Evaluating Posts on the Steemit Blockchain: Analysis on Topics Based on Textual Cues

Online Social Networking platforms (OSNs) are part of the people's everyday life answering the deep-rooted need for communication among humans. During recent years, a new generation of social media based on blockchain became very popular, bringing the power of the technology to the service of social networks. Steemit is one such and employs the blockchain to implement a rewarding mechanism, adding a new, economic, layer to the social media service. The reward mechanism grants virtual tokens to the users capable of engaging other users on the platform, which can be either vested in the platform for increased influence or exchanged for fiat currency. The introduction of an economic layer on a social networking platform can seriously influence how people socialize. In this work, we tackle the problem of understanding how this new business model conditions the way people create contents. We performed term frequency and topic modelling analyses over the written contents published on the platforms between 2017 and 2019. This analysis lets us understand the most common topics of the contents that appear in the platform. While personal mundane information still appears, along with contents related to arts, food, travels, and sport, we also see emerging a very strong presence of contents about blockchain, cryptocurrency and, more specifically, on Steemit itself and its users.

[1]  Veronika Karnowski,et al.  News Sharing in Social Media: A Review of Current Research on News Sharing Users, Content, and Networks , 2015 .

[2]  Dhiraj Murthy,et al.  Modeling virtual organizations with Latent Dirichlet Allocation: A case for natural language processing , 2014, Neural Networks.

[3]  Bin Zhou,et al.  A Fuzzy Approach Model for Uncovering Hidden Latent Semantic Structure in Medical Text Collections , 2015 .

[4]  Shafiq R. Joty,et al.  Applications of Online Deep Learning for Crisis Response Using Social Media Information , 2016, ArXiv.

[5]  Svitlana Volkova,et al.  Inferring Latent User Properties from Texts Published in Social Media , 2015, AAAI.

[6]  Anne Cocos,et al.  Deep learning for pharmacovigilance: recurrent neural network architectures for labeling adverse drug reactions in Twitter posts , 2017, J. Am. Medical Informatics Assoc..

[7]  Barbara Guidi,et al.  When Blockchain meets Online Social Networks , 2020, Pervasive Mob. Comput..

[8]  Cornelia Caragea,et al.  Disaster Response Aided by Tweet Classification with a Domain Adaptation Approach , 2018 .

[9]  Laura Ricci,et al.  Managing social contents in Decentralized Online Social Networks: A survey , 2018, Online Soc. Networks Media.

[10]  Simon O'Keefe,et al.  Deep Learning and Word Embeddings for Tweet Classification for Crisis Response , 2019, ArXiv.

[11]  C. Bail Combining natural language processing and network analysis to examine how advocacy organizations stimulate conversation on social media , 2016, Proceedings of the National Academy of Sciences.

[12]  Derek Greene,et al.  How Many Topics? Stability Analysis for Topic Models , 2014, ECML/PKDD.

[13]  Laura Ricci,et al.  DiDuSoNet: A P2P architecture for distributed Dunbar-based social networks , 2016, Peer-to-Peer Netw. Appl..

[14]  Ying Wah Teh,et al.  Text mining for market prediction: A systematic review , 2014, Expert Syst. Appl..

[15]  Jie Yin,et al.  Using Social Media to Enhance Emergency Situation Awareness: Extended Abstract , 2015, IJCAI.

[16]  J. Khuntia,et al.  Sharing News Through Social Networks , 2016 .

[17]  Sonja Buchegger,et al.  Implementing a P2P Social Network - Early Experiences and Insights from PeerSoN , 2009 .

[18]  Michael Gamon,et al.  Predicting Responses to Microblog Posts , 2012, NAACL.

[19]  Hsi-Peng Lu,et al.  Persuasive messages, popularity cohesion, and message diffusion in social media marketing , 2015 .

[20]  Changhee Kim,et al.  Analysis of the Trends in Biochemical Research Using Latent Dirichlet Allocation (LDA) , 2019, Processes.

[21]  Krzysztof Rzadca,et al.  Decentralized Online Social Networks , 2010, Handbook of Social Network Technologies.

[22]  Heng Ji,et al.  The Age of Social Sensing , 2018, Computer.

[23]  Refik Molva,et al.  Safebook: A privacy-preserving online social network leveraging on real-life trust , 2009, IEEE Communications Magazine.

[24]  Subbarao Kambhampati,et al.  Tweeting the Mind and Instagramming the Heart: Exploring Differentiated Content Sharing on Social Media , 2016, ICWSM.

[25]  Rafael A. Calvo,et al.  Natural language processing in mental health applications using non-clinical texts† , 2017, Natural Language Engineering.

[26]  Jie Yin,et al.  Using Social Media to Enhance Emergency Situation Awareness , 2012, IEEE Intelligent Systems.

[27]  Velislava Stoykova,et al.  Social Network Analysis of “Clexa” Community Interaction Patterns , 2018 .