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Kaito AI has updated its crypto mindshare algorithm to tackle issues of manipulated engagement and low-quality content skewing its leaderboards.
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The update focuses on reducing spammy engagement farming by emphasizing long-term, quality contributions across Crypto Twitter to rebuild community trust.
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Despite some backlash, industry voices acknowledge Kaito’s efforts, advocating for enhanced AI-driven filtering and tiered dashboards to better reward authentic insights.
Kaito AI revamps its mindshare algorithm to prioritize quality crypto content, combat engagement farming, and restore trust in InfoFi leaderboards.
Addressing Manipulated Engagement in Crypto Mindshare Rankings
Kaito AI’s latest algorithm update directly confronts the widespread criticism regarding the integrity of its mindshare scores. For months, users and researchers have highlighted how influencers and projects exploit loopholes to artificially inflate their visibility on InfoFi dashboards. This manipulation undermines the platform’s credibility by promoting hype over genuine value.
The update introduces mechanisms to detect and penalize spammy engagement tactics, such as excessive replies or posts focused solely on rewards and rankings without substantive commentary. By prioritizing long-term contributions and meaningful insights, Kaito aims to realign incentives with authentic community engagement rather than superficial metrics.
Industry Experts Call Out the Flaws in Current InfoFi Models
Leading voices in the crypto research community, including Louround from Redacted Research, have publicly criticized the current leaderboard dynamics. Louround emphasized that high mindshare scores often do not correlate with true protocol interest or value creation, citing examples like the Loud project whose inflated mindshare preceded a dramatic market collapse.
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Similarly, Arkham’s introduction of a Key Opinion Leader (KOL) label for influencers with over 100K followers aims to increase transparency by tracking whether these figures genuinely support tokens or merely engage in paid promotions. This move reflects growing demand for accountability in influencer-driven crypto marketing.
Community Feedback Drives Kaito AI’s Algorithmic Reforms
In response to mounting community pressure, Kaito AI has implemented several key changes designed to elevate content quality. These include excluding posts that focus exclusively on rewards or rankings without offering insightful analysis, limiting the number of mindshare tweets per week, and enhancing loyalty rewards for consistent and valuable contributors.
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By targeting engagement farming and rewarding sustained, quality participation, Kaito hopes to foster a healthier ecosystem where genuine knowledge sharing thrives over superficial hype.
Balancing Innovation with User Expectations in Crypto Content Curation
While some users remain skeptical, others recognize the potential benefits of Kaito’s approach. Web3 content creator Jeff highlights a paradigm shift in crypto trading strategies, noting that valuable insights increasingly emerge from social media conversations rather than traditional chart analysis. This underscores the importance of reliable content curation tools like Kaito AI.
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However, with a reported fully diluted valuation of $1.38 billion and growing influence, Kaito faces heightened expectations to refine its algorithms further. Calls for advanced AI-driven content filtering and tiered dashboards that reward genuine insight over volume are intensifying, signaling a critical juncture for InfoFi platforms.
Conclusion
Kaito AI’s recent algorithm update represents a significant step toward addressing the challenges of manipulated engagement and low-quality content in the crypto mindshare space. By prioritizing long-term, insightful contributions and responding to community feedback, Kaito is working to restore trust and credibility in InfoFi leaderboards. Continued innovation and transparency will be essential as the platform evolves to meet the demands of an increasingly sophisticated crypto audience.
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