AI Glossary
Social Bots – Short Explanation
Social bots are automated programs designed to operate on social media platforms. While some are used for legitimate purposes—like sharing news or automating customer engagement—others mimic human behavior to influence opinions, amplify misinformation, or manipulate public discourse. Their effectiveness lies in their ability to appear human, often posting, liking, and commenting in ways that shape online narratives at scale.
Table of Contents
Social Bots in the Real World
Social bots are already in use on many of the most popular social media platforms like Facebook, Twitter, Instagram, and more. By posting positive reviews of a specific product or service, they can drive consumers towards a particular company. Similarly, by posting false or misleading information, they can be used to discredit companies and individuals.
Chatbots “chat” to customers – hence the name. Social bots are not as intelligent. As simple automated programs that reside on social media platforms, at times, their only purpose is to comment, follow, or like other posts and people. Due to this simplicity of operation, multiple social bots can be managed by a single individual. Chatbots, on the other hand, often require teams of people to maintain a single chatbot.
Social Bots in the World of AI
Understanding the intricate relationship between AI, social bots, and their impact on consumer behavior is crucial, especially in sectors like finance where decisions hinge on accurate and timely data. For a deeper dive into how machine learning is transforming the finance industry, you might find this insightful article informative.
A 2016 study indicated that close to 50% of the traffic online is generated by bots. Since that time, the number of bots and the volume of traffic has only increased. Current statistics place the number of fake accounts on Facebook and Twitter at close to 15%.
Many bots have useful purposes online. They can provide updated weather and traffic information, information about earthquakes, and curated news headlines. The distinction between these bots and other social bots is that they are clearly defined as machines.
Social bots are primarily used to influence consumers with regards to purchases and citizens from a political point of view. They work by searching social networks for posts on a particular topic or conversation that matches their parameters. They then simulate human conversation (like a chatbot), often posting further deceptive or manipulative information. Social bots work by pretending to be humans.
Social bots are successful because they pretend to be human. Some of them actually even have fake sleep-wake cycles to better mimic human behavior. While they are programmed to work independently, in concert they can be very powerful. In fact, social bots are thought to have impacted the Brexit vote in 2016 and even the US presidential election. Understanding the role of these bots in our digital ecosystem is vital, especially when developing AI models that can discern between human and bot-generated content. Enhancing these models with machine learning datasets is a step towards achieving more accurate and reliable AI applications.
Tip:
AI Training Data provider LXT delivers customized datasets through managed crowdsourcing, ensuring bots are trained with real-world, domain-specific data. This level of quality is critical for social bots that aim to interact seamlessly within dynamic digital environments.
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.