Conspiracies and Algorithms: How Reddit’s Conspiracy Community Perceives Algorithm-Driven Social Automation

Digital Technology and Conspiracy Theories

‘Is my phone listening to me?’ This question, one of the most popular inquiries posed to tech experts, highlights the blurred line between skepticism, critique, and conspiratorial thinking on digital technology. Many users report instances where ads seem suspiciously aligned with recent conversations, leading to speculation that smartphones are actively eavesdropping. As Jones (2022) suggests, such suspicions are not simply failures of technical knowledge, but reflect efforts to make sense of opaque data infrastructures and perceived experiences of digital surveillance through conspiracy theorizing. According to tech experts, the answer is generally no—companies don’t need to listen directly; instead, they use vast amounts of data to predict behavior through targeted ads (Hunter, 2021). Nonetheless, doubts persist, especially in light of documented cases in which authorities have exploited smartphone surveillance to track individuals (Scott-Railton et al., 2022). This example therefore shows how everyday encounters with opaque digital systems can blur the boundary between informed skepticism, experiential interpretation, and conspiratorial speculation.

Conspiracy theories, commonly understood as explanations of significant events or conditions that attribute secret, coordinated, and harmful intentions to powerful actors (Butter, 2020; Douglas et al., 2019; Uscinski, 2018), are widely endorsed today, though recent research suggests more continuity than simple linear growth in conspiracy beliefs over time (Uscinski et al., 2022). Rather than skepticism toward institutions alone, conspiracy theories typically extend suspicion into expansive claims about far-reaching control that exceed what available evidence can support (Butter & Knight, 2020; Knight, 2021). In Fenster’s (2008) account, they are organized through narratives of secrecy and power, which helps distinguish them from legitimate critique of institutions or inequality. Conspiracy theories range from the widely circulated QAnon movement to fears of ‘microchipped vaccines’ and claims that social automation will lead to the ‘Great Reset’ of humanity (Enders et al., 2022; Pivetti et al., 2021). Often fueled by emotions like fear and anger, conspiracy theories are socially and emotionally motivated, driven by mistrust of authority, skepticism toward power, and attempts to rationalize complex events with limited information (van Prooijen & Douglas, 2018). For conspiracists, these theories offer alternative ways of making sense of the world, rooted in intergroup feelings, pre-existing social divisions, and alternative sources of knowledge rather than institutional narratives.

As the opening example of smartphone ‘listening’ suggests, digital technologies, especially when they appear opaque, large-scale, and difficult to verify, can become especially powerful objects of conspiratorial interpretations. They may be understood not merely as technical tools, but as hidden or all-powerful systems through which elites monitor and manipulate social life. For instance, in the wake of the 2020 U.S. presidential election, some Reddit users speculated that algorithms had been used by corrupt officials to cast fake votes, despite a lack of evidence and without specifying which algorithm was allegedly involved (Enders et al., 2021). When people interpret the impacts of digital technology through such suspicions, the line between critical analysis and conspiratorial thinking can blur. A range of factors, including trust in institutions (van Prooijen & Douglas, 2018) and personal experience with technology, such as its use and everyday exposure (Cotter & Reisdorf, 2020), may shape how people interpret these systems, although the extent and direction of these effects remain debated. I examine how particular perceptions of algorithms become associated with conspiratorial interpretations, explore the social concerns underlying these beliefs, and consider how algorithmic experience and perception relate to engagement with conspiratorial narratives.

How to Perceive Algorithms

Understanding algorithms begins with clarifying what is meant by the term itself. Technical experts often approach algorithms as technical artifacts, products of scientific and technological development (Gillespie, 2014). Anthropological, humanistic, and social science perspectives suggest viewing algorithms as integral parts of a broader ecology and socio-technical systems, examining how they integrate into people’s lives, shape interactions and identities, and function within social, cultural, and political contexts as potential agents of power that consolidate authority, shape social classifications, and reinforce embedded values within digital infrastructures (Burrell & Fourcade, 2021; Schinkel, 2023). Together, these perspectives underscore the complexity of algorithms and the need for diverse methodologies to interpret their societal impacts.

Scholars’ perspectives on algorithms influence how they approach studying public perceptions and conceptualizing the ideal model of public understanding. One influential approach views algorithms as interactive artifacts and frames public understanding as a cognitive process centered on formal knowledge and skills, often described as algorithmic literacy (Dogruel et al., 2022; Nguyen & Beijnon, 2024; Rader & Gray, 2015). However, treating the understanding of algorithms as a standalone literacy has limitations. Algorithms are often opaque, constantly evolving, and specialized for particular tasks, making them difficult to grasp even for experts, let alone the general public. Moreover, algorithmic literacy does not fully address the subjective experiences and social and demographic contexts that influence how individuals interact with algorithms (Lomborg & Kapsch, 2020). This approach may also overlook the power dynamics that shape people’s understanding of algorithmic processes (Vogels & Perrin, 2022), limiting its effectiveness as a comprehensive tool for user empowerment.

Given these limitations, recent research on public understanding of algorithms has expanded beyond cognitive dimensions to include subjective aspects, such as lived experiences, affective attachment, active interaction, and algorithmic imaginaries. Scholars like Kennedy and Moss (2015) have therefore called for frameworks that better capture users’ everyday encounters with algorithms. This shift includes studying ‘algorithmic imaginaries’, which reflect how people imagine and ascribe meaning to algorithms, thereby shaping their expectations and behaviors in digital spaces (Bucher, 2017). Similarly, many scholars use the concept of ‘folk theories’ of algorithms to describe the informal understandings that users develop to help them navigate algorithm-driven platforms, often by adjusting their behaviors, such as strategically liking or commenting on social media, to align with perceived algorithmic rules (Eslami et al., 2016; Ytre-Arne & Moe, 2021). Taken together, this scholarship shows that when confronting the opacity of algorithmic systems, users rely on both knowledge and subjective interpretation to make sense of them. However, existing studies of subjective algorithmic understanding still focus primarily on individual-level interpretations of specific algorithmic applications. They pay less attention to how communal and socio-political contexts shape broader understandings of algorithm-driven social automation. While some research has noted that social and political factors, such as perceived racial injustice, can lead to differing understandings of algorithms across groups (Bishop, 2019), this broader communal dimension remains underexplored.

At the same time, whether everyday use experience with algorithm-driven applications clarifies or complicates people’s understanding of algorithms remains an open question. On the one hand, experience with algorithms may enhance algorithmic knowledge (Cotter & Reisdorf, 2020). On the other hand, encounters with algorithmically curated systems may also foster exaggerated or quasi-conspiratorial interpretations of algorithmic power, as suggested by research on ‘algorithmic conspirituality’ (Cotter et al., 2022). Research on media use and conspiracy beliefs suggests that this relationship is not direct, but conditioned by factors such as prior conspiratorial predispositions and cognitive reflection (Enders et al., 2023; Stecula & Pickup, 2021). By extension, use experience may also be related to how people perceive algorithmic systems, though the direction of that relationship remains uncertain.

Building on existing scholarship, this study develops two analytical dimensions, perceptual approaches and ontological perspectives, to examine how people perceive algorithms. These dimensions are informed by, rather than directly adopted from, scholarship on algorithmic literacy, algorithmic imaginaries, folk theories of algorithms, and socio-technical understandings of algorithms. Perceptual approaches refer to the ways people come to know and interpret algorithms through literacy, interaction, experience, and imagination (Eslami et al., 2016; Kennedy & Moss, 2015; Ytre-Arne & Moe, 2021). Ontological perspectives refer to assumptions about what algorithms are and how they exist in the social world, whether as technical artifacts, socio-technical systems, or broader infrastructures of power (Burrell & Fourcade, 2021; Gillespie, 2014; Schinkel, 2023). Although these two dimensions often overlap in practice, they are analytically distinct: perceptual approaches capture modes of knowing algorithms, while ontological perspectives capture assumptions about what kinds of entities or systems algorithms are. This distinction helps explain how similar concerns about algorithms may take different forms, ranging from experiential critiques of specific systems to broader suspicions about algorithmic power, social control, and resistance.

Contextualize Algorithmic Concerns, Critiques, and Resistance

To understand public perceptions and concerns about algorithms, it is essential to consider their social, cultural, and political contexts. Research shows that individual knowledge and experiences with algorithms are significantly shaped by broader societal factors (Lomborg & Kapsch, 2020). Structural inequalities have deprived marginalized groups of agency, fueling distrust in algorithms that appear to reinforce power structures and create new forms of disadvantage. Algorithms in public services often rely on intrusive surveillance and opaque assessments that disproportionately affect vulnerable populations (Burrell & Fourcade, 2021). Among marginalized communities, perceived algorithmic risks are heightened by concerns about fairness and equity, especially where algorithms reinforce existing inequalities (Bishop, 2019; Woodruff et al., 2018). Public critiques frequently address these dynamics, raising concerns about automated power consolidation and the role of algorithmic ‘elites’ in shaping access and opportunity (Kalluri, 2020). These critiques can also extend into resistance, including efforts to evade, contest, or reframe systems experienced as manipulative, exclusionary, or unjust (Velkova & Kaun, 2021; Ytre-Arne & Moe, 2021). Such resistance often emerges from lived experiences of inequality, loss of agency, and distrust toward institutions and platform governance. Ultimately, these concerns, critiques, and resistance practices show that public responses to algorithms often extend beyond technological apprehensions and reflect broader struggles over power, visibility, and accountability.

People often develop perceptions of algorithms, along with related concerns, critiques, and resistance, through a combination of experiential sense-making and socially circulating narratives, which may not align with the technical realities of algorithmic systems (Schellewald, 2022). This gap between perception and the actual workings of algorithms can foster less grounded or biased critiques, as people may fill in informational gaps with speculation or interpret algorithms through social and political frameworks that align with their identities. For instance, as attention to racial justice has increased, some users of color argue that YouTube’s algorithms intentionally conceal content by non-white creators (Bishop, 2019). Although opacity can intensify suspicion, greater transparency does not automatically resolve distrust, which is also shaped by broader political orientations, institutional confidence, and conspiracy mentality (Besta et al., 2025; Chlup, 2021). In the United States, for example, after the public learned more about how algorithmic curation works, conservatives tend to see content moderation algorithms as anti-conservative, while liberals contend that these systems amplify right-wing content and misinformation (Gabbatt, 2021; Thompson, 2020). When people interpret algorithmic impacts through alternative knowledge, they may also become more inclined toward conspiracy theories (Michael, 2013, pp. 27–28). These dynamics may also shape broader orientations toward institutions and authority, influencing how people evaluate public decision-making and, potentially, democratic norms and processes. Thus, understanding how algorithmic perceptions intersect with social context and group identity, and the conditions under which they engage with conspiratorial thinking, is pressing. To address this, the following case study examines a social setting where algorithmic power, alternative knowledge, and mistrust of institutions are especially pronounced.

Case Study: Algorithmic Perceptions Within Conspiracy Communities

Building on the discussion above, I turn to online conspiracy communities as a particularly revealing setting for examining how algorithmic perceptions intersect with social context, group identity, alternative knowledge, and mistrust of institutions. Online environments have become important sites for the production, circulation, and collective negotiation of conspiracy narratives, making them especially useful for observing how suspicions are developed, shared, and contested in interaction (Mahl et al., 2023). Recent research increasingly emphasizes the participatory and community-based character of conspiracy culture, and studies of Reddit show that its conspiracy communities are organized through distinctive discursive patterns and participation pathways (de Wildt & Aupers, 2024; Klein, 2023; Klein et al., 2018, 2019). Taken together, these features make Reddit a particularly useful setting for examining how algorithmic perceptions become entangled with conspiratorial meaning-making.

At the platform level, Reddit’s subreddit structure, relative anonymity, and user-governed organization allow a wide range of communities, including conspiracy-oriented ones, to emerge and persist. These affordances enable diverse subreddits to coexist across the platform, thereby allowing conspiracy communities to gather, develop, and remain active over time. At the same time, subreddit-level autonomy, shared identity, community culture, and moderation practices help sustain participation and reinforce internal cohesion, even if they may also limit internal diversity in some cases (Huang et al., 2024; Oddný et al., 2023). This study treats conspiracy communities on Reddit as a strategic case for examining how algorithms are perceived, questioned, and resisted within spaces where alternative knowledge and mistrust of institutional narratives are especially pronounced. The focus here is not on assuming that discussions in these communities are necessarily wrong, nor on adjudicating their truth or falsity. Rather, the aim is to analyze how these communities make sense of algorithms and how such interpretations become related to conspiratorial discourse. This study asks:

Data: Conspiracy Subreddits

This study adopts purposive sampling to select general conspiracy-focused subreddits rather than single-issue conspiracy communities, fringe-interest forums, or debunking and skeptical spaces. The selection focused on subreddits centered on broad conspiracy discussion across multiple topics, with further consideration given to public accessibility, continuity of discussion, sustained user-generated content over time, and relative size and activity. Based on these criteria, four subreddits were selected: r/Conspiracy, r/Conspiracytheories, r/Conspiracy_commons, and r/ConspiracyII. Together, these four subreddits had 2,399,184 subscribers in total, although this figure reflects the sum of subreddit subscriber counts and does not represent unique users across communities. I collected all submissions (i.e., initial posts) and comments containing the keyword ‘algorithm’ from these subreddits using the Pushshift API, which has been live streaming and archiving Reddit data since June 2005 (Baumgartner et al., 2020). I decided to collect content beginning from January 2011, when social media users became aware of and started discussing algorithms, to May 2022. In total, I collected 10,087 threads. Summary statistics for the collected subreddit data are presented in Table 1.

OPEN IN VIEWER

Table 1.

Summary Statistics for Collected Subreddit Data.

Subreddit	Number of subscribers a	Number of submissions on algorithms	Number of comments on algorithms
r/Conspiracy	1,762,308	604	9,272
r/Conspiracytheories	474,798	29	282
r/Conspiracy_commons	125,669	30	210
r/ConspiracyII	36,409	6	48
Total	2,399,184	669	9,812

a

Numbers of subscribers as of June 25, 2022.

Reddit does not require real names for registration or use, making it challenging to obtain accurate demographic data for members of these subreddits. However, estimates based on Reddit’s site administrators and survey results suggest that its user base tends to be young (58% are between 18 and 34 years), predominantly male (57%), and largely American (50%) (Reddit.com, 2021; Sattelberg, 2021). These figures should be treated only as approximate background characteristics rather than stable demographic descriptors for the research period. Although this sample is not fully representative, the aim of this study is not to produce a representative sample of all conspiracy communities on Reddit, but to map patterns in how conspiracy communities understand algorithms. In this sense, the dataset is suitable for this study.

Methods: A Mixed-Methods Approach to Text Analysis

I used a mixed-methods approach, combining computational, qualitative, and quantitative methods, to analyze the collected data. This approach enabled a systematic analysis of a large corpus while incorporating human-centered, in-depth text interpretation (Nelson, 2020). First, I conducted topic modeling on the entire corpus to uncover the main topics and themes of discussions surrounding algorithms. Next, I used a dictionary-based method to identify broad patterns of conspiracy-related discourse and references to algorithm-embedded platforms and applications. Qualitative analysis then served as an interpretive layer across these computational steps, contributing to validation, category construction, and coding. Finally, I used chi-square tests to examine the association between coded perceptual approaches, ontological perspectives, and conspiratorial engagement, and used the Wilcoxon rank-sum test to compare dictionary-based conspiracy-related discourse scores between posts referencing well-known platforms and applications and those that did not, using such references as a proxy for use experience with algorithm-driven applications.

Perceiving Algorithms Among Conspiracy Communities

With the advancement of social automation and the widespread application of algorithms in various fields, ordinary people have more opportunities to encounter and an increasing interest in understanding algorithms. The same trend is evident within conspiracy communities, where mentions of the term ‘algorithm’ are continually on the rise (see Figure 1). The discourse among conspiracists covers a broad spectrum of the social implications of algorithms, ranging from real-world to imagined applications, and from technical details to metaphorical concerns. As the primary way regular people encounter algorithms, a significant majority—approximately 71% of the posts—mention algorithmic applications within the realm of digital platforms. Discussions around social media algorithms dominate, focusing on issues of algorithmic curation and targeted advertising on platforms such as Reddit and Facebook. There is also considerable interest in the algorithms of other digital applications, including search engines like Google and DuckDuckGo, online shopping platforms such as Amazon, and, in some cases, Wayfair, which appears in a more conspiracy-specific context (Walter et al., 2025). Conspiracists also explore a wide range of algorithmic applications beyond digital platforms. Some discussions are grounded in concrete and technical aspects, such as cryptocurrency algorithms, encryption and cryptographic algorithms, and facial recognition algorithms. However, many discussions involve vague, speculative, or imagined applications. These include alleged election algorithms manipulated during the U.S. presidential election, mass surveillance algorithms speculated to support a potential police state, financial algorithms thought to manipulate the financial market, and even hypothetical algorithms predicted to supplant human control.

OPEN IN VIEWER

Figure 1.

Posting frequency of content with ‘algorithm’ (N = 10,087).

To uncover the primary themes in these discussions, I applied topic modeling to the textual corpus, identifying 24 topics that were aggregated into seven clusters, which reflect the main concerns conspiracy communities project onto algorithms (see Table 2). Members of conspiracy communities expressed significant fears surrounding information integrity, particularly concerns about the manipulation of information (Cluster 1) and the unintended consequences of inaccurate or biased algorithms (Cluster 2). Evidence from the discussions also reveals widespread anxiety over surveillance and social control, with members highlighting issues such as privacy infringement and the manipulation of social behaviors (Clusters 3 and 4). In addition, concerns regarding market manipulation, power consolidation, and the potential misuse of algorithms by elites were prevalent (Clusters 5, 6, and 7).

OPEN IN VIEWER

Table 2.

Clusters of Algorithmic Concerns in Conspiracy Communities.

Clusters	Topic numbers	Example quote
(1) Manipulate information and public opinion	1, 2, 8	I just went under the Reddit Popular Tab and I’m stunned on how many of the posts are liberal propaganda and about anti-vaxers . . . It’s absolutely atrocious that Reddit has this centralized control over a closed sourced algorithm which they use to manipulate the thoughts and feelings of the less informed Reddit user.
(2) Inaccuracy, bias, and unintended consequences	6, 7, 17, 18, 19, 21, 22	A filter bubble is a state of intellectual isolation that can result from personalized searches when a website algorithm selectively guesses what information a user would like to see based on information about the user . . . Now, what does this have to do with suicide? Well, these filters are just another element of isolation (which causes suicide).
(3) Surveillance, data abuse, and privacy	4, 12, 15	I am 100% sure my phone is listening to my conversations and using the information to send me ads. I am a 30 something year old dude, why would the YouTube algorithm suggest I would want to buy this exact lady’s perfume unless it already knew I had been talking about it?
(4) Social control	9, 10, 11	With the massive crisis. . .There will be formed a police state and we will get disarmed. People are willing to get the chip implanted. it will with an algorithm check if you are obeying the restrictions.
(5) Business and economic manipulation	3	The debt that has been created out of thin air can’t possibly be paid back because there is more debt in the system then there is money to pay for the debt, therefore the money printing will effectively have to continue exponentially until the system collapses . . . there is a certain possibility that the globalists were the ones who were behind Bitcoin to begin with as Bitcoins hashing algorithm ‘SHA-256’ was originally developed by the National Surveillance Agency.
(6) Power and elitism consolidation	13, 14, 16, 23	I’ve been following the luciferian cult in the celeb and political circles for years on YouTube before they sold out to Google and introduced the algorithm to reflect only the elite agenda with paid corporate sponsorship. Around the 08 recession. All of a sudden you couldn’t find anything on YouTube about it. It’s not even a question that there’s a cult in the elite world. It’s a big club and you’re not in it.
(7) Antihuman misuse, apocalypse	5, 20, 24	Algorithms have initiated a fourth dimensional attack on a largely unsuspecting humanity. By simulating macro-events and the currents of the future with hoarded data, the masters of the algorithm are able to witness the variables needed for the potential future timeline of their preference to occur in actuality. With the same power, they are also able to close off potential future timelines of collective human progress with the right pieces in place.

As existing literature suggests, people understand algorithms in a variety of ways, using knowledge, experience, imagination, and critique to interpret the role of algorithms in both individual and societal contexts. The analysis identified four distinct perceptual approaches:

I’ve been following the Luciferian cult in the celeb and political circles for years on YouTube before they sold out to Google and introduced the algorithm to reflect only the elite agenda with paid corporate sponsorship. Around the 08 recession. All of a sudden you couldn’t find anything on YouTube about it.

Wayfair is like amazon for furniture, they allow vendors to post their listing on the website and set a cost, an algorithm then sets a price including the handling fee for Wayfair. If the price is that high, it’s because the vendor set the cost high.

. . . try to follow lots of different types of people with different opinions. Otherwise, the algorithm will only show you what it ‘think’ you want to see . . .

The debt that has been created out of thin air can’t possibly be paid back because there is more debt in the system then there is money to pay for the debt, therefore the money printing will effectively have to continue exponentially until the system collapses . . . there is a certain possibility that the globalists were the ones who were behind Bitcoin to begin with as Bitcoins hashing algorithm ‘SHA-256’ was originally developed by the National Surveillance Agency.

As algorithms are frequently mentioned yet inconsistently defined in these discussions, I identify three ontological perspectives, that is, three ways members of conspiracy communities understand what algorithms are and how they exist in the social world. The first perspective views algorithms as technical artifacts or infrastructures, such as programming code or software. The second perspective sees algorithms as referents of socio-technical systems, particularly as mechanisms through which power elites can benefit or exert influence. The third perspective perceives algorithms as symbols of pervasive social automation, representing a potential existential threat to society and humanity.

To answer RQ1, members of conspiracy communities understand algorithms through multiple perceptual approaches, projecting concerns about information integrity, manipulation, social control, power consolidation, and antihuman misuse. Their discussions reflect anxieties about the pervasive influence of social automation. The next section examines which of these perceptual approaches and ontological perspectives are more likely to be associated with conspiratorial engagement.

Engaging Algorithmic Conspiracy Theories and Algorithmic Resistance

Using the conspiracy term dictionary described in the Methods section, I identify four recurring categories of conspiracy-related discourse that appear in discussions of algorithms: (1) elite and deep state theories, (2) brainwash and total surveillance state theories, (3) science denial and pseudoscience theories, and (4) supernatural, extraterrestrial, and doomsday conspiracy theories (see Table 3).

OPEN IN VIEWER

Table 3.

Categorization of Conspiracy Theories Engaged With Algorithmic Perceptions.

Conspiracy theories	Description	Key terms mentioned (frequency)
(1) Elite and Deep State	Claims that elites or secret government entities control world systems via algorithms	‘elite’ (53), ‘pizzagate’ (53), ‘Jew’ (16), ‘Illuminati’ (15), ‘cover up’ (11), ‘deep state’ (10), ‘secret society’ (9), ‘George Soros’ (2)
(2) Brainwash and Total Surveillance State	Belief in constant monitoring and manipulation through algorithms for brainwashing and control	‘fake news’ (82), ‘spy’ (76), ‘monitor’ (75), ‘surveillance’ (54), ‘brainwash’ (38), ‘mind control’ (13), ‘Big Brother’ (10)
(3) Science Denial and Pseudoscience	Challenges mainstream science, linking algorithms to climate, 5G, vaccine, and other theories	‘flat earth’ (35), ‘5g’ (13), ‘climate change’ (15), ‘vaccine injury’ (3)
(4) Supernatural, Extraterrestrial, and Doomsday	Focuses on aliens, UFOs, and doomsday scenarios linked to algorithmic control or support	‘alien’ (36), ‘UFO’ (17), ‘genocide’ (15), ‘reptilian’ (4), ‘Area 51’ (2)

Even within conspiracy theory communities, not all discussions involve conspiratorial narratives. In fact, the vast majority of posts and comments do not engage with any mainstream conspiracy theories: only 21% of the posts matched at least one conspiratorial term. So, what types of discussions are more likely to engage with conspiracy theories? Using the conspiracy term dictionary, I identified and randomly sampled 50 posts showing conspiratorial engagement and 50 posts that did not show such engagement and compared them based on their perceptual approaches and ontological perspectives. The results indicate that posts engaging with conspiracy theories are more likely to use perceptual approach 4, Imagining Impacts and Motivations, to perceive algorithms, while those not engaging predominantly rely on Observation and Experience to understand algorithms (χ² = 17.44, p < .001). I also found a strong association between engagement with conspiracy theories and ontological perspectives on algorithms (χ² = 20.7, p < .001). Specifically, viewing algorithms as symbols of pervasive social automation is significantly associated with engagement in conspiracy theories, whereas those who perceive algorithms as technical artifacts or infrastructures are less likely to engage in conspiratorial thinking. This suggests that more technical understandings of algorithms are less likely to be associated with conspiratorial engagement. Together, these two dimensions help answer RQ2 by showing that conspiratorial engagement is associated with both how users come to know algorithms and what kinds of social entities or systems they imagine algorithms to be. However, when algorithms are imagined as all-power systems, they are more likely to be associated with conspiratorial thinking, as demonstrated by the following quote:

This is the automation of humans into animals through narrative . . . the sefirot as an algorithm for building the automaton, focusing the mind through archetypes . . . This thread is about automating humanity through symbols and stories so they never see this, only seeking answers from those that control access. It is to make humans a battery for the interests that subvert their will.

These findings suggest that perceiving algorithms as distant, vague, and abstract entities, along with suspicions about the motives behind them, is more likely to be associated with conspiratorial discourse. These perceptions often evoke imaginations about power consolidation, social control, pseudoscience, and even supernatural or doomsday scenarios, which sometimes lead to algorithmic resistance. Algorithmic resistance refers to actions taken by users to counter or challenge the control of algorithms, often by ‘repairing’ or subverting the biases and misrepresentations embedded in them (Velkova & Kaun, 2021). Studies indicate that users may take action against algorithmic systems based on their algorithmic imaginaries, particularly when they perceive algorithms negatively or as being used against their interests (Bishop, 2019; Ytre-Arne & Moe, 2021). For example, posts from conspiracy communities often call for resistance against algorithms, referring to this as the ‘awakening’ moment:

Resist! You choose how you live your life . . . not some ‘immunity checkpoint’ algorithm or QR code. We (anti-vaxers) only appear as a minority on social media cause we get so heavily censored as well as all the bots and govt shills. The woke mob on Twitter gets followed by bots because the algorithm wants us to feel helpless . . .

Another post calls on everyone not to use Google search because it’s part of the grant algorithm-facilitated social automation project:

Seriously switch your search engine from google . . . I know for a fact you Americans didn’t vote for Biden and I also know he didn’t beat Obama in vote counts without even campaigning. The social media suppression and other illegal tactics will eventually come to light. I also know that conservative YouTubers were toppling liberal political channels until they outright used the algorithm to bury them . . . American social media platforms along with the US government now think they control your thoughts and what you believe, and sadly for now they are when it comes to many.

A related question is how everyday use experience with algorithm-driven applications relates to these interpretations. To assess it, I compared posts referencing algorithms on popular platforms and applications, such as Reddit, Facebook, Airbnb, and Uber, with posts that did not include such references. As described in the Methods section, these references were treated as a proxy for use experience with algorithm-driven applications. The analysis reveals that posts mentioning such platforms and applications exhibit significantly higher dictionary-based conspiracy-related discourse scores (M = 0.557) than posts that do not mention them (M = 0.332), as indicated by the Wilcoxon rank-sum test (W = 11,810,954, p < .001). This suggests that use experience with algorithm-driven applications does not straightforwardly correspond to lower conspiratorial engagement. Instead, what appears to matter is how algorithmic systems are perceived and interpreted. This finding helps answer RQ3 by showing that use experience becomes consequential through the perceptual and ontological frames through which users interpret algorithmic systems, rather than through exposure alone.