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is just as important as their policies on bots. In MUD gaming environments, users could easily access and modify code to build bot characters in the game; in IRC and Usenet, bots were a necessary infrastructural part of interacting with the platform, and users often enjoyed building their own. Similarly, early 2000s virtual worlds like Second Life were designed in such a way that bot development became more accessible for average users (Lugrin et al., 2008). Now, perhaps most significantly for the era of social media, Twitter’s infrastructure is extremely welcoming to bots (and was even more so in the platform’s early days) (Ferrara et al., 2014; Zi et al., 2010). Twitter’s Application Programming Interface (API) makes building and connecting bots to the platform easy, and its infrastructure has arguably done more to democratize bot development and drive their evolution than any other platform or website in bot history.

Different Types of Bots

      One problem with understanding bots is the term’s ambiguity: the word has several distinct (though often overlapping) meanings. This makes it particularly difficult for policymakers trying to write sensible technology legislation. Indeed, in the words of two communications scholars, the “multiple forms of ambiguity are responsible for much of the complexity underlying contemporary bot policy” (Gorwa & Guilbeault, 2018).

      People have been trying to define what bots are since the 1990s, and multiple bot “typologies” have been proposed by journalists, researchers, and academic experts seeking to organize and categorize the profusion of different bots. These typologies vary from informal groupings to more formal taxonomies (Gorwa & Guilbeault, 2018; Leonard, 1997; Maus, 2017; Stieglitz et al., 2017), and some limit themselves to specific subtypes of bots, such as news bots or political bots (DiResta et al., 2017; Lokot & Diakopoulos, 2016). However, the rapid pace of bot evolution means that these taxonomies can quickly break or become out-of-date. Nonetheless, these efforts are extremely important and provide us with footholds with which to navigate the nascent and ever-evolving landscape of bots and their uses, capabilities, and characteristics.

Recognizing the rapidly changing landscape in bot and disinformation research, the bot categories we discuss here are the most important ones at the time we are writing this book. These categories have largely remained relevant for understanding and analyzing bot behavior in the past three decades. This is not an exhaustive list, but it is a useful introduction to the field. Armed with these categories, the reader will be able to grasp modern bots’ main uses in the political, social, commercial worlds.

APIs – How bots connect to websites and social media

      Before diving into the main categories of bots, we’d like to note the importance of Application Programming Interfaces, or “APIs” in driving bot development in the modern era, the tool through which most social media bots connect to do their work. Social bots proliferated dramatically in the early aughts and the 2010s, largely as a result of more widespread connectivity, the declining cost of computing and bandwidth, and the rise of social media. Social bots are not confined to any one particular platform – they can appear on basically any social media platform, including Twitter, Facebook, Instagram, Gab, Reddit, and YouTube; encrypted chat applications like Telegram and LINE; or regular websites more generally (Assenmacher et al., 2020; Boshmaf et al., 2011; Confessore et al., 2018; Dubbin, 2013; Massanari, 2016; Monaco, 2017; Morales, 2020; Read, 2018; S. Woolley et al., 2019). (We will talk more about how social bots are used on each platform in our chapters on social and political bots.) But the technical design of certain platforms makes them more hospitable to bot activity. This has to do with the website’s API.

APIs are a sort of platform-behind-the-platform, a place where computer programs can easily gather data and/or interact with users on social media sites. The data that computer programs can gather from APIs, as well as what actions they can perform on the site, are pre-defined by the architects of that API. For example, on Twitter, computer programs can post messages from a Twitter account, follow other users, or retweet other users’ posts, among many other things (Zi et al., 2010). Bots that use API access are generally fairly easy to program and can be easily created by people with little technical skill. Many of the bots on social media sites, especially the earliest incarnations, were relatively simple bots that used APIs (Woolley, 2020a).

      It is also possible to program bots that interact with users on social media sites without using APIs. These bots typically imitate human users by accessing a website through a browser (such as Google Chrome or Mozilla Firefox) to interact with specific parts of the website. These bots perform the same functions as human users by following a set of programmatic instructions. They can run invisibly on a computer, and for this reason are often referred to as “headless” (a reference that uses a visible browser as a metaphor for a head). Headless bot behavior can be automated using software packages such as Selenium, Puppeteer, and PhantomJS.¹² For example, a skilled developer could use Selenium to write a program that launches Mozilla Firefox, logs into Twitter, and composes a Tweet that uses the top trending hashtag in the US and includes the @-mentions of three other users who recently used the same hashtag. Generally, headless bots and scrapers require a fair amount of technical skill to program, and they are more useful for passive intelligence and data collection than for direct interaction with other users. However, tutorials for programming these bots are freely available online (Mottet, 2019).

Social bots

The first type of bot worth noting, and one of the most widespread on- and offline, is the social bot. In the broadest sense, social bots¹³ (like the other types of bots we have discussed) are automated computer programs, but this subcategory of bots is specifically designed to interact with humans. For example, ELIZA, the conversational computer program that imitated a psychotherapist, could be considered an early social bot (Weizenbaum, 1966). Recently, though, the term has taken on a very specific meaning in the popular imagination: since social media appeared in the 2000s, the term has increasingly come to mean computer programs that pose as humans on websites and social media, often designed to promote/criticize a specific product, politician, or message.

      Social bots can converse with other users on social media, but they can also be used for other purposes. Votebots can skew the results of online polls. As fake followers, social bots can be used to artificially inflate the popularity of celebrities and politicians. They can skew social media trends by promoting or amplifying content, such as commercial products or political messages. Using the same amplification techniques, they can drown out content their designers do not approve of. “Benign” or creative bots promote art or make jokes. In short, social bots are as diverse as humans.

Chatbots

      Chatbots are computer programs that are designed to converse with humans through text or speech (with varying degrees of success). The first chatbot was Weizenbaum’s ELIZA, designed in the 1960s, and chatbots remained popular in MUDs, Usenet and IRC through the 1990s. Popular modern chatbots include the AI assistant systems such as Amazon Alexa, Google Home, or Apple’s Siri and the commercial chatbots often used for online customer service.

Most chatbots work in one of two ways. Some use pattern searching and pre-composed responses to simulate conversation; this is how ELIZA operated. Others use more advanced AI techniques, such as fuzzy logic or the generation of dynamic responses based on a database (or “corpus”) of typical responses. The latest chatbots incorporate advanced machine learning techniques to boost dynamic conversational capabilities and approach human-like discourse. In 2020, OpenAI’s GPT-3 bot took chatbots’ conversational abilities to new heights using vast troves of language data and AI techniques to mimic different styles of writing (Economist, 2020). We’ll delve into these techniques more deeply in Chapter 5, Bots and Artificial Intelligence.

Service bots and bureaucrat bots

      Automated agents are often used to carry out the same tasks humans do, but much, much faster and more consistently than

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