Lynnette Hui Xian Ng Center for Informed Democracy & Social - cybersecurity (IDeaS), Societal and Software Systems Carnegie Mellon University, Pittsburgh, PA 15213 lynnetteng@cmu.edu Kathleen M. Carley Center for Informed Democracy & Social - cybersecurity (IDeaS), Societal and Software Systems Carnegie Mellon University, Pittsburgh, PA 15213

Abstract

Chatter on social media about global events comes from 20% bots and 80% humans. The chatter by bots and humans is consistently different: bots tend to use linguistic cues that can be easily automated (e.g., increased hashtags, and positive terms) while humans use cues that require dialogue understanding (e.g. replying to post threads). Bots use words in categories that match the identities they choose to present, while humans may send messages that are not obviously related to the identities they present. Bots and humans differ in their communication structure: sampled bots have a star interaction structure, while sampled humans have a hierarchical structure. These conclusions are based on a large-scale analysis of social media tweets across million users across 7 events.

Social media bots took the world by storm when social-cybersecurity researchers realized that social media users not only consisted of humans, but also of artificial agents called bots. These bots wreck havoc online by spreading disinformation and manipulating narratives. However, most research on bots are based on special-purposed definitions, mostly predicated on the event studied. In this article, we first begin by asking, “What is a bot?", and we study the underlying principles of how bots are different from humans. We develop a first-principle definition of a social media bot. This definition refines existing academic and industry definitions: “A Social Media Bot is An automated account that carries out a series of mechanics on social media platforms, for content creation, distribution and collection, and/or for relationship formation and dissolutions." With this definition as a premise, we systematically compare the characteristics between bots and humans across global events, and reflect on how the software-programmed bot is an Artificial Intelligent algorithm, and its potential for evolution as technology advances. Based on our results, we provide recommendations for the use of bots and for the regulation of bots. Finally, we discuss three open challenges and future directions of the study of bots: Detect, to systematically identify these automated and potentially evolving bots; Differentiate, to evaluate the goodness of the bot in terms of their content postings and relationship interactions; Disrupt, to moderate the impact of malicious bots, while not unsettling human conversations.

keywords:

social media bot, psycholinguistic analysis, social identity, network analysis

Introduction

The notion of “bots” on social media is ubiquitous across many scholarship. These studies captured a range of different social phenomena where bots operate: politics, hate speech, toxicity and so forth. Bots were used to boost the follower count of politicians in the 2011 Arab Springs uprising, generating false impressions of popularity [1, 2]. In the same uprising, bots flooded news streams to interrupt efforts of political dissidents [1, 2]. In the US 2020 elections, bots augmented human users in strategic communications, and actively distorted or fabricated narratives to create a polarized society [3, 4, 5]. More recently, bots aggressively pushed anti-vaccine narratives and conspiracy theories on social media during the 2021 coronavirus pandemic [6, 7]. Bots applied social pressure to influence humans to favor the anti-vaccine ideology [8, 3]. When the tension of the online ideologies are sufficiently strong, and the spread sufficiently wide, these ideologies spillover to the offline world, resulting in protests, riots and targeted hate-speech [9, 10, 11, 12]. Social media bots gained further media attention in 2022 when Elon Musk proclaimed that at least 20% of the Twitter users were bots, which were influencing content quality [13]. Musk later bought the platform, and took steps to curtail the bot population in a “global bot purge", which includes removing huge amounts of bots, and charging newly created accounts to post and interact on the platform [14].

Much research on social media bots involve constructing bot detection algorithms and applying bot detection algorithms to analyze bot activity during an event. Bot detection algorithms typically extract a series of features from user and post data, then build a supervised machine learning model which classifies the likelihood of a user being a bot or a human [15]. These machine learning models range from logistic regression [16], to random forests [17], to ensemble of classifiers [18, 4], to deep learning methods [19, 20]. Another technique of bot detection is graph-based methods, which infers the probability of a user being a bot by its connections, i.e. friends [21, 22]. Most recently, Large Language Models (LLMs) are incorporated in bot detection algorithms to handle the diverse user information and content modalities [23]. These bot detection classifiers have been used to study bot behavior in many events, ranging from political events [24, 4, 25, 26] to natural disasters [27, 27] to the spread of information and opinions on social media [8, 28, 29].

Although researchers have built automatic bot detection classifiers, behavioral studies show that humans are unable to differentiate between the bot and human user [30]. In fact, the identification of bots by security students are akin to random guesses [31]. Consequently, it is important to study bots and their characteristic nature and activity patterns. Our study is positioned within the social cybersecurity realm, which studies how the digital environment, particularly bots, can be exploited to alter the content and community relationships [32].

This article is being driven by looking at a first principles approach to bots. We ask the following research questions:

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RQ1: What is a social media bot? Many studies are predicated on a general purpose understanding of a bot, or a specific definition particular to the event of study. Instead, we pare down the definition of a bot into its treatment of the core elements of a social media platform (users, content, relationships).
•
RQ2: How does the nature of a bot differ from a human? Systematically, we look at the difference between bots and humans. We use a large scale dataset from Twitter/X that spans over 200 million users, and analyzed four aspects: volume of bot/human user types, use of linguistic cues, use of identity terms, and social interactions.

After an examination of the social media bot, we discuss how a bot is also an Artificial Intelligent (AI) agent, and its potential evolution alongside technological advancements. We finally follow with a discussion of the open research challenges of the study of bots to encourage future studies in this field. The challenges we identify reflect the definition of a bot: Detect, to systematically identify these automated and evolving bots; Differentiate, to evaluate the goodness of the bot in terms of their content postings and relationship interactions; and Disrupt, to moderate the impact of malicious bots, while not unsettling digital human communities.

What is a Social Media Bot?

The term “bot" has become a pervasive metaphor for inauthentic online users [8, 33]. Most social media users have an implicit understanding of a bot, as do most researchers [30]. Table 2 summarizes some of the recent definitions of bots retrieved from academic literature. The security industry also watches social media bot accounts, and Table 3 summarizes definitions from industry sources. Each of the definition grasps one or more relevant properties (highlighted in bold) of a social media bot, yet are not sufficiently comprehensive to describe the bot. Some of these relevant properties are: “automated", “interacts with humans", “artificial agents".

One of the problems with existing definitions is that they often define bots as being malicious and they highlight the nefarious use of bots [34, 35, 5, 36, 37]: “malicious actors", “public opinion manipulation", “malicious tasks" [38, 18, 39]. Most often, the study of bots is established upon nefarious tasks: election manipulation, information operations, even promoting extremism [33, 40, 34]. The exact same technology can be used in both good and bad ways. There are plenty of good bots [41, 42, 43]. Bots provide notifications and entertainment [44], such as the @CoronaUpdateBot found in our dataset which posts critical public health information. Bots support crisis management efforts by gathering the needs and combined locations of people after a disaster, for authorities and community volunteers to identify crucial areas and providing help [45]. Chat bots provide emotional support during stress [46] and continue bonds in times of grieve [47]. Celebrities and organizations use bots to facilitate strategic communications with their fans and clients [3, 48].

In essence, a bot is a computer algorithm. As an algorithm, a bot is neither bad or good. It is the persona it is afforded to that determines the goodness of its use. We develop a generic definition of a bot. The definition is independent of the use of the bot. The determination of the use of the bot warrants separate treatment beyond this paper. Regardless of whether a bot is used for good or ill, the behavioral characteristics of bots remain the same.

To better describe the social media bot, we first need to characterize the environment in which it lives: the social media platform. Within a social media platform, there are three main elements: users, content and relationships [49]. Users are represented by their virtual accounts, and are the key actors driving the system, creating and distributing information. Content is the information generated by users on the platform. Relationships are formed from the user-user, user-content and content-content interactions.

After distilling a social media platform into its core building blocks, it follows that definition of a social media bot should be based on the foundations of a social media platform as first principles. The presence of these components in each of the reference definitions are broken down in Table 4. The first principles of a bot are:

•
User: An automated account that carries out a series of mechanics. A key characteristic of bots is its programmability, which give it its artificial and inauthentic characteristic. Automation is an aspect that has been iterated in all the reference definitions. The key here is that a bot is automated. The model underlying the automation does not matter; any model can be applied equally well to humans and bot. A bot could be built to mimic humans, or it could be built to optimize other functions. For example, some studies describe bots in terms of its mimicry of humans [33, 50], but others observe that bots eventually influence the social space such that humans mimic bots [51, 52].
•
Content: for content creation, distribution, and collection and processing. Bots often generate their content in bulk to distribute a certain ideology [24, 53], such as a good portrayal of their affiliated country [54]. Instances where bots perform content distribution is where the spread fake news and disinformation content [28, 55, 56], or when they distribute general news information [57]. Bots in the form of web crawlers and scrapers download and index data from social media in bulk [58], and sometimes process the data to perform functions like analyzing sentiment of opinions [59].
•
Relationships: for relationship formation and dissolution. In other words, forming a relationship online means to connect with other users via post-based (i.e., retweet, mention) or user-based (i.e., following, friend) mechanisms. Dissolving a relationship means to destroy a connection by forcing users to leave a community. Bots are an actively form and destroy relationships on social media platforms. An example of the formation of post-based relationship is the active amplification of narratives. This technique is mostly employed in the political realm where the bots retweet political ideology in an organized fashion [24, 60, 61]. User-based relationships can grow through coordinated fake-follower bots, that are used to boost online popularity [62], or can be dissolved through toxic bots that spread hate and directed religious ideologies [63, 40], causing users to break away from the community [64].

Figure 1 reflects a first principles definition of a social media bot. A Social Media Bot is “An automated account that carries out a series of mechanics on social media platforms, for content creation, distribution, and collection and processing, and/or for relationship formation and dissolutions." This definition displays the possibilities of mechanics that the bot account can carry out. A bot does not necessarily carry out all the mechanics. The combinations of mechanics that a bot carries out thus affects the type of bot it is and the role it plays within the social media space, and as shown in Table 1, those mechanics can be used for either good or bad. Bot types can be named for their actions or for their content. For example, a bot that carries out relationship formation between two different communities, and does not do any content mechanics can be classified as a bridging bot [16]. We illustrate a few types of bots and their use for good and bad in Table 1. Note that this list is not meant to be an exhaustive list but an illustrative list of the variety of bots in the social media space.

Figure 1: Definition of Social Media Bot. This definition displays the possibilities of mechanics that the bot account can carry out. A bot does not necessarily carry out all the mechanics.

Table 1: Illustration of type of bots and their role in the social media space. Note that this list is not exhaustive but illustrative.

Table 2: Definitions of “Social Media Bot" in academic literature.

Table 3: Definitions of “Social Media Bot" in industry literature

Table 4: Components of definitions of “Social media Bot"

Results

We perform a global comparison of bot and human characteristics by combining several datasets obtained from X (previously named Twitter) using the Twitter V1 Developed API. These events are: Asian Elections [25, 34], Black Panther [77], Canadian Elections 2019 [78], Captain Marvel [79], Coronavirus,[80] ReOpen America [9, 80] and US Elections 2020 [80]. In total, these datasets contain billion tweets and million users. Each user in this database is labeled as bot or human using the BotHunter algorithm [17].

How many bots are there?

Figure 2 presents the percentage of bot users within each dataset. On average, the bot volume across the events are about 20% with the bot percentage spiking up to 43% during the US Elections. This is in line with past work, where a general sample of users usually reveal a bot percentage below 30% [70], yet in a politically-charged topic (i.e. elections, tensions between countries), the bot percentage rises [80, 34]. Our estimate is also empirically consistent with Elon Musk’s estimate of 20% [13]. This finding is important for event analysis, because it provides a comparison baseline towards the percentage of bot-like users within an event. Spikes in bot user percentage beyond 20% suggest that the event and conversation has caught the interest of bot operators, and the analyst can monitor for signs of conversation manipulation.

Figure 2: Comparison of Bot volume across events. The percentage of bot users across the events are on average around 20%.

How do bots differ from humans linguistically?

We extract psycholinguistic cues from the tweets using the NetMapper software[81]. The software returns the count of each cue in the sentence, i.e., the number of words belonging to the cue in the tweet. There are three categories of cues: semantic, emotion and metadata. Semantic and emotion cues are derived from the tweet text, while metadata cues are derived from the metadata of the user. Semantic cues include: first person pronouns, second person pronouns, third person pronouns and reading difficulty. Emotion cues include: abusive terms, expletives, negative sentiment, positive sentiment. Metadata cues include: the use of mentions, media, URLs, hashtags, retweets, favorites, replies, quotes, and the number of followers, friends, tweets, tweets per hour, time between tweets and friends:followers ratio.

3(a) presents the differences between cues used by bots and humans. The detailed numeric differences are in the Supplementary Material. This difference is examined overall, and by event. There are consistent differences in the use of cues by bots and humans. For example, across all events, bots use significantly more abusive terms and expletives, and tweet more than humans. On the other hand, humans use more first person pronouns, positive sentiment, and media (i.e., images, videos). Humans tend to quote and reply to tweets, while bots tend to retweet.

Most events have consistent cue distribution, but some events look different. In general, humans use more sentiment cues. However, in the two elections (US Elections 2020 and Canadian Elections 2019), bots used more sentiment cues. This reveals a deliberate attempt to use bots during the election seasons to polarize online sentiments. Prior research has shown that bots can be highly negative during the election season [82], and that bots express hugely different sentiment sentiment when mentioning different political candidates [83, 8].

(a) Differences in the use of psycholinguistic cues between bots and humans.

(b) Differences in the use of psycholinguistic cues between bots and humans for the combination of Captain Marvel and Black Panther datasets. This compares the cue distribution with and without retweets.

Figure 3: Comparison of psycholinguistic overall cue usage (average cue usage per user) by bots and humans across datasets. Green cells show that humans use a larger number of the cue. Red cells show that bots use a larger number of the cue. * indicates there is a significant difference in the usage of the cue between bots and humans.

When a bot retweets a human, its linguistic profile, by definition, is identical to the human’s. The question though, is whether the bots that are sending original tweets match the linguistic profile of those retweeting, or is the linguistic profile different? For the Black Panther and Captain Marvel events (3(b)), we compared the psycholinguistic profile for all tweets, and the original tweets only (i.e., no retweets). In these two events, bots retweet significantly more than humans. However, the bot-human difference between linguistic cue use of the original tweets vs all tweets are rather similar. Only the average tweet reading difficulty and the number of friends are different: in original tweets, humans have higher values; in all tweets, bots have higher values. Therefore, bots have their unique signature when generating new content, but are guaranteed to match human’s content when retweeting the human’s.

Bots construct tweets with cues that can be easily and heavily automated, while humans construct more personal tweets that require higher cognitive processing to create. Such differences shows how bot accounts still use rather rudimentary techniques: hashtag latching using multiple hashtags [40, 27], connecting several users together with increased number of mentions [54] and flooding the zone with lots of tweets tweets of their desired narratives [24, 84]. More sophisticated communication techniques like having an increased number of media, and more advanced interaction techniques that involve dialogue understanding like increasing the number of replies and quotes, are still left to the humans. In short, bots have not entirely mimicked humans, yet.

How do bots present themselves differently from humans?

Social identity theory depicts how social media users portray their image online, and the community that they want to be associated with [85, 86]. We analyze the difference in the self-presentation of the identities between bots and humans, and the difference between the linguistic cues used by the identities. Across the events, there are consistently a smaller proportion of bots that present with an identity. Overall, 21.4% of bots present an identity, while 27.0% of humans present an identity (see Appendix Table 10). Bots are more likely to obfuscate their identities [87], allowing them to take on different personas to suit their operation requirements [88]. 4(a) presents the top 25 identities by frequency between bots and humans. There is a more exponential drop of the frequency of the use of identities in bot users than in human users, suggesting that bots concentrate their self-presentation on certain identities, mostly the common ones: man, son, fan, lover; while humans have a more varied identity presentation.

We then ask a follow-up question: “How do the same bot/human identities talk about the same topics?" We compare the use of topic frames per identity for the most frequent identity affiliations in 4(b). This plots the percentage difference of the use of framing cues (Family, Gender, Political, Race/Nationality, Religion) between bots and humans. This metric compares the use of cues with the human usage as a baseline. Overall, bots converse more aggressively in all topic frames. In particular, bots converse most around societal fault lines: gender, political, race/nationality. These conversations lie on societal fault lines, which could sow discord and chaos [89], therefore such bots are of interest to monitor and moderate. In fact, bots use more gender-based cues. Other research groups have also identified that a disproportionate number of bots that spread disinformation are females [90, 91], and are thus more likely to use gender frames in their posts. Bots tend to converse largely about political topics, regardless of the identity they affiliate with, indicating that a good proportion of bots are deployed for political purposes, either by political parties or by political pundits [16, 68, 26]. Finally, the difference between the usage of topic frames between bots and humans could be due to their vocabulary used. The words used by humans are more varied and mostly not standard dictionary words, while bots are still being programmed with a limited set of vocabulary, as evidenced by the proportion of words identified by the dictionaries in the NetMapper program used. In a similar aspect, chat bot interactions have a more limited vocabulary than human interactions [92].

4(c) presents the average use of topic frames by identity categories. Humans affiliate themselves equally with all identity categories, while bots generally affiliate themselves with racial and political identities. Both bots and humans converse a lot on gender and political issues.

Bots converse mostly about topics that closely match their identity. For example, a bot that presents itself as “man" and “son" mostly converse about family then gender; while bots that take on the identities “conservative" and “american" converse significantly more about politics. This observation can be read from the heatmap: for the bots that associate with the religion identity, the average use of religious words is 0.04, while that for humans is 0.00. If the users associate with the family identity, the average proportion of the use of family words within the content is 0.19 for bots and 0.04 for humans. Such is the curated presentation and programming of bot users, which allows for an aspect of predictability - if a bot user affiliates with a certain identity, it is likely to talk about topics related to its identity. This shows that bots are likely designed to look like humans. They are strategically designed to be in character by having the right affiliation to fit in and converse with a specific group.

Our observations in the affiliation of identities by bots in their user description and the use of identity-related topic frames means that bots are being used strategically. They are not just used to support or dismiss groups in general, but are specifically being aimed at a gender (i.e., women or men), or at a political actor (i.e., president, governor, politician). Bots are overused in the political, religious, and racial realm, suggesting that they are targeting topics of societal tensions.

(a) Comparison of the use of the identity affiliations by bots and humans. 21% of the users affiliate with an identity in their user description.

(b) Percentage Difference of the use of each topic frame in messages by the top frequent identity affiliations.

(c) Average use of topic frames by identity category referred to. Bots are more likely to refer to gender and political identities, and are more likely to utilize racially typed language.

Figure 4: Comparison of identity-related behaviors in bots and humans

How do bots communicate differently from humans?

Social interactions between users are an indication of the information dissemination patterns and the communication strategies of the users. We calculate the network metrics (total degree, in degree, out degree, density) of the all-communication ego-networks of the users. In the network graphs, the users are nodes, and the links between users represent all communications between the two users (i.e., replies, quotes, mentions, retweets). Table 5 compares the two metrics for bots and humans. Bot ego networks have higher density than ego networks (8.33% more dense), which reflects that the bots have tighter communication structures and form more direct interactions than humans. On average, a bot has 9.66% bot alters and 90.34% human alters, whereas on average a human has 7.31% bot alters and 92.69% human alters. Although bots interact with a higher proportion of bot alters than humans do (32% more bot alters), our findings show that both bots and humans interact more with humans rather than bots in their ego network. By the principle of homophily, it is natural for humans to interact with other humans [93]. However, bots violate the principle of homophily, and instead of interacting with more bots, they interact with more humans. Therefore, bots are actively forming communication interactions with humans, perhaps attempting to influence humans [94].

Table 5: Comparison of network metrics. For the in-degree, out-degree, total degree and density, we present the ratio of mean(metric) for agent type : max(metric) across all agents in the event

Figure 5 shows the interaction of bots and humans in a network diagram. These users are illustrative of the most frequent communicators in the Asian Elections dataset. In this diagram, users are represented as nodes, and links between users represent a communication (e.g. a retweet, reply, mention). The network diagrams presented are one- and two-degree ego-networks, generated by the ORA software [81]. This means that the networks present users that are in direct communication with the user (1-degree), and are in direct communication with the 1st-degree users (2-degree).

Figure 5: Ego network structures of Bots and Humans who are the most frequent communicators in the Asian Elections dataset. Nodes represent social media users. Links between users represent a communication relationship between the two users (i.e., retweet, mention). Bot users are colored in red, human users in grey. The width of the links represent the extent of interactions between the two users. In these most frequent communicators, bots have a star network structure, and humans a tree structure. Bot networks have more bot alters, while human networks have more human alters.

A common way for bots to be used in political discourse (e.g. elections) is to amplify other users. As an amplifier, bots are the pendants of the user they are amplifying. Therefore, bots appear in star networks in many of the peripheral nodes. A star structure is a network that have a strongly connected core and peripheral networks. This structure is most prominent in bots in political discourse, where core bots create information, and peripheral bots amplify the information through the retweeting mechanic [24]. Humans, on the other hand, are more likely to be part of a tree structure, where one can make out the tiered first- and second-hop interactions. In the same discourse, humans are more likely to be performing many actions, sometimes retweeting other users, sometimes tagging other users and so forth.

This difference in interactions between bot and human users reveals the communication patterns of both user classes. The star structure of bots suggests that they have a hierarchy of interconnected bot users in an operation network to disseminate information, which is easily achieved with the help of automation. On the other hand, humans communicate predominantly within their immediate network before extending their communication outwards. The bot ego networks are more dense, signifying that they were constructed to interact more than do humans, and are sometimes constructed as networks of bots (the botnet) [95, 94].

Discussion

Through our large scale empirical study, we show that bots and humans have interesting and consistent differences between them. These differences span from their volume, to the linguistic features of their text, to the identities they affiliate with, to their social network structure. These features can be used to characterize a social media bot, and how it differs from humans. We study a huge amount of data dated from 2018 to 2021. These data show consistent differences over the years, which means that while bot technology do evolve, it does not evolve drastically. Moreover, the consistent differences show that there are scenarios where bots can be better than humans, and scenarios where humans can be better than bots. These differences provide insights to how both can be utilized to afford conversations on social media: bots can be used for methodological postings with deliberate selection of hashtags, tweets per hour, and a structured star communication network. Humans can be used for more complex cognitive tasks such as adding media to a post or replying to a post, and for conversing on a larger range of topics [3].

Strengths of Social Media Bots

An Artificial Intelligent (AI) system is a machine, or computer system, that can perceive its environment and use intelligence to perform actions to achieve defined goals [96]. The social media bot perceives the digital environment to decide their targets (i.e., users to retweet, users to mention), and intelligently carry out content and interaction mechanics to achieve their goals (i.e., spreading information [56, 28], sowing discord [97, 98], assisting the community [45, 46, 47]). The software-programmed social media bot is an AI algorithm, and thus has potential to be harnessed for social good.

Table 6 lists some recommendations of our results on how bots can be leveraged on for social good, and how they can be regulated. First, given that bots use more retweets and mentions than humans, and have high tweets per hour, bots can be used for menial tasks like announcements and distribution of information. Second, since bots have a star interaction network, they can be used for big announcements like disaster and crisis management without message distortion. A star network sends messages directly through interactions, hence the original message is preserved. However, the human’s hierarchal interaction network will distort the message as it passes through the tiers. Third, bots typically post content that matches their identity, they can be used to provide educational material about topics that people associate with certain profession. For example, a weather news bot can provide weather information. Lastly, since bots use more abusive and expletive terms than humans, instead of regulating toxic language itself, regulation can be focused on disallowing bots to use such toxic language, which would therefore reduce the amount of hyperbole and offense online.

Table 6: Recommendations of our observations on leveraging and regulating bots for social good

Challenges and Opportunities of studying Social Media Bots

Next, we elaborate on three challenges in the study of social media bot, and discuss some opportunities for future research.

Detect

The first step to bot detection is to systematically detect these bots. However, these automated agents are constantly evolving and adapting their behavior in response to the changing setup of social media platforms and user patterns. The stricter data collection rules of social media platforms [99, 100] and the increasing usage of AI in these bot agents [73] creates further variability in these digital spaces bots reside in. This therefore muddles any developed algorithms based on previous datasets.

Already, linguistic differences between bot and human tweets have narrowed between 2017 and 2020, making bot accounts more difficult to systematically differentiate [19]. More recently, AI-powered botnets have emerged, using ChatGPT models to generate human-like content [73], closing the gap between bot and human.

Bot evolution and bot detection are thus a “never-ending clash" [101], and sometimes bot accounts evolve faster than current known bot detection algorithms [68], presenting several opportunities in continual improvement of bot detection algorithms, specifically to be adaptable, faster, and more efficient. The increasing trends of using Large Language Models and Large Vision Models to create generated texts and deepfakes lend bots a helping hand in the construction of more believable narratives. These same generative technology are also used to construct offensive bots for humor [102]. However, current trends reflect that the use of such technologies are not very prevalent, for example, [73] only found one set of such botnet in their study, reflecting that bots are still relying on traditional techniques, likely because such heuristic-based techniques are easier and faster to deploy en masse.

Differentiate

After identifying which users are likely to be bots, one must differentiate the goodness of the bot and its function. This evaluation can be inferred from the bot’s content postings and relationship interactions. However, bots do not fall squarely in a spectrum of goodness; the lines of good and bad bots are blurred. In fact, bots can move between neutral in which they post messages that are not harmful, to bad, where they post politically charged and extremist messages [40, 103]. Herein lies an opportunity to construct a rubric to determine the goodness of the bot; this, though, is a complex task, for there are ethical and societal issues to consider. Bots can change their goodness, too. They may be supporting a certain cause initially, then making a swing to a different stance soon enough. This swing of support was witnessed during the coronavirus pandemic era, and especially so when the bots require little conviction to change allegiances [8]. Another challenge involves identifying the type of bot, which can provide insight towards possible impact of the bot. For example, an Amplifier Bot that intensifies political messages could be intended to sow discord [24, 61].

Disrupt

The third challenge is to mindfully disrupt the operations of bot users. That is, moderating the impact of malicious bots, while not unsettling human conversations. While banning bot users can be an easy solution, a blanket ban can result in many false positives, which thus results in humans being identified as bots and being banned. Such situations can result in emotional or psychological harm of the human being banned, or toxic online behavior where users repeatedly report another user that they personally dislike as a bot to silence them [104]. Additionally, social media bots do not necessarily work alone: they coordinate with other bots – sometimes even human agents – to push out their agenda [80], and therefore if one agent warrants a ban, should the entire network be banned? To ban an entire network may entangle several unsuspecting humans who have been influenced by the bots to partake in the conversation. With these considerations in mind, regulation is a scope of problem with which to be studied: which types of bots should we ban? What are the activities of a bot that would warrant a ban?

Methods

Examining Bot Literature

We examined recent bot literature for the definition of “social media bot". For academic definitions, we searched the phrase “social media bot" on Google Scholar. For industry literature, we searched the phrase “social media bot" on Google Search. Then, we manually scanned through the results. We picked out the more relevant and highly cited papers that had a definition of a social media bot. We read through each paper, and manually extracted the definition of a social media bot stated in the paper.

Next, we looked through all the definitions and picked out key phrases. We then harmonized the phrases and definitions to create a general definition of the bot. All authors agreed on the definitions and categorizations.

Data Collection and Labeling

We collected a dataset from Twitter/X involving global events which provides a richness in a general understanding of the bot and human differentiation. The list of data collection parameters are detailed in Appendix Table 8.

We labeled each user in this dataset as bot or human with the BotHunter algorithm. This algorithm uses a tiered random forest classifier with increasing amounts of user data to evaluate the probability of the user being a bot. The algorithm returns a bot probability score that is between 0 and 1, where scores above 0.7 we deem as a bot, and scores below 0.7 we deem as a human. This 0.7 threshold value is determined from a previous longitudinal study that sought to identify a stable bot score threshold that best represents the automation capacity of a user [69]. This bot algorithm and threshold is chosen so that our studies will be consistent with the original studies of the dataset that used the BotHunter algorithm [34, 9, 77, 78, 79, 80]. We calculated the proportion of bot users against the total number of users within each event. Our results are presented in a bar graph.

Comparison by Psycholinguistic Cues

We parse the collected dataset of tweets through the NetMapper software [81] to extract out psycholinguistic cues of the texts. NetMapper extracts the number of each of the cues per tweet. The software returns three types of cues: semantic cues, emotion cues and metadata cues. The linguistic cues are returned by matching words against a dictionary for each category. The dictionary has words in 40 languages. Then, for each user, we average the use of each cue per category, as the trend for the user. We then perform a student t-test comparison between the cues of each user type with Bonferroni correction, and identify whether the cues are significantly different between the bot and human at the level. We then remove the retweets from the Captain Marvel and Black Panther datasets and compare the cue distribution of original tweets with all tweets. This analysis compares the differences in the distribution of cues of tweets originating from the user type and their retweets.

Comparison by Self-Presentation of Identity

To classify identities, we compare the user description and bio information against a survey of occupations of United States users performed in 2015 [105]. If the occupation is present in the user information, the user is tagged with the identity. A user can have more than one identity. We compare the top identities used by bots and human users across all events. These identities are also divided up into seven categories: religion, race/nationality, political, job, gender, family and others. We then classify each user into these categories of identities. Again, each user can fall into multiple categories.

Next, we examined how different identities frame their posts differently. We extract framing cues from the overall set of of psycholinguistic cues generated. The topic frames we examined are: family, gender, political, race/nationality and religion. For each most frequent identity affiliated with by bots and humans, we compare the difference in the average use of each topic frame through a percentage difference calculation. The percentage difference in the use of framing cues is calculated as: , where is the average use of the framing cue by humans, and is the average use of framing cue by bots. This comparison tells us how much more bots use a framing cue as compared to humans. If the percentage is negative, bots use the framing cue more than humans. If the percentage is positive, bots use the cue less than humans.

The set of topic frames also corresponds with the identity categories. Therefore, we also compared the identity categories against the average use of each topic frame. This comparison is performed across bots and humans. We plot heatmaps to show the relationship between the average use of each topic frame topic frame against the identity categories.

Comparison by Social Interactions

We construct the all-communication ego-networks of the users in our dataset. We analyzed all the users for Asian Elections, Black Panther, Canadian Elections 2019, Captain Marvel and ReOpen America events. Due to the size of the data, we analyzed a 2% sample of users of the Coronavirus2020-2021 users (mil), and a 50% sample of users from the US Elections 2020 (k). The ego-networks are network graphs of the bot and human users in focus. In the networks, each user is represented as a node, and a communication interaction between users are represented as links. The ego-networks are constructed using all-communication interactions, that is any communication between users (i.e., retweet, @mentions, reply, quote) is reflected as a link. We analyzed the network properties of the ego-networks constructed per event. These properties are: total-degree, in degree, out degree, density. We also analyzed the number of bot and human alters there are in the ego networks. No pre-processing were performed on the networks prior to the calculations. We used the ORA software to load in the networks and perform the calculations [81]. We finally visualize the network graphs of one- and two- degree ego networks of a sample of bots and humans Figure 5. These are the 20 most frequent communicators in the Asian Elections sub-dataset A 1-degree network shows alters (connected users) that are in direct communication with the user, and a 2-degree network shows alters in direct communication with the 1st-degree alters.

Conclusion

Social media bots are deeply interweaved into our digital ecosystem. More than half of the Internet traffic in 2023 were generated by these AI agents [106]. Bots are able to generate this volume of traffic because of their use of automation, which enables them to create more content and form more relationships. This article surmised a definition of a social media bot based on the three elements that a social media platform contains: user, content, interactions. Our definition breaks down the automation on social media platforms into its core mechanics, and therefore provide the foundation for further research, analysis and policies regulating the digital space. We performed a large scale data analysis of bot and human characteristics across events around the globe, presenting the uniqueness of the bot species from a macro perspective: how bots and humans differ in terms of the use of linguistic cues, social identity affiliations and social interactions. On a global scale, bots and humans do have consistent differences, which can be used to differentiate the two species of users. Table 7 summarizes the differences between bots and humans as a conclusive remark. Finally, we provide recommendations for the use and regulation of bots. These recommendations are informed by our results. We also lay out the challenges and opportunities for the future of bot detection in a “Detect, Differentiate, Disrupt" frame. We invite academics, non-profits and policymakers to take part in this active research area.

Table 7: Summary of Differences between Bots and Humans.

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Acknowledgements

The first author thanks Christine Lepird for her ideas and comments towards this work. This material is based upon work supported by the Scalable Technologies for Social Cybersecurity, U.S. Army (W911NF20D0002), the Minerva-Multi-Level Models of Covert Online Information Campaigns, Office of Naval Research (N000142112765), the Threat Assessment Techniques for Digital Data, Office of Naval Research (N000142412414), and the MURI: Persuasion, Identity & Morality in Social-Cyber Environments (N000142112749), Office of Naval Research. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing official policies, either expressed or implied by the Office of Naval Research, U.S. Army or the U.S. government.

Author contributions statement

L.H.X.N. conceived the experiments, collected the data and performed the analysis. K.M.C. reviewed the analysis. All authors reviewed the manuscript.

Competing Interests

The authors declare that there is no competing interests.

Supplementary Information

Data Collection Parameters

Table 8 presents the parameters of data collection for our study.

Table 8: Dataset collection parameters. Data was collected using the Twitter Developer V1 API

Comparison of Linguistic Cues

Table 9 presents the detailed comparison of linguistic cues between bots and humans across all events.

Table 9: Comparison of Mean of Psycholinguistic Cues per user for Bots and Humans across all events. This value is the number of words that match each cue per tweet, divided across the number of tweets the user has. *** indicates a significant difference between the bots mean and humans mean via a student t-test at the level.

Identity Use Per Event

Table 10 presents the percentage of users with identities that match the US census of occupations [105]. Figure 6 shows the identities used per event, separated by the top identities used by both bots and humans.

Table 10: Percentage of Users with identity affiliations matching census

Figure 6: Comparison of frequency of use of the top Identity affiliations by bots and humans per event.

Identity vs Framing Charts Per Event

Figure 7: Percentage difference of narrative frames used for identities that are common between bots and humans in Asian Elections dataset

Figure 8: Percentage difference of narrative frames used for identities that are common between bots and humans in Black Panther dataset

Figure 9: Percentage difference of narrative frames used for identities that are common between bots and humans in Canadian dataset

Figure 10: Percentage difference of narrative frames used for identities that are common between bots and humans in Captain Marvel dataset

Figure 11: Percentage difference of narrative frames used for identities that are common between bots and humans in Coronavirus dataset

Figure 12: Percentage difference of narrative frames used for identities that are common between bots and humans in US Elections 2020 dataset

Figure 13: Percentage difference of narrative frames used for identities that are common between bots and humans in ReOpen America dataset

Centrality Values of User Types Per Event

Table 11, Table 12, Table 13 and Table 14. shows the centrality values of the bot and human ego networks per event. The ego networks are All Communication networks. That means that users are nodes, and links between users represent any communication interaction between the two users (i.e., replies, quotes, mentions, retweets). For the larger datasets, the values were calculated using a sample of users. The four centrality values we calculated are: total degree, in degree, out degree and density. These values provide insights towards the extent of interactions in a network. Total degree centrality indicates the number of edges in a network. A larger total degree centrality value indicates that there are more edges, and therefore more interactions, within a network. In degree centrality and out degree centrality is calculated in a directed network fashion. Social media interactions are not always symmetrical. In our ego-networks, if the alter interacts with the user (i.e., the alter retweets, @mentions, quotes, replies to the user’s post), the interaction is counted as an incoming links. In-degree centrality measures the number of incoming links. Higher in-degree centrality means more nodes in the network interact with them. If the user interacts with an alter (i.e., the user retweets, @mentions, quotes, replies to the alter’s post), the interaction is counted as an outgoing link. Out-degree centrality measures the number of outgoing links. Higher out-degree centrality values suggests the user tend to initiate more connections with others. The density of the ego network is the ratio of the total degree and the maximum number of possible edges. A denser network suggests tighter interactions between users in the network.

For each degree centrality value, we calculate the degree ratio. This ratio is derived from the mean degree centrality value of the agent type (bot/ human) against the maximum degree centrality value of all agents in the event. This calculation normalizes the centrality values and allows for us to compare average centrality values across different events.

We also calculate the percentage of bot alters for each ego network. The results are presented in Table 15, which shows that bots tend to have more communication interactions with bot alters while humans have more communication interactions with human alters.

Table 11: Centrality Values of Ego Networks for Total Degree

Table 12: Centrality Values of Ego Networks for In Degree

Table 13: Centrality Values of Ego Networks for Out Degree

Table 14: Centrality Values of Ego Networks for Density

Table 15: Percentage of Bot Alters in first-degree ego networks

Articles: Twitter automation tools, ranking, published in 2025