Posting Bot Detection on Blockchain-based Social Media Platform using Machine Learning Techniques
Taehyun Kim, Hyomin Shin, Hyung Ju Hwang, Seungwon Jeong

TL;DR
This paper introduces a novel machine learning approach for detecting posting bots on the blockchain-based social media platform Steemit, utilizing unique clustering-based features that outperform traditional methods.
Contribution
It is the first study to focus on posting bot detection on a blockchain social media platform and proposes MAC-CDFA features that improve classification accuracy.
Findings
MAC-CDFA features outperform traditional features in bot detection.
Detection accuracy is higher on Steemit compared to Facebook and Twitter.
Features enable detection without limiting post length or number.
Abstract
Steemit is a blockchain-based social media platform, where authors can get author rewards in the form of cryptocurrencies called STEEM and SBD (Steem Blockchain Dollars) if their posts are upvoted. Interestingly, curators (or voters) can also get rewards by voting others' posts, which is called a curation reward. A reward is proportional to a curator's STEEM stakes. Throughout this process, Steemit hopes "good" content will be automatically discovered by users in a decentralized way, which is known as the Proof-of-Brain (PoB). However, there are many bot accounts programmed to post automatically and get rewards, which discourages real human users from creating good content. We call this type of bot a posting bot. While there are many papers that studied bots on traditional centralized social media platforms such as Facebook and Twitter, we are the first to study posting bots on a…
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Taxonomy
TopicsMisinformation and Its Impacts · Spam and Phishing Detection · Blockchain Technology Applications and Security
MethodsSpatial Broadcast Decoder
