Seeing is No Longer Believing: How AI-Powered Fakes Go Viral

Author: Denis Avetisyan

New research reveals that the combination of misleading text and AI-generated images on Reddit creates exceptionally viral content, rapidly spreading misinformation.

The prevalence of generated content and misinformation within information cascades proves to be a surprisingly potent driver of virality-far exceeding the influence of inherent image or textual characteristics-and fosters propagation across community lines, creating significant challenges for content moderation on platforms like Reddit.

A cascade analysis demonstrates that multimodal disinformation – combining text and AI-generated imagery – propagates further and faster than either modality alone, necessitating new detection strategies.

While the spread of misinformation online is well-documented, understanding how artificially generated imagery amplifies its reach remains a critical gap. This research, titled ‘When GenAI Meets Fake News: Understanding Image Cascade Dynamics on Reddit’, presents a large-scale analysis of how AI-generated images and misleading text combine to drive viral content across diverse online communities. Our findings demonstrate that posts leveraging both visual and textual misinformation exhibit significantly faster and broader propagation than either element alone. How can platforms effectively detect and mitigate these multimodal cascades to safeguard online information ecosystems?

The Velocity of Belief: How Networks Amplify Untruth

The architecture of modern social media, particularly platforms like Reddit, has fundamentally altered the velocity of information exchange. While enabling unprecedented opportunities for rapid dissemination of news and ideas, this accelerated pace concurrently creates fertile ground for misinformation to flourish. Content, both accurate and misleading, can achieve widespread visibility in a matter of hours, often bypassing traditional gatekeepers of journalistic integrity. The platform’s emphasis on user-generated content and algorithmic amplification, designed to maximize engagement, prioritizes speed and reach over verification, meaning unsubstantiated claims and fabricated narratives can quickly gain traction and become widely perceived as factual. This presents a significant challenge, as the sheer volume of information circulating online overwhelms individual fact-checking capabilities and necessitates innovative approaches to combat the spread of false or misleading content.

The swift and widespread dissemination of information, often termed virality, is no longer simply a phenomenon of popular culture but a critical factor in the propagation of misinformation. Research indicates that content achieving viral status bypasses traditional gatekeepers of accuracy, reaching vast audiences before fact-checking mechanisms can effectively intervene. This accelerated spread isn’t solely about speed; emotional resonance and novel framing significantly amplify a piece of content’s reach. Consequently, a detailed understanding of the dynamics driving virality – encompassing network structures, algorithmic amplification, and psychological triggers – is paramount. By dissecting how information gains rapid traction, strategies can be developed to mitigate the harmful effects of false narratives and promote the circulation of verified, reliable content. The challenge lies not in halting the spread of information, but in influencing what spreads and ensuring a more informed public discourse.

The propagation of information in the digital age isn’t solely driven by written narratives; visual elements are equally, if not more, influential in achieving virality. Studies reveal that images and videos often garner significantly higher engagement rates than text-based posts, rapidly accelerating content dissemination across networks. This dual influence demands a comprehensive analytical approach; understanding how visual cues – composition, color, emotional resonance – interact with textual messaging to capture attention is paramount. Researchers are increasingly employing multimodal analysis, combining natural language processing with computer vision techniques to dissect the synergistic effects of text and imagery. Consequently, effective strategies to counter misinformation require addressing both the linguistic and visual dimensions of viral content, recognizing that a message’s impact isn’t solely derived from its words, but also from its visual presentation and the emotional response it elicits.

Mapping the Cascade: Tracing the Flow of Influence

Content dissemination rarely occurs through immediate, widespread exposure; instead, it progresses via diffusion cascades – sequential chains of reposts and shares initiated by an original piece of content. These cascades are structured networks where content spreads as individual users share it with their respective networks, creating branching pathways of exposure. The size and shape of these cascades vary significantly, influenced by factors such as network topology, user influence, and content characteristics. Analyzing these cascades requires identifying the initial seed of the content and tracing its propagation through successive shares, effectively mapping the network of users involved in its distribution. Understanding these diffusion patterns is critical for modeling and predicting content spread, as virality isn’t a singular event but rather the emergent property of these complex network dynamics.

Reconstructing content diffusion cascades relies on identifying instances of content propagation across different platforms and networks. This is commonly achieved through techniques such as tracking shared image URLs; when an image is reposted, the URL acts as a unique identifier linking the instances. Analyzing crossposting patterns-specifically, identifying when content originally posted on one platform appears on another-provides further evidence of cascade construction. These methods allow researchers to map the flow of information, determining the origin and progression of content as it spreads through various online communities and enabling the identification of influential nodes within the cascade.

Virality prediction at the cascade level demonstrates high accuracy, achieving an Area Under the Curve (AUC) of 0.998 when utilizing both content-based and diffusion features. This performance represents a significant improvement over models relying solely on diffusion characteristics, which yield an AUC of 0.995, and those based exclusively on content features, which achieve an AUC of 0.957. The substantial increase in predictive power underscores the importance of integrating both the characteristics of the content itself and the patterns of its spread to accurately forecast virality.

Forecasting the Signal: Models for Anticipating Spread

Machine learning models, specifically Random Forest and LightGBM, demonstrate capability in predicting content virality by analyzing inherent content features. These models are trained on datasets comprising various content attributes – such as image characteristics, text length, and posting time – correlated with observed virality metrics like shares, likes, and comments. Feature importance analysis within these models identifies which content characteristics are most predictive of viral spread. The predictive performance is evaluated using metrics like precision, recall, and area under the receiver operating characteristic curve (AUC-ROC), with models achieving statistically significant results in forecasting virality potential prior to widespread dissemination.

The Virality Attention Index (VAI) is a metric designed to predict the potential for content to achieve viral spread, operating at the individual post level. Utilizing exclusively visual features extracted from content, the VAI has demonstrated an 87% accuracy rate in predicting post-level virality. This early-stage assessment is achieved through a trained model that analyzes visual elements to estimate the likelihood of broad dissemination, offering a quantitative measure prior to significant user engagement. The VAI provides a data-driven approach to understanding and anticipating content performance based solely on its visual characteristics.

Interpretable AI methods, such as SHAP (SHapley Additive exPlanations) values, are essential for deconstructing the factors influencing machine learning model predictions regarding content virality. SHAP values assign each feature an importance weight for a particular prediction, quantifying its contribution to the model’s output; this allows for the identification of features that positively or negatively correlate with predicted virality. Unlike “black box” models, these methods provide transparency, enabling analysts to understand why a model predicts a specific outcome for a given piece of content. This granular understanding is crucial for actionable insights; for example, identifying that high visual complexity consistently increases predicted virality allows content creators to strategically adjust their content accordingly, and verifying model behavior builds trust in its recommendations.

The Mirage of Authenticity: Deepfakes and the Erosion of Trust

The proliferation of generative artificial intelligence has unlocked unprecedented capabilities in content creation, but simultaneously introduced a significant challenge to information integrity. These technologies, initially celebrated for artistic and creative potential, are now increasingly leveraged to fabricate highly realistic, yet entirely false, content – commonly known as deepfakes. This synthetic media, encompassing manipulated videos, audio recordings, and images, can convincingly portray events or statements that never occurred, effectively blurring the lines between reality and fabrication. The ease with which such content can be generated and disseminated through social media platforms amplifies the potential for widespread misinformation campaigns, impacting public opinion, political discourse, and even individual reputations. Consequently, understanding the mechanics of these technologies and developing robust detection methods are crucial steps in mitigating the risks posed by AI-generated disinformation.

The escalating prevalence of synthetic media demands robust detection methods, and specialized machine learning models are proving critical in this effort. Support Vector Machines (SVMs), a supervised learning technique, are frequently employed for distinguishing between authentic and artificially generated content due to their effectiveness in high-dimensional spaces. Training these classifiers, however, necessitates substantial datasets comprised of both real and synthetic examples; resources like AMMeBa and Fakeddit are specifically designed for this purpose. AMMeBa provides a platform for generating manipulated media, enabling researchers to create controlled datasets, while Fakeddit offers a collection of Reddit posts intentionally crafted to resemble misinformation, allowing for the development of models that can identify subtle cues indicative of fabrication. These datasets empower researchers to refine algorithms, improving their capacity to discern increasingly realistic deepfakes and synthetic content with greater accuracy.

Recent research indicates that the most potent form of online disinformation isn’t solely fabricated news or entirely AI-generated imagery, but a dangerous combination of both. Analyses reveal that “mixed-flag” content – posts incorporating both misinformation and synthetic media – demonstrates significantly greater propagation dynamics than either component in isolation. Specifically, these hybrid posts achieve an average cascade size of 26.96, meaning they are shared to approximately 27 unique users, and reach a depth of 25.96, indicating a substantial chain of re-shares. Moreover, the structural virality – a measure of how effectively the content branches and spreads – reaches 7.14, substantially exceeding the values observed with purely fabricated narratives or AI-generated content alone. This suggests that the combination leverages the credibility of established narratives with the novelty and visual impact of synthetic media, creating a particularly virulent form of online deception.

Cultivating Resilience: Safeguarding the Information Ecosystem

The proliferation of easily manipulated visual content presents a significant threat to the integrity of the information ecosystem, necessitating robust safeguards like ‘Safe Search’ filters. These filters function by employing a combination of algorithmic analysis and human review to identify and suppress images and videos containing harmful or misleading information, ranging from explicit content to fabricated news. Current implementations leverage techniques such as object recognition, scene understanding, and semantic analysis to assess visual content against predefined criteria, effectively reducing user exposure to inappropriate material. While not foolproof – adversarial attacks and the evolving nature of disinformation constantly challenge their effectiveness – ongoing refinement of these filters, coupled with user reporting mechanisms, remains a crucial step towards mitigating the spread of harmful visuals and fostering a more responsible online environment.

The spread of information online isn’t random; it unfolds as a ‘diffusion cascade’ – a chain of re-shares and engagements. Recent research demonstrates that analyzing the structural complexity of these cascades can expose deliberate manipulation. By employing graph theory metrics, such as the Wiener Index – which quantifies the total distance between all node pairs in a network – investigators can identify patterns indicative of coordinated inauthentic behavior. A higher Wiener Index suggests a more decentralized, organic spread, while a lower index, coupled with other anomalies, may signal a tightly controlled, artificial amplification campaign. This approach moves beyond simply detecting what is spreading to understanding how it spreads, providing crucial insights into the origins and intent behind information flows and bolstering defenses against malicious actors seeking to distort the information ecosystem.

Analysis of information spread reveals a concerning reality: purely fabricated misinformation maintains a substantial presence within the digital landscape, registering a mean score of 35,567 in recent assessments. This persistent circulation underscores the significant potential for harm, necessitating continued and focused investigation. Current research prioritizes improving the accuracy of virality prediction models – identifying content likely to spread rapidly – and refining misinformation detection techniques. These efforts are not merely academic exercises, but rather crucial components in building a more resilient information ecosystem capable of withstanding manipulation and promoting the reliable dissemination of knowledge. A proactive approach to understanding and mitigating the spread of false information is paramount to safeguarding public discourse and informed decision-making.

The study illuminates a predictable trajectory: systems, once connected, exhibit emergent behaviors beyond initial design. This research demonstrates how the confluence of textual misinformation and AI-generated imagery on Reddit doesn’t simply add to virality, but catalyzes it-a cascading effect where the whole exceeds the sum of its parts. As Tim Berners-Lee observed, “The Web is more a social creation than a technical one.” This holds true here; the platform’s architecture, combined with human susceptibility, allows manipulated content to propagate with alarming speed, showcasing how even well-intentioned systems are vulnerable to unforeseen consequences when scaled and populated by complex actors.

The Currents Shift

The study reveals, predictably, that falsehood gains velocity when adorned with convincing imagery. It is not a triumph of deception, but a demonstration of how little friction exists between belief and confirmation. The architectures built to detect such cascades – the multimodal filters, the veracity scores – will inevitably lag behind the ingenuity of those who seek to bypass them. Each layer of defense simply raises the stakes, incentivizing more sophisticated forgeries and more subtle manipulations. Technologies change, dependencies remain.

The focus on ‘detection’ feels, in retrospect, like treating a symptom rather than understanding the disease. The substrate is not information, but trust – and trust, once eroded, is a difficult thing to rebuild. Future work will undoubtedly explore increasingly complex models of virality, but should also consider the sociological forces at play. The patterns observed are not merely algorithmic; they are reflections of human susceptibility, of our eagerness to find patterns, even where none exist.

One anticipates a proliferation of ‘synthetic commons’ – digital spaces saturated with indistinguishable realities. The question is not whether such falsehoods can be created, but whether anyone will truly care. Architecture isn’t structure – it’s a compromise frozen in time. The currents shift, and the maps become obsolete before they are even drawn.

Original article: https://arxiv.org/pdf/2512.04639.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/