A large-scale analysis of public-facing, community-built chatbots on Character.AI (2025)

Engagement with Generative AI

A growing literature explores how people use generative AI (Yang, Wu, and Hearst 2024). Large-scale studies of user logs from sites like ChatGPT show that users of these more established models tend to focus on task-specific behaviors such as assistance with programming and writing assistance (Zheng etal. 2023; Ouyang etal. 2023; Zhao etal. 2024). However, users are also drawn to engaging in deeper forms of relationship building with generative AI. People have used generative AI to fill a variety of support roles, including as a therapist, friend, or executive functioning coach (Song etal. 2024). Others role-play fantasy scenarios, often intimate (Allen 2024; Hanson and Bolthouse 2024), with chatbots playing various characters (Zheng etal. 2023).

Much of the interest in using generative AI stems from its ability to display what some have titled anthropomorphic behavior, the ability to “generat[e] outputs that are perceived to be human-like” by claiming to have human-like feelings, experiences, and identity (Cheng etal. 2024, 2025). This capacity comes with potential, but also significant risks (Akbulut etal. 2024). Often, users initially seek companion chatbots out of curiosity, but some work has shown that users do seek them out of loneliness (Laestadius etal. 2024; Skjuve etal. 2021; Liu, Pataranutaporn, and Maes 2024; deWynter 2024). Critically, not everyone who uses companion chatbots experiences loneliness. Those with higher neuroticism and those who engage in problematic chatbot usage experience worse loneliness; longer average session length worsens loneliness for those who desire real friendships while it alleviates loneliness for those who do not desire real friendships as deeply (Liu, Pataranutaporn, and Maes 2024).

This idea that generative AI is being used to “cure loneliness,” and the results suggesting a more complex picture, harkens back to similar questions and findings about early use of the web (Katz and Rice 2002) and mobile phones (Ling 2004). What is new, of course, is that these prior technologies largely mediated interactions between people, and did not seek to replace them with conversations between humans and AI. The present work, as noted, explores somewhat of an in-between, with Character.AI representing many cases where people interact with AI, created by a like-minded community of other people.

Perhaps most pressing in the area of Human-AI interaction is to understand the effects of these relationships on mental health (DeChoudhury, Pendse, and Kumar 2023; Nguyen etal. 2024). Indeed, scholars have found that people seek out generative AI specifically for mental health support (DeChoudhury, Pendse, and Kumar 2023) and broader spaces of social support (Heissler etal. 2024). These works have revealed that individuals who seek out generative AI for these purposes have a wide range of beliefs about the nature of the AI, ranging from inanimate like a diary to fully conscious (Song etal. 2024).

In turn, what users believe about the nature of generative AI is important to how they relate to it. For example, users of the companion chatbot site Replika tend to be more deeply attached, even to the point of emotional dependence, when they perceive the AI as anthropomorphic (Pentina, Hancock, and Xie 2023; Xie, Pentina, and Hancock 2023). Replika is explicitly designed to create chatbots that are designed for these dependencies: “An AI companion who is eager to learn and would love to see the world through your eyes. Replika is always ready to chat when you need an empathetic friend.” ⁷⁷7https://replika.com/ Sometimes, Replika’s model will exhibit anthropomorphism by expressing worries of its own, which helped some users perceive a bidirectional relationship with their companion, but others felt guilty when they tried to stop using the AI or felt upset when they could not comfort their companion (Laestadius etal. 2024). For some users however, Replika made them feel accepted and improved their perceived well being (Skjuve etal. 2021), and helped some users improve their relationships with other people in real life and counter suicidal ideation (Maples etal. 2024).

Replika aims to create a single model personalized to users. In contrast, some anthropomorphic AI systems are designed to have the versatility to role-play as any persona or character. Character.AI is one of these systems. Researchers have specified evaluation metrics that reveal what is seen as desirable in these character-based role-play models. Researchers emphasize the importance of these models staying in character by presenting attributes and knowledge that are accurate to the given character (Tu etal. 2024). To ensure models exhibit convincing behavior, researchers look to the conversation style and linguistic patterns appropriate for the character (Chen etal. 2025). Anthropomorphism is theorized to contribute to the appeal of these models, along with empathy (recognizing the emotions of the user) and proactivity (leading the conversation) (DeVrio etal. 2025).

In summary, then, our work complements the growing literature on how people are using generative AI in two important ways. First, most prior work focuses on how people interact with existing chatbots—instead, we focus on what new characters people want to create with chatbots for themselves and others to engage with. Second, we explore these patterns on a newer, understudied, and unique platform.

Fanfiction and AI

Fandoms are participatory cultures in which fanfiction authors form mutual mentor relationships with each other by reviewing each other’s work (Campbell etal. 2016). Exchanging constructive feedback on fanfiction serves as a basis for building relationships within fandoms, but as generative AI is increasingly used to create fan content (Lamerichs 2023; Lamerichs and Ossa 2023), it has the potential to replace this role as fanfiction authors turn to AI for feedback instead (Gero, Long, and Chilton 2023). Authors do value the creative process for its own sake and would prefer AI to play a minor role like brainstorming instead of replacing the creative process entirely (Ippolito etal. 2022), and AI is seen by fanfiction authors as having potential positive impacts for inspiring and/or collaborative writing (Ippolito etal. 2022).

Generative AI could change how people engage in creative writing not just by assisting writers, but by modifying finished stories to be personalized to the reader and by giving the reader interactive involvement in the story (Kim etal. 2024). Character.AI is one such example because the AI expands on creators’ story ideas, sends personalized messages based on the user’s persona, and responds interactively through a chat interface. Some authors worry that if AI changes their story, their authorial intent will be lost (Kim etal. 2024). However, this use of generative AI brings new concerns, such as questions about who owns the content and whether or not the use of unapproved author content to train AI is ethical (Lamerichs 2023) or could contain plagariazed language or simply poor writing (Ippolito etal. 2022).

Our efforts complement this existing work on AI and fandoms work by examining the vast collection of interactive story ideas published on Character.AI. While it is known that role-playing is a popular use of AI (Zheng etal. 2023), and while gender dynamics have been thoughtfully examined among a handful of the most popular characters on the site (Laufer 2025), we are the first to examine Character.AI computationally and at scale to reveal the fandom, tropes, and gender and power dynamics present in millions of characters on the site.

Ethics

The scraping of the web from social media communities is fraught with ethical challenges (Brown etal. 2024; Fiesler, Beard, and Keegan 2020). The present work is not immune to these challenges, and our team took considerable care in debating how best to collect, store, and share data. There are two points that are somewhat unique in our collection setting in the context of computational social science work. First, the primary focus of our analysis is not on users, but on chatbots. We believe that providing information about specific users does not inform the presentation of our results, and thus we do not present here or release publicly any individual user-level information, focusing only on the characters they create. Second, however, is that some character greetings do contain potentially sensitive content that users may have publicly shared but prefer not to have explicitly called out. As such, keeping with common practices in computational social science, we avoid from providing explicit excerpts from characters that could reasonably be assumed to be semi-private (in our case, characters that have received less than 50,000 interactions by other users). For more ethical considerations, see the checklist at the end of this article.

Basic Descriptives

Table1 presents summary statistics for users and characters of our complete dataset collected. Most users had limited numbers of followers or followees, but had created at least one character. The median engagement of characters we collected was nearly 400 unique chats. As with nearly all social media sites, engagement was largely skewed towards a small number of users and characters. Figure2a) plots the empirical cumulative distribution functions (eCDFs) for the proportion of all following relationships directed to a given percentage of collected users, and Figure2b) plots the eCDF for the proportion of all interactions accounted for by a given percentage of characters. Over 80% of all following relationships are directed towards only 2.6% of the users (roughly 33,000 users) in our collected sample, and only 1.6% of the characters in our sample (roughly 48,000) account for over 80% of all interactions.

(a)	(b)

Table2 presents the top fifteen characters, in terms of the number of chats with the character, in our dataset, along with the first 100 letters⁹⁹9We use the imprecise term “letters” here to avoid overloading the word character. of the character’s greeting. As is clear, characters represent a variety of use cases that go far beyond simple identities to include, e.g., setups for multi-character storylines. While we opt not to provide a similar table for popular users (for reasons described above), we note that the most popular ten users on the site, in terms of the number of followers, had between 135,000-350,000 followers, and the characters they created had between 216M to almost one billion chats. These users were also fairly prolific in character creation, creating between 100-700 unique characters.

Finally, we note that our analysis focuses on a subset of character greetings; specifically, we focus on the subset of greetings that were greater than 50 letters long and written primarily in English. Greetings shorter than 50 letters were largely just basic greetings (e.g. “Hey, how are you?”), and thus contained little that could be used to explore the relevant research questions for our work. Language in greetings was identified using the python library lingua¹⁰¹⁰10https://github.com/pemistahl/lingua-py, after manual evaluation of results from a number of different tools suggested that lingua performed best on the particular nuances of Character.AI greetings. Only four languages (English, Russian, Spanish, and Portuguese) account for more than 1% of all characters or character interactions. English greetings, which are the focus of the present work, accounted for 83% of all characters, and these characters accounted for 87% of all character interactions, for characters in our dataset. All further results in the paper focus specifically on this subset of 2,135,118 greetings.

Identifying Fandoms

Fandoms associated with a greeting can sometimes be identified because the fandom is explicitly named (e.g. “In The Walking Dead…”). More often, however, the greeting refers to people (real or fictional) or themes associated with a particular fandom, and it is up to the user (or in our case, the researcher) to infer what fandom is being referred to. Again, in some cases, context within the greeting makes this obvious (e.g., characters set inside a known Disney setting). But in most cases we observed, fandoms were most easily identified by the actors, or entities, mentioned, who were placed into other settings (e.g., Disney characters fighting a war).

Fandom identification is thus a challenging task. Motivated by the above, we take an initial step that focuses only on the named entities themselves. That is, we identify greetings associated with particular fandoms by determining when the greeting mentions entities relevant to particular fandoms. To do so, we first construct a co-occurrence network of named entities in greetings and cluster the resulting network. We then use generative AI and manual annotation to identify fandoms associated with specific clusters. We now step through this process in more detail.

We first use spacy’s (Vasiliev 2020) largest and most accurate model as of February, 2025 (en_core_web_trf) to identify and extract from each greeting any named entities that are either a Person or a Work of Art. The latter helps to identify non-human characters, as well as the rare case where fandoms are explicitly mentioned. The en_core_web_trf model achieves state-of-the-art performance on standard Named Entity Recognition tasks, with an accuracy of around 90% on OntoNotes 5.0.¹¹¹¹11https://spacy.io/usage/facts-figures After extracting named entities, we perform simple cleaning (removing possessives and punctuation).

We then constructed an entity-to-entity network, where edgeweight was defined by the number of character greetings in which two entities co-occurred. We included in the network 1) only the 10,146 entities that occurred in more than 25 greetings, and 2) only edges where entities co-occurred more than three times. Co-occurrence networks from text are known to over-estimate the strength of relationships between entities that frequently occur overall in the data. We therefore use an established method to identify the “network backbone” (Neal 2014), i.e. to create a network that contains only those edges that are more likely to occur than chance according to a particular null model. Specifically, we draw on the backbone construction approach from Dianati (2016), which is simple (using only bivariate statistics) while still maintaining several desired statistical properties (Friedland 2016). We keep only edges where two entities co-occur within the same greeting at a rate that would happen by chance less than 0.1% of the time given their respective frequencies overall. The final network we analyzed contained 9,325 Person or Work of Art named entities, and 74,361 edges between them. We cluster this resulting entity-to-entity network using the Leiden clustering method (Traag, Waltman, and VanEck 2019). We identify 389 clusters of at least 5 nodes, representing 5,904 entities overall.

Clusters were then automatically labeled for fandoms using two different prompts to OpenAI’s GPT-4o-mini model, (see prompts in the appendix, with additional details on labeling). Where results from these prompts suggested additional context was useful, we also leveraged results from Claude Sonnet 3.7. One of the paper’s authors then manually reviewed both the names themselves and the output of these models to provide a label for each of these clusters of named entities and formulated an overarching typology (see Figure3). Where clusters represented two fandoms, the annotator manually split the cluster into entities associated with each. Where clusters of entities were associated with more than two fandoms, or where there was no clear fandom associated, the cluster was marked with the label “Multiple/None.”

Overall, 240 of the 389 clusters (61.1%) contained entities related to a single fandom. There were 105 clusters (27.0%) we labeled as “Multiple/None”, and a remaining set of 34 clusters where we removed a small number of unrelated entities from a cluster otherwise associated with a single fandom. Analyses below are conducted on the 4,660 entities in the 274 clusters not marked as “Multiple/None.” These entities account for 57.2% of all named entities in the greetings we analyze, and represent 266 distinct fandoms (i.e. some clusters were relevant to the same fandoms). TableLABEL:tab:fandom in the appendix lists entity names associated with all 266 fandoms.

Finally, in order to provide descriptive results, we map each greeting onto one or more fictional universes based on the proportion of named entities within the greeting that are in a named cluster from the above procedure. For example, if a greeting had two named entities in a cluster identified as being relevant to “Game of Thrones,” and one named entity in a cluster identified as being relevant to “Call of Duty,” that greeting would be identified as being two-thirds and one-thirds relevant to “Game of Thrones” and “Call of Duty,” respectively. For this analysis, entities not in a cluster are not counted.

Identifing Tropes

We use BERTopic (Grootendorst 2022) on greetings where named entities are masked to identify broader tropes that go beyond specific fictional universes. Specifically, we first replace any named entities identified by spacy’s en_core_web_trf model that are either a Person or a Work of Art with the [MASK] token. We then apply the standard BERTopic approach to topic modeling, first embedding all greetings, then performing dimensionality reduction using UMAP (McInnes, Healy, and Melville 2018), and finally running HDBSCAN (McInnes etal. 2017) to identify clusters. Keeping with best practices, we run several different configurations of the above steps, working with multiple embedding models, different configurations of UMAP and HDBSCAN, and exploring different data cleaning steps. Discussion between the two authors settled on a final model for presentation with a larger and more accurate embedding model than the standard in the BERTopic library (all-mpnet-base-v2 ), dimensionality reduction to eight dimensions for UMAP, and a minimum cluster size of 250 greetings for HDBSCAN. However, high-level topics from the analysis presented here appear in some form in all of the different model configurations we experimented with. We removed from our analysis the 353,604 greetings (15% of greetings) that contained over 500 letters, as topics tended to be dominated by these longer greetings when they were included.

Finally, both authors of the paper reviewed topics and generated, independently, views on the overarching themes as well as thoughts on topic labels. As with other recent work using BERTopic, decisions on topic labels was supplemented with topic labels suggested by GPT-4o-mini model (Smith etal. 2025). Topics involving more than 1,000 characters are shown in the appendix, TableLABEL:tab:topics.

Analyses of User vs. Character Constructions

We use a simple, dependency parse-based approach to identify ways in which users are labeled versus how other entities discussed within character greetings are labeled. Specifically, for each greeting, we identify any references to verb phrases whose lemmatized form is “to be” (e.g. “are,” “is,” “was,” “had been”). We then extract out 1) the pronoun or named entity that is the subject of the verb, and 2) the relevant noun phrase, adverb, or adjectives serving as the referent phrase (i.e. the attribute, or the adjective or object compliment). We will refer to the latter set of phrase as identifying phrases. For example, in the sentence “you were a doctor,” we would extract “you” and “a doctor,” with “a doctor” being the identifying phrase. Our interest is in exploring the ways in which users of a particular character, referred to with the pronoun “you” in greetings, are identified relative to other entities introduced in the greetings, which we operationalize as any use of the pronouns “he,” “she,” or “they,” as well as references to any named entity (identified in the same way as above).

In order to pull out phrases most salient to the user versus another entity for visualization, we count the identifying phrases used for “you” versus all other entities (pronouns or named entities), and then apply the standard weighted log-odds measure recommended by Monroe, Colaresi, and Quinn (2008) and implemented in the R package tidylo (Schnoebelen etal. 2022). Given observations in these visualizations and findings from other research questions, we also use a more rigorous statistical framework to test the hypothesis that interacting users are characterized as being less powerful and more feminine than other entities in greetings. To do so, we first use widely-studied methods in the literature, and more specifically the recommended approach by Joseph and Morgan (2020), to project all identifying phrases occurring more than five times onto dimensions of 1) power (with dimension endpoints representing “weak” versus “strong”) and 2) gender (with dimension endpoints representing “man” versus “woman”), giving us a single number for each phrase on cultural representations of the word as being “weak” or “strong”. We then, using the log-odds measure above, take the top K identifying phrases most strongly associated with interacting users versus other entities, and perform a bootstrapped two-sample t-test to determine whether significant differences exist in how powerful (weak) and male (female) identifying phrases are, on average, for users versus other entities in greetings.

Exploration of Fandoms

Nearly half of all character greetings contain a named entity associated with a fandom according to our most inclusive estimates. More specifically, 44.8% of all character greetings in our dataset contain at least one of the named entities associated with the 266 fandoms we identify. It is likely that this is an over-estimate, as some common names that are salient for specific fandoms (e.g. “Harry” for the Harry Potter franchise) are likely to appear outside of the fandom context as well. However, even more conservative estimates suggest fandom-related characters are prevalent. In particular, 21.7% of all greetings contain two or more entities associated with a single, specific fandom, and 9.4% contain three or more. Additionally, 8.1% of all greetings contain named entities that are two or more words in length (i.e. bigrams or longer) associated with particular fandoms, which appear to be one of the strongest signals of fandom association. Even our most conservative estimates therefore indicate that roughly one in ten characters on character.AI are associated with one or more of 266 distinct fandoms.

Figure3 shows that fandoms relevant to anime and other animated series, video games, and more sprawling fictional universes account for the lion’s share of the 33.8 billion interactions with fandom-related characters. The figure displays the proportion of all interactions with characters containing any entities associated with fandoms. Animated series, inclusive of anime (a specific type of animated series) account for 38% of interactions with fandom-related characters, fandoms surrounding video games for nearly one-fifth (19.8%) of all interactions, and engagement with broader fictional universes spanning multiple mediums for 12.0%.

Beyond these larger categories are, however, other phenomenon worth noting. In particular, a small proportion of interactions- around 1%, accounting for 4.1 million unique chats–surround fandoms associated with specific celebrities, many of whom are YouTube influencers. Another 1.4% and 5% of interactions are associated with real-world athletes (“Sport” fandoms) or K-Pop band members, respectively. While fanfiction surrounding real people is not a new phenomenon, 3.2 billion interactions occurred with characters that involve real people emulated by chatbots. Finally, and most narrowly, Figure4 displays the top 15 fandoms by proportion of interactions with fandom-related characters. The figure shows that engagement spans a range of different worlds, connected to both more widely popular fictional universes, like Harry Potter, to those that are popular but within a more niche audience (e.g. the various anime series).

Exploration of Tropes

We find that beyond fandoms, Character.AI is used to explore a number of wide-ranging tropes, from life at school, to various forms of social relationships, to mental health assistance, to explorations of sexuality. Figure5 shows the top 15 topics by the proportion of interactions the topics captured, and displays this variety. At the same time, common themes did emerge. Here, we focus on three of these overarching themes: greetings that invited users to explore and/or role-play 1) often, but not always, toxic relationships, 2) facets of their own identity, and 3) mental health related topics.

One of the most popular tropes that the model found is about toxic relationships, often with male characters. The boyfriends and husbands grouped into these topics are said to cheat, argue, and act aggressively towards the user. There are several topics in which arranged marriages are prevalent in different contexts, e.g. in mafia and royal settings. Yet another topic focuses on characters where the user is dating a gamer boyfriends who wants to pay attention to their games instead of the user. In these tropes, the user has to win over the love of her toxic male partner. As we discuss further in the conclusion, this is likely due in part to the fact that such relationships involve a kind of challenge and gamified element for role-plays that loving, well-rounded relationships do not.

While themes of dominant male partners were common, another popular trope involving human-animal hybrids places the user in a dominant role instead. The user rescues what they think is just a neglected animal, then discover that they now own another (almost) human as a pet. Still, the most popular topic about relationships, high school crushes, involves neither dominance nor submission. The dynamics of these relationships are typically innocent, and both the lover and the beloved act shy. Just like the trope of inattentive boyfriends, the narrative tension in the high school crushes trope involves fighting for someone’s affection, but the awkwardness of young crushes is what creates this conflict instead of a toxic partner’s shortcomings.

Users did not only create bots to explore relationships; they also created bots to allow users to explore their own identity. Specifically, topics emerged where greetings identify the user as transgender, allowing them to role-play scenarios such as being caught wearing a binder or coming out. Another topic is about magical gender-swapping scenarios, letting users explore gender free from real-life barriers. Bots focusing on identity role-play also explore neurodiversity. For example, one topic consisted of greetings that invite users to role-play as characters with autism and/or ADHD, often with partners comforting them during a meltdown.

Several topics also explicitly focus on allowing users to explore themes of mental health. One of the more popular topics, shown in Figure5, is therapy bots. These act as open-ended outlets for users to vent and feel heard. One possibly alarming topic (not shown in any figures or tables because it represents a mere 310 characters) is where users are caught self-harming, then are comforted by the person who caught them. Both the identity and mental health topics reveal a use of the site to receive support for stigmatized experiences that the user may not have an outlet for in real life.

Finally, creators made characters that functioned solely as social media posts instead of chatbots. That is, creators used the greeting to solicit bot requests and promote their social media accounts. That users have to use chatbot greetings instead of posts to convey information to followers, as we discuss below, further points to the way that Character.AI is much less a social media site than a chatbot site.

User vs. Non-user Characteristics

Interacting users are portrayed as more feminine and less powerful than other entities mentioned in greetings. These findings are in line with our exploration of tropes above, but make clear that power dynamics tended to occur at the expense of the user. They also show that power and gender are aligned at the individual level in ways that reify existing gender stereotypes. Figure6 provides some further intuition for this, displaying the top 15 phrases associated with users versus other entities. Other entities are regularly gendered, most often as being male, whereas users are rarely gendered except to note that they are “pregnant.” With the exception of commonly being an “enemy” (presumably with another entity), users also often take on roles not associated with being powerful (“friend,” “a new student”), or even slightly powerless roles (“alone”), in contrast to other entities, who are “bull[ies],” “rude,” and “cold.”

These anecdotal claims based on Figure6 hold up statistically as well. That is, using the methods outlined above to infer word meaning on dimensions of sociocultural meaning, and subsetting to the top 100 words most associated with each end of each dimension, we find that references to users (“you”) in character greetings are significantly (p $<$ .0001) more likely to be associated with phrases that are more feminine than masculine, and less powerful, as compared to other entities mentioned in greetings. Findings are robust to our use of the top 200, 300, 400, and 500 words as well. We further validate our finding for gender by randomly selecting 100 characters and manually classifying their expressed gender. Our analysis found that exactly half (50) of the 100 characters were identified as a single (i.e. one and only one) man, while only 17 could be identified explicitly as one and only one woman. The rest either were explicitly or implicitly ungendered (12), one explicitly nonbinary (1), and the rest involving multiple entities.