From scripts to social agents: AI bots now shape politics, markets, and public opinion

Social agents increasingly operate across text, images, audio, and video, enabling richer interaction and more convincing impersonation. Visual grounding allows agents to interpret and generate images tied to geographic locations or real-world events, further complicating efforts to authenticate online content.

Automated online actors are no longer confined to simple scripts or predictable behaviors. Advances in artificial intelligence (AI) have transformed social bots into increasingly autonomous agents capable of reasoning, planning, persuasion, and emotional imitation at a scale that is reshaping digital ecosystems worldwide. A new academic editorial warns that this transformation represents a structural shift rather than a gradual evolution, demanding urgent attention from researchers, policymakers, and platform operators.

These findings are outlined in the study “Advances in Social Bots,” published in the journal Electronics. The editorial brings together cutting-edge research to chart the transition from traditional social bots to what the authors describe as autonomous social agents. 

From scripted automation to cognitive autonomy

The editorial traces the origins of social bots to early automated systems designed for narrow tasks such as content scheduling, customer support, or basic information retrieval. These early bots followed rigid rules and displayed little adaptability. Their impact, while noticeable, was constrained by their limited capacity to respond to complex social cues or evolving conversational contexts.

That constraint has now largely disappeared. The authors argue that large language models have enabled a leap from scripted automation to cognitive autonomy. Modern social bots can now generate context-aware responses, adjust tone and rhetoric to specific audiences, and sustain multi-turn conversations that closely resemble human interaction. This capability has significantly blurred the distinction between human and machine participants in online environments.

The editorial highlights data showing that automated traffic surpassed human-generated traffic for the first time in 2024, signaling a tipping point in digital activity. Much of this growth is attributed to AI-driven agents capable of operating continuously, coordinating with other bots, and adapting behavior based on real-time feedback. These agents can infiltrate online communities, amplify narratives, and exploit algorithmic recommendation systems with unprecedented efficiency.

At an individual level, the study notes that social agents can now engage in strategic persuasion. By tailoring language, sentiment, and framing to specific demographic or psychological profiles, these systems can influence opinions more effectively than earlier bot generations. At a collective level, networks of agents can simulate social consensus, distort perceived popularity, and reinforce echo chambers, amplifying polarization and misinformation.

This evolution challenges existing detection methods. Traditional bot detection tools often rely on identifying repetitive behavior, limited vocabulary, or static posting patterns. In contrast, modern agents exhibit variability, emotional nuance, and long-range conversational coherence, making them harder to distinguish from human users.

Multimodal intelligence and the expansion of bot capabilities

Social agents increasingly operate across text, images, audio, and video, enabling richer interaction and more convincing impersonation. Visual grounding allows agents to interpret and generate images tied to geographic locations or real-world events, further complicating efforts to authenticate online content.

The editorial identifies perceptual robustness as a critical research frontier. As synthetic media becomes more realistic, distinguishing authentic user-generated content from AI-generated material requires systems that can adapt to shifting data distributions. Agents can now modify their behavior dynamically to evade detection, forcing defensive systems to operate continuously rather than relying on static models.

The authors also point to continual learning as a defining feature of next-generation social agents. Unlike earlier systems that degraded over time or required retraining, modern agents can update internal representations incrementally, tracking emerging topics and adapting to new social contexts without catastrophic forgetting. This capability allows them to remain relevant during fast-moving events such as elections, crises, or market volatility.

The editorial extends the discussion to embodied intelligence, where social agents interact not only in digital spaces but also through physical systems. Advances in robotics, tactile sensing, and environmental perception are enabling agents to operate in real-world settings, from service environments to autonomous infrastructure. While these developments open new opportunities in areas such as healthcare and logistics, they also introduce risks when combined with persuasive or deceptive social behavior.

Underlying these capabilities is the need for resilient communication infrastructure. The study highlights research on alternative network architectures, including high-altitude and stratospheric systems, designed to support distributed autonomous agents in environments where traditional connectivity fails. Such infrastructure could allow coordinated agent swarms to operate across vast geographic areas, further amplifying their reach and influence.

Governance, detection, and the future of Human–AI interaction

The key challenge has shifted from simply detecting social bots to governing their interaction with human society. As agents become more autonomous and persuasive, the consequences of misuse grow more severe. The authors warn that current governance frameworks are ill-equipped to address systems that can reason, adapt, and operate at scale without direct human oversight.

One major concern is the erosion of trust. When users cannot reliably distinguish between human and machine participants, the social fabric of online platforms weakens. This uncertainty can undermine democratic processes, distort public debate, and reduce confidence in digital communication channels. The editorial stresses that authentication mechanisms must evolve alongside AI capabilities to preserve accountability.

Another challenge lies in intent understanding. Modern agents can pursue complex goals, sometimes emerging implicitly from training objectives rather than explicit programming. Without robust alignment mechanisms, these systems may optimize for engagement or influence in ways that conflict with societal values. The authors call for research that integrates ethical constraints directly into agent architectures rather than treating ethics as an external control layer.

The study also highlights the limitations of reactive policy responses. Regulatory frameworks often lag technological development, addressing harms only after they manifest. Given the pace of AI advancement, the authors argue for proactive governance that anticipates emerging risks. This includes cross-disciplinary collaboration between computer scientists, social scientists, legal scholars, and policymakers.

Importantly, the editorial does not frame social agents solely as a threat. The authors acknowledge their potential for positive applications, including large-scale social simulation, crisis response coordination, and improved human–machine collaboration. In controlled environments, agent-based simulations can provide valuable insights into social dynamics, policy outcomes, and collective behavior.

However, realizing these benefits requires careful boundary setting. The society is entering a phase where human and machine intelligence are increasingly intertwined. Managing this relationship responsibly will depend on transparency, robust infrastructure, and shared norms governing acceptable AI behavior, the study concludes.