Enhancing Twitter AI Agent’s Human-Like Behavior and Engagement Efficiency

[sosi-fcfs]

Is your feature request related to a problem? Please describe.

Currently, the agent behavior is too predictable, making it easily detectable by external users and Twitter's moderation system. This can lead to suspicion, reduced engagement, or even temporary shadowbans due to excessive, unnatural activity patterns.

In a previous version, the agent adhered to a "working hours" schedule, remaining inactive at night. This approach not only helped avoid shadowbans but also improved efficiency by focusing engagement during peak hours. However, in its current form, the agent runs continuously, posting and commenting strictly according to predefined .env parameters, making it look artificial and increasing the risk of detection.

Additionally, engagement with newer posts significantly boosts visibility and interactions. Therefore, ensuring that the bot prioritizes fresh posts—those published within the last hour—would optimize its impact.

Describe the solution you'd like

• Configurable Active Hours: Introduce an option in the settings to define specific working hours, e.g., 10:00 - 21:00 UTC. This would make the agent behavior more human-like and reduce the risk of being flagged by Twitter’s algorithms.

• Post Freshness Limit: Add a setting to specify the maximum age of posts the bot engages with. For instance, allowing interactions only with posts published within the last 5 to 50 minutes. This ensures the agent remains relevant by engaging with fresh content, thereby maximizing visibility and interaction rates.

• Randomized Activity Patterns: Instead of rigid time frames, allow the bot to have slight variations in its posting schedule, mimicking human-like inconsistencies. This could include random delays between actions and periodic "idle" times.

• Engagement-Based Adaptation: Implement a dynamic adjustment mechanism where the bot increases or decreases its activity based on engagement metrics. For example, if a certain time window yields higher interactions, the bot could prioritize those hours automatically.

Describe alternatives you've considered

• Using machine learning to analyze peak engagement times and adjust activity accordingly.

• Introducing a "cooldown" system where the bot temporarily slows down if engagement drops too quickly, preventing detection.

Additional context

These enhancements would make the bot appear more organic, reducing the risk of detection and optimizing engagement. By mimicking natural behavior, the bot could sustain long-term growth without triggering Twitter’s moderation systems. A dynamic and adaptable approach ensures that it remains effective in an evolving algorithmic landscape.