Posting Bot Detection on Blockchain-based Social Media Platform using Machine Learning Techniques

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 blockchain-based social media platform. Compared with the bot detection on the usual social media platforms, the features we created have an advantage that posting bots can be detected without limiting the number or length of posts. We can extract the features of posts by clustering distances between blog data or replies. These features are obtained from the Minimum Average Cluster from Clustering Distance between Frequent words and Articles (MAC-CDFA), which is not used in any of the previous social media research. Based on the enriched features, we enhanced the quality of classification tasks. Comparing the F1-scores, the features we created outperformed the features used for bot detection on Facebook and Twitter.