  In recent years, Blockchain-based Online Social Media (BOSM) platforms have
evolved fast due to the advancement of blockchain technology. BOSM can
effectively overcome the problems of traditional social media platforms, such
as a single point of trust and insufficient incentives for users, by combining
a decentralized governance structure and a cryptocurrency-based incentive
model, thereby attracting a large number of users and making it a crucial
component of Web3. BOSM allows users to downvote low-quality content and aims
to decrease the visibility of low-quality content by sorting and filtering it
through downvoting. However, this feature may be maliciously exploited by some
users to undermine the fairness of the incentive, reduce the quality of highly
visible content, and further reduce users' enthusiasm for content creation and
the attractiveness of the platform. In this paper, we study and analyze the
downvoting behavior using four years of data collected from Steemit, the
largest BOSM platform. We discovered that a significant number of bot accounts
were actively downvoting content. In addition, we discovered that roughly 9% of
the downvoting activity might be retaliatory. We did not detect any significant
instances of downvoting on content for a specific topic. We believe that the
findings in this paper will facilitate the future development of user behavior
analysis and incentive pattern design in BOSM and Web3.
