Social bot

Imagine a conversation on social media—but one of the participants isn't human. A social bot, sometimes called a social AI or algorithm, is an autonomous software agent designed to communicate and interact on these platforms. Their pervasive presence and multifaceted uses—from marketing to political influence—make understanding them crucial in our increasingly digital age. Social bots are sophisticated automated accounts designed to communicate and influence on social media, often blurring the line between machine and human. Their applications range from benign customer service to malicious activities like spreading misinformation and orchestrating phishing attacks. Detecting social bots is an ongoing challenge, as they rapidly evolve to mimic human behavior, prompting constant updates in detection methods and legal regulations.

Source: Wikipedia

AI Summary

Social bots are sophisticated automated accounts designed to communicate and influence on social media, often blurring the line between machine and human.
Their applications range from benign customer service to malicious activities like spreading misinformation and orchestrating phishing attacks.
Detecting social bots is an ongoing challenge, as they rapidly evolve to mimic human behavior, prompting constant updates in detection methods and legal regulations.

Welcome to the World of Social Bots

A social bot is essentially a software agent designed to communicate autonomously on social media platforms. These digital entities can range from simple automated response systems to highly advanced artificial intelligence capable of mimicking natural human dialogue. They can operate independently or with partial human oversight, often referred to as 'hybrid' bots.

The Many Faces of Automation

These sophisticated bots aren't confined to obscure corners of the internet; they thrive on popular platforms like Twitter, Instagram, Facebook, and YouTube. Their functions are incredibly diverse, reflecting a wide spectrum of intent—from helpful to deceptive.

Manipulating Perception

One of their most common uses is to inflate a user's perceived popularity. Social bots generate fake likes, reposts, comments, or followers, creating a deceptive impression of widespread influence and engagement that can draw in real human followers. There's even a thriving black market where individuals, brands, and organizations can purchase these artificial metrics to appear more authoritative and desirable.

Customer Service in the Digital Age

Not all bots are nefarious; many corporations deploy them for essential customer service. These automated agents handle high volumes of support requests, providing instant, affordable responses to common questions and guiding users towards solutions or further assistance. It's an efficient, cost-effective approach to managing digital interactions.

However, the rise of these virtual assistants often has a significant consequence. Their increasing capability can lead to companies reducing their human customer service staff, reflecting a broader shift towards automation in various industries.

Shaping Public Opinion

Social bots are also powerful tools for influencing what we think and believe. They can flood social media with posts supporting specific products, companies, or even political campaigns, giving the illusion of widespread grassroots support. This can create a distorted perception of public consensus and sway opinions.

Such orchestrated campaigns can have significant real-world consequences. They can sway market trends, influence the direction of stock prices, or even impact election outcomes by creating a false sense of popular momentum around certain candidates or issues. They can also fuel fads and trends by creating an artificial buzz.

The Dark Side: Phishing and Propaganda

Unfortunately, social bots are frequently weaponized for malicious purposes, such as amplifying phishing attacks. These bots lure users into clicking deceptive links that lead to malicious websites, designed to steal passwords, personal data, or install malware. They often use URL shorteners like TinyURL or bit.ly to mask their true destination, increasing the likelihood of a click.

The illusion of popularity created by fake followers and high engagement makes these scams more believable. Victims are more likely to trust a link or message if it appears to come from an account with a large, active, and seemingly legitimate following.

Beyond phishing, bots are central to computational propaganda, algorithmic curation, and radicalization. They can selectively disseminate information, pushing users towards extremist views or even facilitating 'influence-for-hire' schemes where entire social media accounts are sold to spread specific messages or narratives.

A Brief History of Digital Mimicry

The concept of machines mimicking human intelligence dates back to the very dawn of computing. Visionaries like Alan Turing laid the groundwork in the 1950s with his famous Turing Test, exploring the profound question of whether a machine could exhibit intelligent behavior indistinguishable from a human.

Decades later, programs like Joseph Weizenbaum’s ELIZA emerged in the 1960s. Considered one of the first chatbots, ELIZA simulated natural conversation using simple pattern matching, most famously with its 'DOCTOR' script, which mimicked a Rogerian psychotherapist by reflecting user statements as questions.

With the explosion of social media in the early 2000s, these automated agents found their perfect habitat. Early marketing firms quickly utilized social bots on platforms like MySpace to artificially inflate user page activity, making individuals appear more popular and influential to a wider audience than they actually were.

Navigating the Rules of Engagement

Twitter quickly became a hotbed for bot activity, though its terms of service generally prohibit bots used for spamming or unsolicited liking and following. However, the platform's API does permit some automation for 'entertainment, informational, or novelty purposes,' striking a delicate balance.

Other platforms, such as Reddit and Discord, also allow bots, provided they adhere to strict policies against harmful content and abusive behavior. Social media companies continuously develop automated tools to filter out bot messages, though the challenge of complete detection remains a significant, ongoing battle.

The Ongoing Battle: Detection and Regulation

Identifying social bots is a complex and ever-evolving challenge. Early bots were often easy to spot due to their superhuman posting rates, but modern bots have grown incredibly sophisticated, exhibiting nuanced human-like activity and behavioral patterns. They can even achieve 'artificial social proof'—the illusion of widespread human endorsement.

What Gives Them Away?

To truly unmask a social bot, a combination of sophisticated pattern detection techniques is usually required. Researchers look for a variety of tell-tale signs in their profiles and activities, often applying multiple criteria simultaneously to confirm their artificial nature.

These are some of the key indicators researchers analyze.

cartoon figures as user pictures (or random stolen ones) reposting rate and temporal patterns sentiment expression in messages followers-to-friends ratio and username length variability in posted messages engagement rates (likes/followers) time series analysis of posts

Despite these advanced methods, bots are increasingly difficult to understand and remove. Their chameleon-like ability to change behavior, combined with their sheer volume across every platform, makes detection a constant arms race. In 2017, for instance, an estimated 15%—or 48 million—of Twitter's 319 million users were believed to be bots.

Tools in the Fight

Researchers and platforms employ various tools and techniques in this ongoing struggle. Botometer, a public web service, analyzes Twitter account activity using over a thousand features to score the likelihood of an account being a bot. Early detection methods even included 'honeypot' accounts, specifically designed to lure and identify spam bots.

More advanced techniques now leverage artificial intelligence, including active learning, feature engineering, and both unsupervised and supervised learning, to discover correlations and identify new bot behaviors. There's even research applying Benford's Law to analyze the frequency distribution of leading digits in data to detect malicious online activity, first introduced in 2020 at the University of Pretoria.

Some of the most concerning bot activities involve synchronized operations, where numerous orchestrated accounts work together to amplify messages. For example, ISIS used Twitter bots to push their content onto 'Hot Lists' and significantly expand its reach for propaganda purposes, a tactic that can also be used for stock market manipulation.

The Legal Landscape

Given the difficulty of distinguishing legitimate automation from deceptive bot activity, enforcing legal regulations is a complex undertaking. Yet, the potential for bots to autonomously shape public opinion through misinformation and manipulation has spurred legislative action around the world.

In the United States, states like California have led the way with laws such as the Bolstering Online Transparency (B.O.T.) Act of 2019. This act makes it illegal for automated software to impersonate humans with the intent to influence purchasing or voting decisions on social media. Utah and Colorado have followed suit with similar restrictions.

The European Union has introduced the Artificial Intelligence Act (AI Act), the first comprehensive law governing AI use. It mandates transparency, requiring that AI-generated content on social media be clearly marked, specifically to prevent social bots from mimicking human behavior without clear disclosure.

Social Bots Across Platforms

Instagram, with its massive user base, has also grappled with pervasive bot infiltration. In 2018, it was estimated that up to 10% of its billion active monthly users were controlled by automated social bots, primarily used for engagement—liking, following, and commenting to create false virality for users.

Instagram's parent company, Meta (formerly Facebook), has committed significant resources, hiring thousands to bolster security and combat the proliferation of bots and malicious posts. This increased vigilance, alongside sophisticated detection methods, has led to challenges for botting companies, as platforms impose interaction limits and restrict their access to payment services.

Twitter's Enduring Challenge

Twitter's bot problem is exacerbated by the relative ease of creating and maintaining these accounts, often facilitated by its APIs. In 2017, CNBC reported that approximately 15% of Twitter's users—a staggering 48 million accounts—were bots. While the platform claims to remove about 1 million spam bots daily, it remains an ongoing battle.

Not all Twitter bots are malicious, however. Many are used for beneficial automation, such as scheduling tweets, downloading videos, setting reminders, or issuing natural disaster warnings. Twitter even encourages the use of its API for certain levels of automation that genuinely enhance user experience and interaction.

Meta's AI Characters and SocialAI's Vision

Looking ahead, Meta announced in 2025 plans to allow users to create AI characters on Instagram and Facebook, complete with bios and profile pictures, capable of generating and sharing AI-powered content. These 'verified' AI accounts, reportedly unblockable by humans, began appearing, further blurring the lines between human and artificial presence online. In a different vein, Michael Sayman, a former product lead at Google and Facebook, launched SocialAI in 2024—an app designed solely for interaction with AI bots, completely devoid of human interaction, a concept that has even been linked to the intriguing 'Dead Internet Theory'.

Article

Social bot

A social bot, refers to fully or partially automated social media accounts designed to perform most regular users’ actions, such as liking, posting content, and chatting with other users. Although their levels of autonomy vary, and often include a human-in-the-loop, social bots can use artificial intelligence to perform social media actions and can use large language models to mimic human dialogue. Social bots can operate alone or in groups that coordinate messaging as part of a network of coordinated inauthentic behavior. Social bots are often used to perform ad fraud by artificially boosting viewership and engagement metrics and to spread disinformation on social media.

Uses

Social bot

Social bots are used for a large number of purposes on a variety of social media platforms, including Twitter, Instagram, Facebook, and YouTube. One common use of social bots is to inflate a social media user's apparent popularity, usually by artificially manipulating their engagement metrics with large volumes of fake likes, reposts, or replies. Social bots can similarly be used to artificially inflate a user's follower count with fake followers, creating a false perception of a larger and more influential online following than is the case. The use of social bots to create the impression of a large social media influence allows individuals, brands, and organizations to attract a higher number of human followers and boost their online presence. Fake engagement can be bought and sold in the black market of social media engagement.

Corporations typically use automated customer service agents on social media to affordably manage high levels of support requests. Social bots are used to send automated responses to users’ questions, sometimes prompting the user to private message the support account with additional information. The increased use of automated support bots and virtual assistants has led to some companies laying off customer-service staff.

Social bots are also often used to influence public opinion. Autonomous bot accounts can flood social media with large numbers of posts expressing support for certain products, companies, or political campaigns, creating the impression of organic grassroots support. This can create a false perception of the number of people who support a certain position, which may also have effects on the direction of stock prices or on elections. Messages with similar content can also influence fads or trends.

Many social bots are also used to amplify phishing attacks. These malicious bots are used to trick a social media user into giving up their passwords or other personal data. This is usually accomplished by posting links claiming to direct users to news articles that would in actuality direct to malicious websites containing malware. Scammers often use URL shortening services such as TinyURL and bit.ly to disguise a link's domain address, increasing the likelihood of a user clicking the malicious link. The presence of fake social media followers and high levels of engagement help convince the victim that the scammer is in fact a trusted user.

Social bots can be a tool for computational propaganda. Bots can also be used for algorithmic curation, algorithmic radicalization, and/or influence-for-hire, a term that refers to the selling of an account on social media platforms.

History

Social bot

Bots have coexisted with computer technology since the earliest days of computing. Social bots have their roots in the 1950s with Alan Turing, whose work focused on machine intelligence with the development of the Turing Test. The following decades saw further progress made towards the goal of creating programs capable of mimicking human behavior, notably with Joseph Weizenbaum’s creation of ELIZA. Considered to be one of the first Chatbots, ELIZA could simulate natural conversations with human users through pattern matching. Its most famous script was DOCTOR, a simulation of a Rogerian psychotherapist that was programmed to chat with patients and respond to questions.

With the growth of social media platforms in the early 2000s, these bots could be used to interact with much larger user groups in an inconspicuous manner. Early instances of autonomous agents on social media could be found on sites like MySpace, with social bots being used by marketing firms to inflate activity on a user’s page in an effort to make them appear more popular.

Social bots have been observed on a large variety of social media websites, with Twitter being one of the most widely observed examples. The creation of Twitter bots is generally against the site’s terms of service when used to post spam or to automatically like and follow other users, but some degree of automation using Twitter’s API may be permitted if used for “entertainment, informational, or novelty purposes.” Other platforms such as Reddit and Discord also allow for the use of social bots as long as they are not used to violate policies regarding harmful content and abusive behavior. Social media platforms have developed their own automated tools to filter out messages that come from bots, although they cannot detect all bot messages.

Legal regulation

Social bot

Twitter bots posting similar messages five days after the 2016 United States elections

Due to the difficulty of recognizing social bots and separating them from "eligible" automation via social media APIs, it is unclear how legal regulation can be enforced. Social bots are expected to play a role in shaping public opinion by autonomously acting as influencers. Some social bots have been used to rapidly spread misinformation, manipulate stock markets, influence opinion on companies and brands, promote political campaigns, and engage in malicious phishing campaigns.

In the United States, some states have started to implement legislation in an attempt to regulate the use of social bots. In 2019, California passed the Bolstering Online Transparency Act (the B.O.T. Act) to make it unlawful to use automated software to appear indistinguishable from humans for the purpose of influencing a social media user's purchasing and voting decisions. Other states such as Utah and Colorado have passed similar bills to restrict the use of social bots.

The Artificial Intelligence Act (AI Act) in the European Union is the first comprehensive law governing the use of Artificial Intelligence. The law requires transparency in AI to prevent users from being tricked into believing they are communicating with another human. AI-generated content on social media must be clearly marked as such, preventing social bots from using AI in a manner that mimics human behavior.

Detection

Social bot

The first generation of bots could sometimes be distinguished from real users by their often superhuman capacities to post messages. Later developments have succeeded in imprinting more "human" activity and behavioral patterns in the agent. With enough bots, it might be even possible to achieve artificial social proof. To unambiguously detect social bots as what they are, a variety of criteria must be applied together using pattern detection techniques, some of which are:

• cartoon figures as user pictures • sometimes also random real user pictures are captured (identity fraud) • reposting rate • temporal patterns • sentiment expression • followers-to-friends ratio • length of user names • variability in (re)posted messages • engagement rate (like/followers rate) • analysis of the time series of social media posts

Social bots are always becoming increasingly difficult to detect and understand. The bots' human-like behavior, ever-changing behavior of the bots, and the sheer volume of bots covering every platform may have been a factor in the challenges of removing them. Social media sites, like Twitter, are among the most affected, with CNBC reporting up to 48 million of the 319 million users (roughly 15%) were bots in 2017.

Botometer (formerly BotOrNot) is a public Web service that checks the activity of a Twitter account and gives it a score based on how likely the account is to be a bot. The system leverages over a thousand features. An active method for detecting early spam bots was to set up honeypot accounts that post nonsensical content, which may get reposted (retweeted) by the bots. However, bots evolve quickly, and detection methods have to be updated constantly, because otherwise they may get useless after a few years. One method is the use of Benford's Law for predicting the frequency distribution of significant leading digits to detect malicious bots online. This study was first introduced at the University of Pretoria in 2020. Another method is artificial-intelligence-driven detection. Some of the sub-categories of this type of detection would be active learning loop flow, feature engineering, unsupervised learning, supervised learning, and correlation discovery.

Some operations of bots work together in a synchronized way. For example, ISIS used Twitter to amplify its Islamic content by numerous orchestrated accounts which further pushed an item to the Hot List news, thus further amplifying the selected news to a larger audience. This mode of synchronized bots accounts can be used as a tool of propaganda as well as stock markets manipulations.

Platforms

Instagram

Instagram reached a billion active monthly users in June 2018, but of those 1 billion active users, it was estimated that up to 10% were being run by automated social bots. While malicious propaganda posting bots are still popular, many individual users use engagement bots to propel themselves to a false virality, making them seem more popular on the app. These engagement bots can like, watch, follow, and comment on the users' posts.

Instagram automation

On Instagram, the term automation is used for a range of practices, from legitimate automation via approved tools (such as publishing workflows for professional accounts) to prohibited automation that artificially manipulates engagement (e.g., automated liking, following, commenting, or mass interactions meant to simulate human activity). Instagram warns users against granting account access to third-party services that promise likes or followers, which are commonly associated with inauthentic engagement and account security risks.

Meta’s policies define inauthentic engagement as the use of networks of inauthentic assets to generate substantial quantities of fake engagement designed to appear authentic, and treat such behavior as a policy violation across Meta platforms, including Instagram.

Instagram also provides official APIs for professional accounts through the Instagram Platform (part of Meta for Developers), which enables programmatic access to certain account data and approved interactions, subject to platform rules and rate limits. Research on Instagram has also examined the presence of fake and automated accounts and their role in producing fake engagement, as well as methods for detecting such accounts.

Around the same time, the platform achieved the 1 billion monthly user plateau. Facebook (Instagram and WhatsApp's parent company) planned to hire 10,000 to provide additional security to their platforms; this would include combatting the rising number of bots and malicious posts on the platforms. Due to increased security on the platform and the detection methods used by Instagram, some botting companies are reporting issues with their services because Instagram imposes interaction limit thresholds based on past and current app usage, and many payment and email platforms deny the companies access to their services, preventing potential clients from being able to purchase them.

Twitter

Twitter's bot problem is caused by the ease of creating and maintaining them. The ease of creating the account as and the many APIs that allow for complete automation of the accounts are leading to excessive amounts of organizations and individuals using these tools to push their own needs. CNBC claimed that about 15% of the 319 million Twitter users in 2017 were bots; the exact number is 48 million. As of July 7, 2022, Twitter is claiming that they remove 1 million spam bots from their platform every day.

Some bots are used to automate scheduled tweets, download videos, set reminders and send warnings of natural disasters. Those are examples of bot accounts, but Twitter's API allows for real accounts (individuals or organizations) to use certain levels of bot automation on their accounts and even encourages the use of them to improve user experiences and interactions.

SocialAI

SocialAI, an app created on September 18, 2024, was created with the full purpose of chatting with only AI bots without human interaction. Its creator was Michael Sayman, a former product lead at Google who also worked at Facebook, Roblox, and Twitter. An article on the Ars Technica website linked SocialAI to the Dead Internet Theory.