Safebook: A privacy-preserving online social network leveraging on real-life trust. IEEE Communications Magazine 47, 12 (2009), 94--101.Google ScholarDigital Library
- Anwitaman Datta, Sonja Buchegger, Le-Hung Vu, Thorsten Strufe, and Krzysztof Rzadca. 2010. Decentralized online social networks. In Handbook of social network technologies and applications. 349--378.Google Scholar
- Barbara Guidi. 2020. When Blockchain meets Online Social Networks. Pervasive and Mobile Computing 62 (2020), 101131.Google ScholarDigital Library
- Barbara Guidi, Tobias Amft, Andrea De Salve, Kalman Graffi, and Laura Ricci. 2015. DiDuSoNet: A P2P architecture for distributed Dunbar-based social networks. Peer-to-Peer Networking and Applications (2015), 1--18.Google Scholar
- Barbara Guidi and Andrea Michienzi. 2021. Dynamic Community Structure in Online Social Groups. Information 12, 3 (2021), 113.Google ScholarCross Ref
- Barbara Guidi, Andrea Michienzi, and Laura Ricci. 2020. Steem Blockchain: Mining the Inner Structure of the Graph. IEEE Access 8 (2020), 210251--210266.Google ScholarCross Ref
- Barbara Guidi, Andrea Michienzi, and Andrea De Salve. 2020. Community evaluation in Facebook groups. Multim. Tools Appl. 79, 45--46 (2020), 33603--33622.Google ScholarDigital Library
- Petter Holme and Jari Saramäki. 2012. Temporal networks. Physics reports 519, 3 (2012), 97--125.Google Scholar
- Yuriy Hulovatyy, Huili Chen, and Tijana Milenković. 2015. Exploring the structure and function of temporal networks with dynamic graphlets. Bioinformatics 31, 12 (2015), i171--i180.Google ScholarCross Ref
- Lauri Kovanen, Kimmo Kaski, János Kertész, and Jari Saramäki. 2013. Temporal motifs reveal homophily, gender-specific patterns, and group talk in call sequences. Proceedings of the National Academy of Sciences (2013), 201307941.Google ScholarCross Ref
- Jiali Lin, Zhenyu Li, Dong Wang, Kavé Salamatian, and Gaogang Xie. 2012. Analysis and comparison of interaction patterns in online social network and social media. In 2012 21st ICCCN. 1--7.Google Scholar
- Kai Liu, William K. Cheung, and Jiming Liu. 2013. Detecting Stochastic Temporal Network Motifs for Human Communication Patterns Analysis. In Proceedings of the 2013 IEEE/ACM ASONAM. 533--540.Google ScholarDigital Library
- Andrea Michienzi, Barbara Guidi, Laura Ricci, and Andrea De Salve. 2021. Incremental communication patterns in online social groups. Knowledge and Information Systems 63, 6 (2021), 1339--1364.Google ScholarDigital Library
- Ron Milo et al. 2002. Network motifs: simple building blocks of complex networks. Science 298, 5594 (2002), 824--827.Google Scholar
- Lucia Nasti, Andrea Michienzi, and Barbara Guidi. 2021. Discovering the Impact of Notifications on Social Network Addiction. In From Data to Models and Back. 72--86.Google Scholar
- Ursula Redmond, Martin Harrigan, and Pádraig Cunningham. 2012. Identifying time-respecting subgraphs in temporal networks. In Proceedings of the ECML PKDD. 51--63.Google Scholar
- Jiajing Wu, Jieli Liu, Weili Chen, Huawei Huang, Zibin Zheng, and Yan Zhang. 2020. Detecting Mixing Services via Mining Bitcoin Transaction Network with Hybrid Motifs. arXiv preprint arXiv:2001.05233 (2020).Google Scholar
- Qi Xuan, Huiting Fang, Chenbo Fu, and Vladimir Filkov. 2015. Temporal motifs reveal collaboration patterns in online task-oriented networks. Physical Review E 91, 5 (2015), 052813.Google ScholarCross Ref
- Yilin Zhang et al. 2018. Discovering political topics in Facebook discussion threads with graph contextualization. Annals of Applied Statistics 12, 2 (2018), 1096--1123.Google Scholar
- Yi-Qing Zhang, Xiang Li, Jian Xu, and Athanasios V Vasilakos. 2015. Human interactive patterns in temporal networks. IEEE Transactions on Systems, Man, and Cybernetics: Systems 45, 2 (2015), 214--222.Google ScholarCross Ref
- Qiankun Zhao et al. 2010. Communication motifs: a tool to characterize social communications. In Proceedings of ACM CIKM. 1645--1648.Google Scholar
- Matteo Zignani et al. 2018. Temporal Communication Motifs in Mobile Cohesive Groups. In Proceedings of 6th CNA. 490--501.Google Scholar
Index Terms
- Incremental communication patterns in blockchain online social media
Recommendations
Fork-based user migration in Blockchain Online Social Media
WebSci '22: Proceedings of the 14th ACM Web Science Conference 2022Nowadays, Online Social Media (OSM) are among the most popular web services. Traditional OSM are known to be affected by serious issues including misinformation, fake news, censorship, and privacy violations, to the point that a pressing demand for new ...
Incremental communication patterns in online social groups
AbstractIn the last decades, temporal networks played a key role in modelling, understanding, and analysing the properties of dynamic systems where individuals and events vary in time. Of paramount importance is the representation and the analysis of ...
Social exchange in online social networks. The reciprocity phenomenon on Facebook
Our research is focused on reciprocity, which is crucial for social exchanges.The online social network platform of our choice was Facebook, which is one of the most successful online social sites.In our study we found strong empirical evidence that an ...
Comments