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Diverse Misinformation: Impacts of Human Biases on Detection of Deepfakes on Networks

Social media users are not equally susceptible to all misinformation. We call ``diverse misinformation'' the complex relationships between human biases and demographics represented in misinformation, and its impact on our susceptibility to misinformation is currently unknown. To investigate how users' biases impact susceptibility, we explore computer-generated videos called deepfakes as a type of diverse misinformation. We chose deepfakes as a case study for three reasons: 1.) their classification as misinformation is more objective; 2.) we can control the demographics of the persona presented; and 3.) deepfakes are a real-world concern with associated harms that need to be better understood. Our paper presents a survey (N=2,000) where U.S.-based participants are exposed to videos and asked questions about their attributes, not knowing they might be a deepfake. Our analysis investigates the extent to which different users are duped and by what perceived demographics of deepfake personas. First, if users not explicitly looking for deepfakes are not particularly accurate classifiers. Importantly, accuracy varies significantly by demographics, and participants are generally better at classifying videos that match them (especially male, white, and young participants). We extrapolate from these results to understand the population-level impacts of these biases using an idealized mathematical model of the interplay between diverse misinformation and crowd correction. Our model suggests that a diverse set of contacts might provide ``herd correction'' where friends can protect each other's blind spots. Altogether, human biases and the attributes of misinformation matter greatly, but having a diverse social group may help reduce susceptibility to misinformation.

T here is a growing body of scholarly work focused on distributed harm in online social networks: from leaky data (1) and group security and privacy (2) to hate speech (3), misinformation (4) and detection of computer-generated content (5). Social media users are not all equally susceptible to these harmful forms of content. Our level of vulnerability depends on our own biases. We call "diverse misinformation" the complex relationships between human biases and demographics represented in misinformation. Our paper explores deepfakes as a case study of misinformation to investigate how U.S. social media users' biases influence susceptibility to misinformation. We choose deepfakes as a critical example of the impacts of diverse misinformation for three reasons: 1.) their status of being misinformation is binary; they either are a deepfake or not; 2.) the perceived demographic attributes of the persona presented in the videos can be characterized by participants; 3.) deepfakes are a current real-world concern with associated negative impacts that need to be better understood. Together, this allows us to use deepfakes as a critical case study of diverse misinformation to understand the role individual biases play in disseminating important misinformation online.
We present an empirical survey (N=2,000) testing what shapes participants' ability to detect deepfake videos. Survey participants entered the study under the pretense that they would judge the communication styles of video clips. Our study is careful not to prime participants so we could gauge their ability to view and judge deepfakes when they were not expecting them. Our survey also investigates the relationship between human demographics and the video person(a)'s features and, ultimately, how this relationship impacts the participant's ability to detect deepfake content.
Specific to deepfakes, our paper explores four distinct questions related to bias. (Question 1) Priming bias: How much does classification accuracy depend on participants being primed about the potential for a video to be fake? Our participants are not primed on the meaning of deepfakes and are not explicitly looking for them. This paper adopts a multidisciplinary approach to answer these questions and understand their impacts. We use a survey analysis to measure individual biases related to deepfake detection. We then use results from the survey to motivate an idealized mathematical model to theoretically explore population-level dynamics of diverse misinformation on online social networks. Altogether, this allows us study the mechanisms and impacts of diverse misinformation, as illustrated in Fig. 1.
Our paper is structured as follows. We outline the harms and ethical concerns of diverse misinformation and deepfakes in Section 1. We explore the effects through which demographics impact susceptibility to diverse misinformation through our empirical study in Section 2. We then investigate the networklevel dynamics of diverse misinformation using a mathematical model in Section 3. We discuss our findings and their implications in Section 4. Our full survey methodology can be seen in Section A.

Background
A. Misinformation. Misinformation is information that imitates real information but does not reflect the genuine truth (6). Misinformation has become a widespread societal issue that has drawn considerable recent attention. It circulates physically and virtually on social media sites like Facebook and Twitter in particular (7). Motivations vary broadly to explain why people disseminate misinformation and disinformation (i.e., misinformation with intent to deceive). Some examples of motivation include: 1) purposefully trying to deceive people by seeding distrust in information; 2) believing the information to be true and spreading it mistakenly; and 3) spreading misinformation for monetary gain.
There are many contexts where online misinformation is of concern. Examples include : 1) misinformation around political elections and announcements; 2) misinformation on vaccinations during global pandemics (8,9); 3) false speculation to disrupt economies or speculative markets (10); 4) distrust in news media and journalism (4, 11); 5) false information in critical informational periods such as humanitarian or environmental crises (12), and 6) propagation of hate speech online (3) which spreads harmful false content and stereotypes about groups.
Political harms: Recent elections in the United States, the United Kingdom, and Brazil, as well as other events like Brexit (13,14), sparked a wave of widespread interest in misinformation and its impact on politics. Large-scale misinformation campaigns raise concerns about the potential rise of nationalist and populist political movements, growing political polarization, and declining public trust in the media (6,15,16).
Health-related harms: The spread of misinformation in critical informational periods such as humanitarian crises (12) and global health crises are particularly concerning. Information silos and echo chamber effects work to amplify the spread of health-related misinformation (8). Misinformation campaigns have adversely impacted healthcare efforts in the Ebola, and COVID-19 pandemics (8,9,17). The harms associated with health misinformation have a worldwide impact and undermine efforts to stop the spread of deadly infectious diseases. Inaccurate assessments of the threat and unhelpful and dangerous treatment can result from exposure to false information. Misinformation undermines public health professional's ability to communicate important health-related information about signs of infection, testing accuracy, and prevention techniques (18,19). More recently, misinformation about COVID-19 vaccines could lead to significant vaccine hesitancy in the general population (18,19).
Harms to news media: The public's trust in news institutions has significantly declined in recent years (4,11,20,21). Academics, journalists, and politicians are concerned that the rise of fake news will undermine media credibility and weaken these institutions (20,22). Fake news creators often work to enhance audience engagement through provocative and polarizing material. Additionally, false information can damage the media's reputation by accusing them of being biased, complicit, and incompetent and refuting various statements made by credible mainstream media (20).
Harms related to hate speech: Misinformation plays an essential role in the propagation of hate speech online (especially on social media platforms) (3). This contributes to the spread of harmful false content and stereotypes about groups. Social media platforms are recognized as places where race and racism manifest themselves in troubling ways (23,24). The features, rules, algorithms, and business decisions of social media platforms influence the dissemination of hate speech even more (25). According to Sue et al. (26), covert and overt discrimination can be seen in social media platform governance and designs.
Correction of misinformation: There are currently many ways to try to detect (27) and mitigate the harms of misinformation online. On one end of the spectrum are automated detection techniques that focus on the classification of content or on observing anomaly detection in the network structure context of the information or propagation patterns (28,29). Conversely, crowd-sourced correction of misinformation leverages other users to reach consensus or simply estimate the veracity of content (30)(31)(32).
Connection with deepfakes: The potential harms of misinformation can be amplified by computer-generated videos used to give fake authority to the information. Imagine, for instance, harmful messages about an epidemic conveyed through the computer-generated persona of a public health official. Unfortunately, deepfake detection remains a challenging problem, and the state-of-the-art currently involves human judgment (5).

B. Deepfakes.
Deepfakes are artificial images or videos in which the persona in the video is generated synthetically. Deepfakes can be seen as false depictions of a person(a) that mimics a person(a) but does not reflect the truth. Deepfakes should not be confused with augmented or distorted video content, such as using color filters or adding stickers to a video. Creating a deepfake can involve complex methods such as training neural networks such as generative adversarial networks (GANs) on existing media (33) or simpler techniques such as face mapping. Deepfakes are deceptive tools that have gained attention in recent media for their use of celebrity images and their ability to spread misinformation across online social media platforms (34).
Biological signals, including eye blinking, heartbeat, and head tilting, often expose that the persona featured in the video is not real. For example, blinking behaviors in humans occur unconsciously and repeatedly. Even though eye blinking is a normal phenomenon, factors such as gender and time of day influence the rate at which it occurs (35). Human-aided interventions for deepfake detection can be a valuable tool to help to assist automated techniques, especially when there are gaps in automation (5).
Early deepfakes were easily detectable with the naked eye due to their unnatural visual attributes and movement (36). However, research and technological developments have improved deepfakes, making them more challenging to detect (4). There are currently several automated deepfake detection methods (35,(37)(38)(39)(40). However, they are computationally expensive. As deepfakes become ubiquitous, it will be important for the general audience or viewers to identify deepfakes independently during gaps between the development of automated techniques or in environments that are not always monitored by automated detection (or are offline). It will also be important to allow human-aided and human-informed deepfake detection in concert with automated detection techniques.
Several issues currently hinder automated methods: 1) they are computationally expensive; 2) there is quite a lot of bias in deepfake detection software and training data-credibility assessments, particularly in video content, have been shown to be biased (41); 3) As we have seen with many cybersecurity issues, there is a "cat-and-mouse" evolution that will leave gaps in detection methodology; humans can help fill these gaps. However, we wonder to what extent human biases impact the efficacy of detecting diverse misinformation. If human-aided deepfake detection becomes a reliable strategy, we need to understand the biases that come with it and what they look like on a large scale. We also posit that insights into human credibility assessments of deepfakes could help develop more lightweight and less computationally expensive automated techniques.
C. Bias Types. Human biases are important to understand as they impact the transmission and correction of misinformation and its impacts on polarization (42). Indeed, there are differences in user demographic groups (e.g., age) abilities to detect deepfakes and misinformation (43).
Previous work has demonstrated that homophily bias towards content aligned with one's political affiliation can impact one's ability to detect misinformation (44,45). Traberg et al.
show that political affiliation can impact a person's ability to detect misinformation about political content (44). They found that viewers misclassified misinformation as being true more often when the source of information aligned with their political affiliation. Homophily bias, in this case, made them feel as though the source was more credible than it was.
In this paper, we investigate the accuracy of deepfake detection based on multiple homophily biases in age, gender, and race. We also explore other bias types, such as heterophily bias, priming, and prior knowledge bias impacting deepfake detection.
D. Ethical considerations regarding deepfakes. As deepfakes improve in quality, the harms of deepfake videos are coming to light (46). Deepfakes raise several ethical considerations: 1) the evidentiary power of video content in legal frameworks (4,47,48); 2) consent of the individual(s) featured in deepfake videos (49); 3) bias in deepfake detection software and training data (41); 4) degradation of our epistemic environment (meaning there is a large-scale disagreement between what community members believe to be real or fake), including misinformation and distrust (4, 11); 5) and possible intrinsic wrongs of deepfakes (50). Understanding who gets duped by these videos and how this impacts people's interaction with any video content is necessary.
The gap between convincing deepfakes and reliable detection methods could cause significant harm to democracy, national security, privacy, and legal frameworks (4). Consequently, additional regulatory and legal frameworks (51) will need to be adopted to protect citizens from harms associated with deepfakes and uphold the evidentiary power of visual content. Any outcome will be similar to the False Light Invasion of Privacy laws which were put in place to allow a plaintiff to claim civil tort when privacy harms were created by falsified evidence, particularly still images (47). However, using these laws is often not easy due to the supporting evidence required.
In the same way, any laws and regulations against deepfake manipulation are only as powerful as our ability to detect the video manipulation and a plaintiff's ability to show that the defendant created the video to deceive the courts. Other state-level laws (52) have emerged, making specific deepfake manipulations a crime. Still, these efforts are particular in scope, have been criticized as difficult to enforce, possibly an infringement on the constitutional rights to free speech, and are limited to specific regions in the U.S. (53). To further define the importance of deepfake detection for legal contexts, it is important to determine which attributes cause a jury or judge to detect a deepfake video themselves.
Deepfake harms in the U.S. legal framework have been primarily concerned with the invasion of personal privacy. This calls into question an ethical consideration about the extent to which a person has autonomy and control over their person's likeness. The boundaries are fuzzy in this regard. It seems clear that GAN revenge porn, where one person distributes photographic or video representations of another person engaged in sexual activity without the other person's consent, would violate privacy (54). However, where does the boundary fall? Does a GAN art video fall into this scope? There are also questions about the implications for copyright and intellectual property infringement.
The ethical implications of deepfake videos can be separated into two main categories: the impacts on our epistemic environment and the moral relationships and obligations people have with others and themselves. Consider first the epistemic environment, which includes our capacity to take certain representations of the world as true and our taking beliefs and inferences to be appropriately justified. Audio and video are particularly robust and evocative representations of the world. They have long been viewed as possessing more testimonial authority (in the broader, philosophical sense of the phrase) than other representations of the world. This is true in criminal and civil contexts in the United States, where the admissibility of video recordings as evidence in federal trials is specifically singled out in Article X of the Federal Rules of Evidence (55). (State courts have their own rules of evidence, but most states similarly have explicit rules that govern the admissibility of video recordings as evidence.) The wide adoption of deepfake technology would strain these rules of evidence; for example, the federal rules of evidence reference examples of handwriting authentication, telephone conversation authentication, and voice authentication but do not explicitly mention video authentication.
Moreover, suppose juries are aware of the possibility that a video is a deepfake. That might affect the degree to which they treat the video representation as authoritative, regardless of the video's being admitted as evidence. To that end, it is important to know how reliable people are concerning deepfake identification and what factors might be relevant.
Belief and knowledge transmission often happen in nonregulated environments, and this belief and knowledge transmission are of particular importance for democratic societies. A wide prevalence of deepfake videos would harm such belief and knowledge transmission and represent a degradation of our epistemic environment. In addition, legal contexts do not exhaust the epistemic environment; people often take video representations of the world as more truthful or authoritative.
Deepfake videos do not merely degrade the epistemic environment, however, because deepfakes do not merely represent the world; they also represent other agents in the world. As such, deepfakes entail implications for our moral obligations towards one another. Because one person can create a deepfake video representing another person, the question is naturally raised as to whether consent is a necessary condition for such a video to be morally permissible. To what extent, that is, is control over our likeness within the proper scope of our autonomy? For example, some state statutes that outlaw revenge porn do not specify whether the photographic or video representation has to be authentic. Whether people believe the image to be real might be morally significant; however, compare a crude animation of a person engaged in sexual activity with a highly realistic deepfake video of the same activity. The fact that most people disbelieve the crude animation seems morally relevant. Potential harms and moral wrongs of the deepfake video depend on whether the video is viewed as veridical. Moreover, there is every reason to suspect that these social harms are not distributed equally but that more socially vulnerable people are subject to greater social harm.
These considerations make it critical to identify the factors that lead people to classify things as real or fake.
E. Research Questions. Our paper asks four primary research questions regarding how human biases impact deepfake detection. (Q1) priming How important is it to know that a video might be fake? (Q2) prior knowledge How important is it to know about deepfakes, and how does social media usage affect accuracy? (Q3-Q4) homophily and heterophily bias Are participants more accurate at classifying videos whose persona they perceive to match (homophily) or mismatch (heterophily) their own demographic attributes in age, gender, and race?
To address our four research questions, we designed a survey (N=2,000) using video clips from the Deepfake Detection Challenge (DFDC) Preview Dataset (56,57). Our survey participants entered the study under the pretense that they would judge the communication styles of video clips (they were not explicitly looking for deepfake videos). After the consent process, survey participants were asked to watch two short clips. After each video, a questionnaire asked participants to rate the pleasantness of particular features (e.g., tone, gaze, likability, content) of the video on a 5-point Likert scale, they are also asked to state their perception of the persona in the video in terms of gender identity, age, and whether the person is white or a person of color.
After viewing both videos and completing the related questionnaires, the participants were then debriefed on the deception of the survey, given an overview of what deepfakes are, and then asked if they thought the videos they just watched were real or fake. After the debrief questions, we collected demographic information on the survey participants' backgrounds, demographics, and expressions of identity.
Our project investigates features or pairings of features (of the user or the person(a) in the video) that are most important to determine a user's ability to detect deepfake videos and avoid being duped. Conversely, what pairings of features (of the user or the person(a) in the video) are important to determine a user's likelihood of being duped by a deepfake video?
Our null hypothesis asserts that none of the features or pairing of features we measure in our survey produce biases that are significantly important in a user being duped by a deepfake video or being able to detect a deepfake video. We then measure our confidence in rejecting this null hypothesis by measuring a bootstrap confidence interval for a difference in means test between the accuracy of two populations. In all tests, we use 10,000 bootstrap samples such that confidence of 90% in rejecting the mean means that one was better than the other in 9,000 pairs of samples from the original two populations. With this method, our paper aims to better understand how potential social biases affect our ability to detect misinformation.

Results
The overarching takeaway of our results can be summarised as follows: (Q1) If not primed, our survey participants are not particularly accurate at detecting deepfakes (51%). (Q2) Accuracy increases with frequent social media usage and knowledge of deepfake. (Q3-Q4) Accuracy varies by demographics of participants and video persona, so participants were generally better at classifying videos that match their own demographic. Our results show that of the 4,032 total videos watched, 49% were deepfakes, and 72% of those successfully duped our survey participants. A confusion matrix showing the True Positive (TP), False Negative (FN), True Positive TP), and True Negative (TN) rates can be seen in Fig. 2. We also note that the overall accuracy rate (where accuracy = (TP+TN)/(TP+FP+FN+TN)) of our participants was 51%. This translates to an overall Matthew's Correlation Coefficient (MCC, a simple binary correlation between the ground-truth and the participant's guess) score of 0.0334 for all participant's guesses vs. actual states of the videos. Regardless of the metric used, our participants were barely better than a simple coin flip (confidence 94%). All summary statistics for our study and all confusion matrices for our primary and secondary demographic groups can be found in Appendix SI2 and Appendix SI3 respectively (58). Q1: Priming Bias: Our results suggest that priming bias may play an important role in a user's ability to detect deepfakes. Compared with notable prior works (5, 59, 60), our users were not explicitly told to look for deepfake videos while viewing the video content. Our survey takers participated in a deceptive study where they thought they answered questions about effective communication styles that make videos credible communicators. Only after the survey participants completed the questionnaire about the videos were they debriefed and asked if they thought the video clips were real or fake. Priming, on the contrary, would mean that when the user watched the two video clips, they would be explicitly looking for deepfakes.
Our results show that our non-primed participants were only 51% accurate at detecting if a video was real or fake. In comparison, there was a 66% accuracy rate in a previous study using the same benchmark video data (5) where subjects were informed and explicitly looking for deepfakes (see Table 1).
Other works measured primed human deepfake detectors to compare them to machines and humans with machine aid. For example, in a study by Azur et al., HYPE's humans were used as deepfake evaluators on Amazon Mechanical Turk. Their participants were explicitly asked to view images and look for fake images. Participants in the HYPE study were also required to pass a qualification test where they needed to correctly classify 65% real and fake images to participate in the study (59) fully. As another example, in a 2022 study by Groh et al. (5), they asked survey participants to view video clips from the Facebook Deepfake Detection Challenge Dataset (DFCD) and asked them to explicitly look for deepfake videos and then tested how this compared to machines alone and machines aided by humans. Groh et al. found an accuracy score of 66% for primed humans, 73% for a primed human with a machine helper, and 65% for the machine alone. In another study, Chen et al. (60) show that hybrid systems that combine crowd-nominated and machine-extracted features outperform humans and machines alone.
One important takeaway from these related works is that human-machine cooperation provides the best accuracy scores. However, all of these works are performed with primed participants. We believe a more realistic reflection of how deepfake encounters would occur 'in the wild' would be with observers who were not explicitly seeking out deepfakes. Future work is needed to investigate how non-primed human deepfake detectors perform when aided by machines.

Q2 Prior knowledge effect:
We also ask if participants are better at detecting a deepfake if they have prior knowledge about deepfakes or more exposure to social media. In Fig. 3, we plot MCC scores by target variables for prior knowledge bias. Our results show that accuracy varies by prior knowledge but not significantly. We see that participants who are frequent social media users (use social media once a week or more) had a higher MCC score (MCC = 0.0396) than those who used social media less frequently (MCC = -0.0110). In this case, we can reject the mean with only 83% confidence. Participants who knew what a deepfake was prior to taking the survey (MCC = 0.0790) were also better than those unfamiliar with , not knowledgeable about deepfakes (blue), uses social media more than once a week (yellow), uses social media less than once a week (black). MCC ranges from -1 (perfect negative correlation between prediction and truth: all misclassified) to +1 (perfect positive correlation between prediction and truth: all correctly classified) deepfakes (MCC = 0.0175). In this case, we can reject the mean with 94% confidence.

Q3-4: Homophily versus heterophily bias:
We then focus on the impacts that heterophily and homophily biases play on a participant's ability to detect if a video is real or a deepfake. We look at the MCC for all user groups and compare their guesses on videos that either match their identity (homophily) or do not match their own identity (heterophily). Results of these MCC scores related to homophily and heterophily bias can be seen in Fig. 4.
Based on our Matthew's Correlation Coefficient (MCC) (61, 62) values shown in Fig. 4, we can see that some of our demographic subgroups were significantly more accurate when guessing the state of videos that match their own demographic. We can test our null hypothesis by comparing the answers given by a certain demographic of participants when looking at videos that match and do not match their identity. We only see a significant homophily bias for white participants. Still, in that case, we can reject the null hypothesis that they are equally accurate on videos of white personas and personas of color with 99% confidence.
We can further break down this potential bias in two dimensions (overall demographic classes of the participants and video persona) in Fig. 5). We then see more significant results. Here we are comparing to subgroups of our survey participants (e.g., male vs. female viewers, person of color vs. white viewers, and young vs. old viewers) to see which groups perform better when watching videos of a specific sub-type (e.g., videos of men, videos of women, videos of persons of color, videos of white people, videos of young people, and videos of old people).
By gender, we find that male participant are significantly more accurate than female participants in multiple categories of video persona. Similarly, by race, we find that participants of color are significantly more accurate than white participants. Lastly, young participants have the highest accuracy score overall for any of our demographic subgroups. Of course, these results may be confounded with other factors, such as social media usage, which can be more prominent in one group (e.g., young participants) than another (e.g., older participants). More work here needs to be done to understand the mechanisms behind our results.
In summary, our main significant results (confidence above 95% in rejecting the mean) on human biases in deepfake detection are as follows. • White participants show a significant homophily bias, meaning they are more accurate at classifying videos of white personas than they are at classifying videos of personas of color.

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• When viewing videos of male personas, male participants in our survey are significantly more accurate than female participants.
• When viewing videos of the persona of color, participants of color are significantly more accurate than white participants.
• When viewing videos of young persons, participants between the ages of 18-29 are significantly more accurate than participants above the age of 50.

Mathematical model
To consider the implications of our survey results, we integrate some of our findings into an idealized mathematical model of misinformation to better understand how deepfakes spread on social networks with diverse users and misinformation. Models of misinformation spread often draw from epidemiological models of infectious diseases. This approach tracks how an item of fake news or a deepfake might spread, like a virus, from one individual to its susceptible network neighbors, duping them such that they can further spread misinformation. (63). However, unlike infectious diseases, an individual's recovery does not occur on its own through its immune system. Instead, duped individuals require fact-checking or correction from their susceptible neighbors to return to their susceptible state (64). We integrate these mechanisms with the core finding of our study: Not all classes of individuals are equally susceptible to misinformation. Specifically, we build a simple model to investigate two mechanisms where we expect an impact of heterogeneous susceptibility to misinformation based on individual demographic characteristics. (1) Individuals with increased susceptibility Fig. 5. MCC Scores, significance is anything above 95% using the bootstrapped comparisons between MCC scores for features paring seen in this figure. MCC ranges from -1 (perfect negative correlation between prediction and truth: all misclassified) to +1 (perfect positive correlation between prediction and truth: all correctly classified). * Confidence intervals greater than 95% measure the confidence of MCC Scores between user groups using a bootstrapping method to compare means.
should be preferentially duped, but this effect exists only if misinformation can spread (above a certain contagion threshold) but not saturate the population (below certain transmissibility). (2) Individuals with a diverse neighborhood are also more likely to have friends who can correct them should they be duped by misinformation.
Our model uses a network with a heterogeneous degree distribution and a structure inspired by the mixed-membership stochastic block model (65). This stylized structure captures the known heterogeneity of real networks, and its modular structure of echo chambers and bridge nodes with diverse neighborhoods (66). We then track individuals based on their demographics. These abstract classes, such as 1 or 2, could represent a feature such as younger or older social media users. We also track their state, e.g., being currently duped by a deepfake video (infectious) or not (susceptible). We also track the demographics of their neighbor to know their role in the network and exposure to other users in different states.
Our model has two critical mechanisms. First, inspired by our survey, individuals get duped by their duped neighbor at a rate λi dependent on their demographic class i. Second, as in crowd-sourced approaches to correction of misinformation based on the "self-correcting crowd" (30)(31)(32), duped individuals can get corrected by their susceptible neighbors at a fixed rate γ. The dynamics of the resulting model are tracked using a heterogeneous mean-field approach (67) detailed in Box 1 and summarized in Fig. 6.
This model has a simple interesting behavior in homogeneous populations and becomes much more realistic once we account for heterogeneity in susceptibility. In a fully homogeneous population, λi = λ ∀ i, if misinformation can, on average, spread from a first to a second node, it will never stop. The more misinformation spreads, the fewer potential fact-checkers remain. Therefore, misinformation invades the entire population for a correction rate γ lower than some critical value γc, whereas misinformation disappears for any correction rate γ larger than the critical correction rate γc.

Box 1: Mathematical model of diverse misinformation on social networks
We wish to explore the impacts of our results on the spread of diverse misinformation on social networks. We want to consider that multiple independent streams of misinformation are spreading simultaneously; i.e., there are multiple sets of deepfakes, each with its own demographical biases. We also want to consider that social networks are often very heterogeneous with a skewed distribution of contacts per user and modular with denser connections among users of the same demographics. We account for the above using three stylized patterns for the network structure. First, we divide the network into two demographic classes of equal size, simply labeled 1 and 2. Second, we assume a power-law distribution p k of contacts k per user with p k ∝ k −γ regardless of demographics. Three, we use a mixed-membership stochastic block model to generate the network structure: Half of the nodes of each demographic always interact following their demographics, and half act as bridge nodes connecting randomly. The probability that a contact falls within a single demographic class is proportional to Q, while contacts across classes occur proportionally to 1 − Q; with Q > 0.5 for modular structure. According to the above, we can write the fraction of nodes p 1 k, which are of demographic class 1 with k contacts of class 1 and contacts of class 2: On this contact structure, we then define a simple dynamical process where individuals are exposed to misinformation through each of their duped network neighbors, and themselves get duped at a rate λi based on their demographic class i. We also introduce an important mechanism where non-duped neighbors can correct their duped neighbors at a rate γ (30-32), e.g., we assume that your network neighbors can fact-check something you diffuse online and potentially correct your opinion. The fraction of individuals of a certain type (i, k, ) that are duped, D i k, , can be followed in time using a set of ordinary differential equations: where θi,j and φi,j represent the probabilities that a connection from an individual of demographic i to an individual of demographic j connects to a duped or non-duped individual, respectively. They can be calculated, for example, as These quantities close the system of equations and allow us to simulate a relatively simple model that manages to capture the heterogeneity (γ) and community structure (Q) of social networks, as well as demographic-specific susceptibility to misinformation ({λi}) and fact-checking among the population (γ). Our results are summarized in Fig.  6 and further analyzed in Appendix SI4 (58).

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The invasion threshold for misinformation is shown in Fig. 6(a). In heterogeneous populations, where different nodes can feature different susceptibility λi, the discontinuous transition from a misinformation-free to a misinformation-full state is relaxed. Instead, a steady state of misinformation can now be maintained at any level depending on the parameters of misinformation and the demographics of the population.
In this regime, we can then further break down the dynamics of the system by looking at the role of duped nodes in the network, as shown in Fig. 6(b). The key result here is that very susceptible individuals with a homogeneous assortative neighborhood (e.g., an echo chamber) are at the highest risk of being duped. Conversely, nodes in the same demographic class but with a mixed or more diverse neighborhood are more likely to have resilient susceptible neighbors able to correct them if necessary.
Consider now that diverse misinformation spreads, with just two types of misinformation (say young or older personas in two deepfake videos) targeting each of our two demographic classes (say younger and older social media users). We find, in Fig. 6(c), two connectivity regimes where the role of network structure is critical. For low-degree nodes, a diverse neighborhood means more exposure to diverse misinformation than a homogeneous echo chamber. Such that the misinformation that best matches the demographics of a low-degree user is more likely to find them if they have a diverse neighborhood. For high-degree nodes, however, we find the behavior of herd correction: A diverse neighborhood means a diverse set of neighbors that is more likely to contain users able correct you if you get misinformed (12,68,69).
Our stylized model is meant to show how one can introduce biases in simple mathematical models of diverse misinformation. Future modeling efforts should also consider the possible interactions between different kinds of misinformation (70). These can be synergistic (71), parasitic (72), or antagonistic (73); which all provide rich dynamical behaviours. Other possible mechanisms to consider are the adaptive nature of online social networks can provide feedback loops to facilitate the spread of misinformation (74).

Discussion
Understanding the structure and dynamics of misinformation is important as it can come with a great and unfair amount of societal harm. Misinformation has negatively impacted the ability to disseminate important information during critical elections, humanitarian crises, global unrest, and global pandemics. More importantly, misinformation degrades our epistemic environment, particularly regarding distrust of truths. It is important to understand who is susceptible to misinformation and how it spreads on social networks to mitigate its harm and propose meaningful interventions. Further, as deepfakes deceive viewers at greater rates, it becomes increasingly critical to understand who gets duped by deepfakes and how our biases and social circle impact our interaction with video content. We hope this work will contribute to the critical literature on human biases and machine-generated content interplay.
The overarching takeaway of our results can be summarised as follows. (Q1) If not primed, humans are not particularly accurate at detecting deepfakes. (Q2) Accuracy increases with frequent social media usage and deepfake knowledge but not significantly. (Q3-Q4) Accuracy varies by demographics, but humans are generally better at classifying videos that match them. Combining these results, more work needs to be done to better understand how things like education about deepfakes and exposure to the technology impact a user's ability to detect deepfakes and thus drive our results. In future work, we hope to test education materials to see what makes a significant impact and what does not.
In this study, we also explored the impacts of these results in a simple mathematical model and extrapolated from our survey to hypothesize that a diverse set of contacts might provide "herd correction" where friends can correct each other's blind spots. Friends with different biases can better correct each other when duped. This modeling result is a generalization of the self-correcting crowd approach used in the correction of misinformation (30).
Susceptibility to misinformation varies based on our biases and the diversity of the social networks to which we belong. If we view an online social network as a community working together to help one another from getting duped, then perhaps it would be most advantageous to surround oneself with people with biases that differ from yours (75). Having a diverse set of contacts may help provide "herd correction" where friends can correct each other's blind spots.
In future work, we hope to investigate how non-primed human deepfake detectors perform when aided by machines. We also would like to empirically investigate our survey and model results on real-world social networks with different levels of diversity to measure the spread of diverse misinformation in the wild. Consequently, we would be interested to also test possible education or intervention strategies for adversarial misinformation campaigns. Our study is a step towards understanding the role and impacts of social biases in an emerging societal problem with many multilevel interdependencies.

Materials and Methods
A. Survey Methodology. We first ran a pilot stage of our study. We conducted a simple convenience sample of 100 participants (aged 18+) to test the efficacy of our survey. We then ran phase 1 (April-May 2022) of the full survey using a Qualtrics survey panel of 1,000 participants who matched the demographic distribution of U.S. social media users. We then ran phase 2 (September 2022) of the full survey, again using Qualtrics and the same sampling methodology. The resulting full study from phases 1 and 2 is a 2,000-participant sample.
Survey participants did not know before the start of the survey that the videos could potentially be deepfakes. The survey was framed for participants as a study about different communication styles and techniques that help make video content credible. Participants were told that we were trying to understand how aspects of public speaking, such as tone of voice, facial expressions, and body language, contribute to the effectiveness and credibility of a speaker. The survey's deceptiveness allowed us to ask questions about speaker attributes, likeability, and agreeableness naturally without priming the participants to look specifically for deepfakes (76). We chose to make our survey deceptive not to prime the participants but also because we feel this more closely replicates the deceptiveness of misinformation deepfakes a social media user would encounter in the real world.
We designed our survey using video clips from the deepfake Detection Challenge (DFDC) Preview Dataset (56,57). In our survey, we ask the participants to view two random video clips, which are approximately 10 seconds in length each. Each video clip may be viewed unlimited times before reading the questions but not again after moving to the questions. The information necessary to answer these questions relies solely on the previously shown video clip. A link to the full survey and survey questions is available in Appendix SI1 (58).
After viewing both videos, the participants are then asked to complete a related questionnaire about the communication styles and techniques of the videos. The questions ask about attributes of the video, such as pose, tone, and style and are asked to rate them on a Likert scale from very pleasant to very unpleasant. We also asked them to rate their agreement with the video content and credibility. We also ask participants to identify the perceived gender expression of the person(a) in the video, to identify what age group they belong to, and to ask if they perceive the person in the video to be a person of color or not.
Following the viewing of both videos and completion of a related questionnaire, the participants are debriefed on the deception of the survey, given a short explanation of deepfake technology, and then asked if they think the videos were real or fake (as seen in Fig. 7). Lastly, we collect demographic information on the survey participants' demographic background and expression of identity. We also ask participants how knowledgeable they already were on deepfakes, how often they use social media, and their political and religious affiliations. We also asked participants if they knew that the survey was about deepfakes before taking the survey (survey participants who were primed were subsequently dropped from the analysis).
Sampling: Survey responses from 2,000 participants were collected through Qualtrics, an IRB-approved research panel provider via traditional, actively managed, double-opt-in research panels (77). Qualtrics' participants for this study were randomly selected stratified samples from the Qualtrics panel membership pool that represents the average social media user in the U.S. (78). Our survey respondents represent the following categories and demographic breakdown in Table 3. For this project, we use the publicly available Facebook AI Research Deepfake Detection Challenge (DFDC) Preview Dataset (N = 5,000 video clips) (56,57 A video's deepfake status (deepfake or not) will not be revealed to the respondent during or after such a survey. Many augmenters and distractors will be noticeable to the respondent, but they shall not be specifically revealed.

Original Data
In order to analyze our survey results for our analysis, we transformed all survey response variables of interest into numerical form. All Likert survey questions were converted from 'Very unpleasant,' 'Unpleasant,' 'Neutral,' 'Pleasant,' and 'Very pleasant' to an ordinal scale of 1,2,3,4,5.
Participants selected their social media usage levels from 'I do not use social media,' 'I use social media but less than once a month,' 'Once a month,' 'A few times a month,' 'Once a week,' 'A few times a week,' 'Once a day,' 'More than once a day were transformed to an ordinal scale of 1-8 respectively. Variables were split into the category of frequent social media users 5-8 and infrequent social media users 1-4.
Participants selected their knowledge of deepfake from 'I did not know what a deepfake was,' 'I somewhat knew what a deepfake was,' 'I knew what a deepfake was,' 'I consider myself knowledgeable about deepfakes was transformed to an ordinal scale of 1-4 respectively. Variables were split into users who are knowledgeable about deepfakes 3-4 and users who are not knowledgeable about deepfakes 1-2.
All nominal and categorical variables were transformed into binary variables. Categorical variables (some survey questions included write-in answers) were combined into coarser-grained categories for analysis, such as participant racial/ethnic identity (transformed to Person of Color or White), U.S. state of residence (transformed to U.S. regions), employment (transformed to occupational sectors), religious affiliation (transformed into religious affiliations), and political affiliation (transformed to major political affiliations).
We allowed survey participants to identify their gender identity, the results of which were largely binary. Unfortunately, our sample was insufficient to perform meaningful analysis on a larger nonbinary gender identity spectrum. Variables with an N under 30 were subsequently dropped. Our survey participants were given two video clips to view and critique; in our analysis, we decided to analyze the first or second video in the same way.

Matthews Correlation Coefficient:
To understand the relationship between the participant's guesses on the status of the video (fake or real) and the actual state of the video (fake or real), we ran a Mathews Correlation Coefficient (MCC) (61,62) to compare what variables most significantly impact a participant's ability to guess the actual state of the video correctly. MCC is typically used for classification models to test the classifier's performance. Here we are treating human participant subgroups as classifiers and are measuring their performance with MCC. MCC takes the participant subgroup's guesses and the actual answers and breaks them up into the following categories: number of true positives (T.P.), number of true negatives (T.N.), number of false positives (F.P.), and number of false negatives (F.N.). The MCC metric ranges from -1 to 1, where 1 indicates total agreement between participant guess about the video and the actual state of the video, -1 indicates complete disagreement between participant guess about the video and the actual state of the video, and 0 indicates something similar to a random guess. To calculate the MCC metric for our human classifiers, we then use the following formula: MCC is considered a more balanced statistical measure than an F1, precision, or recall score because it is symmetric, meaning no class (e.g., TP, TN, FP, FN) is more important than another.
To compare MCC scores, we bootstrap samples from pairs of confusions matrices and compare their MCC scores. This process generates 10,000 bootstrapped samples of differences in correlation coefficients. We then simply compare the null hypothesis (difference equal to zero) to the bootstrapped distribution to measure the significance of biases and get a confidence interval on their strength.
Accuracy Rate: The performance metric we use to measure participant accuracy is the ratio of the correct guesses to the entire pool of guesses (seen in the equation below). Accuracy = (T P + T N ) (T P + F P + F N + T N )

ACKNOWLEDGMENTS.
Institutional Review Board Approval: The survey in this project is CHRBSS (Behavioral) STUDY00001786, Approved by the University of Vermont I.R.B. on 12/6/2021. The authors would like to thank Anne Marie Stupinski, Nana Nimako, Austin Block, and Alex Friedrichsen for their feedback on early drafts as well as Jean-Gabriel Young for comments on our analysis. This work is supported by Google Open Source under the Open-Source Complex Ecosystems And Networks (OCEAN) project, and MassMutual under the MassMutual Center of Excellence in Complex Systems and Data Science. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the aforementioned financial supporters.