AI Glossary
Social Bots – Short Explanation
A social bot is an automated software program that operates social media accounts and interacts with users without direct human involvement. Depending on how it is configured, a social bot can post content, share updates, like posts, follow accounts, comment on discussions, and engage with other users automatically.
Not all social bots are harmful. Some are designed to provide useful services, such as sharing earthquake alerts, weather warnings, public transport updates, or breaking news. However, many social bots are created to manipulate online conversations, amplify specific narratives, spread misinformation, or create the appearance of widespread public support where little or none exists.
Social Bot vs. Chatbot
Although the terms are often used interchangeably, social bots and chatbots serve different purposes.
A chatbot is designed to communicate directly with users. It answers questions, provides information, and assists with tasks through websites, messaging apps, and customer support channels.
A social bot, by contrast, is designed to operate within social networks. Its primary goal is often to increase engagement, amplify content, influence discussions, or grow an account’s visibility. Earlier social bots were largely limited to liking, sharing, or reposting content automatically. Today, many use large language models (LLMs) to generate human-like responses and participate in conversations.
What Are Social Bots Used For?
Legitimate Uses
Some social bots provide genuine value and are openly identified as automated accounts. Common examples include:
- Weather and emergency alert systems
- Public transport updates
- Automated news feeds
- Customer service channels
- Event notifications
These bots help distribute information quickly and consistently without requiring constant human oversight.
Malicious Uses
Malicious social bots are frequently used to influence public opinion, manipulate engagement metrics, spread misinformation, and support coordinated influence campaigns.
One of the most widely cited studies on political bot activity, Social Bots Distort the 2016 U.S. Presidential Election Online Discussion, found that social bots participated in election-related discussions on Twitter and generated nearly one-fifth of all election-related conversations during the final stages of the campaign.
Studies have repeatedly shown that automated accounts can help spread misleading content and amplify divisive narratives online.
Common malicious uses include:
- Amplifying political narratives
- Spreading misinformation and propaganda
- Artificially inflating follower counts and engagement metrics
- Promoting scams and phishing campaigns
- Manipulating financial and cryptocurrency discussions
- Coordinating influence operations across multiple platforms
How Common Are Social Bots?
Determining the exact number of social bots is difficult because sophisticated bots are specifically designed to avoid detection. Available research though suggests that automated activity now represents a substantial share of online traffic.
According to the 2025 Imperva Bad Bot Report, automated traffic accounted for 51% of all internet traffic in 2024, marking the first time non-human activity exceeded human-generated traffic online. The report also found that malicious bots alone were responsible for 37% of all internet traffic.
Social media platforms are particularly affected. Researchers Lynnette Hui Xian Ng and Kathleen Carley reported in A Global Comparison of Social Media Bot and Human Characteristics that approximately 20% of social media activity surrounding major global events originates from bot accounts. Their study examined nearly 200 million users across multiple countries and events, providing one of the most comprehensive analyses of bot behaviour to date.
How Social Bots Have Changed with AI
The earliest social bots were relatively simple. They followed fixed rules, posted content on schedules, and often repeated identical messages.
Over time, bot developers adopted increasingly sophisticated techniques. Bots began mimicking human behavior by varying posting frequency, maintaining realistic activity schedules, and using AI-generated profile images that could not be traced through reverse image searches.
Generative AI has made social bots considerably more convincing. Instead of posting pre-written messages, modern bots can generate original content, respond to comments, and adapt their language to different audiences in real time. Some influence operations now rely on synthetic personas: entirely fabricated identities supported by AI-generated profile photos, detailed biographies, and months of seemingly authentic posting history.
Tip:
Training social bots to behave convincingly at scale requires large volumes of domain‑specific, human‑annotated data – including linguistic patterns, user interactions, and sentiment cues.
LXT supports your bot‑training pipeline with enterprise‑grade datasets and annotation services designed for real‑world digital ecosystems.
How to Detect a Social Bot
Detecting sophisticated social bots has become increasingly difficult, but several warning signs remain useful.
Examine the Profile Photo
Many bots still use stolen images or AI-generated profile pictures. Reverse image search tools such as Google Lens can help determine whether a photo appears elsewhere online.
Check Posting Patterns
Human behaviour is naturally irregular. Accounts that post continuously around the clock or maintain highly consistent activity patterns for months may be automated.
Review Account History
An account with thousands of followers but very little posting history may indicate artificial growth or bot activity.
Read the Comments
Basic bots often post repetitive comments or generic responses that fail to engage meaningfully with the discussion. While AI-powered bots are becoming more sophisticated, many still lack the personal detail and context that characterize genuine user interactions.
Compare Followers and Engagement
A large audience combined with unusually low engagement can indicate inflated follower numbers or automated amplification.
Use Bot Detection Tools
Tools such as Botometer analyze account behaviour and compare it with known bot characteristics. Although no detection system is perfect, these tools can provide useful indicators of whether an account behaves like a bot.
Training Social Bots
To perform convincingly and serve their intended purpose, social bots require training based on vast datasets. These bots learn to mimic human behavior by processing annotated data that includes linguistic patterns, user interaction sequences, sentiment cues, and topical relevance. Training involves both supervised learning using labeled examples and reinforcement techniques where bots improve over time based on feedback loops. The more diverse and high-quality the data, the more convincing and adaptive a social bot becomes.
Conclusion
Social bots have evolved from simple automation tools into sophisticated AI-powered accounts capable of influencing online discussions at scale. While some serve legitimate purposes, others are used to manipulate engagement, spread misinformation, or imitate real users. Understanding how social bots work and how they are trained can help individuals and organizations better identify automated behaviour online.