Guided by our motivation to examine the prevalence, spreaders, and emotional reception of AI-generated political images on X, we formulate the following research questions (RQs):

What is the prevalence and concentration of AI-generated images on X?

What are the characteristics and sharing behaviors of AIGC superspreaders?

How do users respond to AI-generated images on X?

This study serves as an initial step toward understanding how generative AI technologies shape social media dynamics in political discussions. As one of the first investigations using real-world data to assess the prevalence of AIGC on social media, we reveal that approximately 12% of images within the online discourse on X related to the 2024 U.S. Presidential Election are AI-generated. Notably, a small fraction of users dominate AIGC dissemination, with around 10% of image spreaders account for 80% of the shared AI-generated images. We identify and characterize the behaviors of AIGC superspreaders, noting that they are more likely to have a right-leaning political orientation, subscribe to X Premium, and exhibit stronger bot-like behaviors. Furthermore, users tend to adopt a more positive tone and exhibit lower levels of toxicity in response to AI-generated images. We hope our findings pave the way for further research on the role of AIGC in social media, offering valuable insights for platform governance, policy-making, and public awareness of generative AI’s growing impact on online sociopolitical landscapes.

Generative AI has redefined the boundaries of visual content production. On one hand, it enables people to translate their imagination into highly customized and expressive visual content [2, 18]. On the other, AI-generated images may embed biases [45] originating from skewed training data [29] or maliciously crafted prompts. When disseminated on social media platforms, such biases can be reproduced or even amplified, potentially reinforcing harmful stereotypes, distorting public discourse, and exacerbating existing social inequalities. However, due to challenges in detecting AIGC and limited real-world datasets, only a few studies have explored its presence and impact on social media. Research has examined the prevalence and misuse of GAN-generated visuals for inauthentic activities [42, 50], the role of synthetic content like political memes in shaping discourse during elections [10, 38], the use of AI-generated images to gain profit on Facebook [16], and the broader influence of AIGC on platform dynamics, e.g., content creation and consumption patterns [49].

Despite these contributions, existing studies lack a comprehensive characterization of AI-generated images on social media. Many are constrained to a narrow set of generative models, such as GANs [42, 50], or depend on explicit hashtags to identify labeled AIGC [49], thereby excluding unlabeled content that may be misleading or harmful. Manual identification methods [10], while useful, are neither scalable nor consistently accurate. Together, these limitations impede a full understanding of the AIGC landscape on social media platforms.

Superspreaders, also referred to as supersharers, are users who disproportionately contribute to the spread of specific types of content on social media, such as low-credibility or fake news. They play a unique role in shaping online information ecosystems by generating content that garners substantial reach and engagement. Studies have consistently found that a small fraction of superspreaders accounts for the majority of misinformation shared online [15, 39]. For instance, during the 2016 U.S. Presidential Election, just 0.1% of Twitter users were responsible for nearly 80% of fake news shared, with similar patterns observed during the 2020 Election [5, 23, 25]. Superspreaders are typically identified using various metrics, such as k-core decomposition to assess centrality within the network, the sum of nearest neighbors’ degrees to evaluate local influence, and the h-index to measure the volume and reach of their shared content [15, 40]. Superspreaders are not limited to bots or automated accounts but frequently include politically active individuals and pundits with large followings [5, 24]. Recent work has also shown that X’s recommendation algorithm amplifies superspreaders like political commentators and partisan influencers [52].

While most studies have focused on superspreaders of misinformation, the rise of generative AI introduces new complexities and raises open questions. AIGC’s potential to mimic diverse styles and create realistic and complex content at scale raises concerns about its integration into existing misinformation networks [21]. Superspreaders of AIGC may exploit these affordances and automation capabilities to amplify their influence further. This study, therefore, builds on prior work by investigating AIGC superspreaders during the 2024 U.S. Presidential Election.

Understanding emotional reception of AI-generated images is both urgent and important. Visual content evokes stronger emotions than text [33] and can more powerfully influence attitudes [47], so it is essential to investigate how users perceive and engage with AI-generated visuals. Most prior work has relied on controlled experiments across diverse contexts, yielding mixed findings. In artwork evaluation, for instance, individuals report lower emotional engagement when they believe a piece is AI-generated [1], and human-created art consistently elicits stronger emotions than AI-created art [14]. When looking into specific emotions, AI-generated images have been shown to provoke more negative reactions [37]. Nevertheless, in the context of architectural design, AI images effectively convey joy but poorly transmit negative emotions [53].

However, these experimental settings do not reflect the rapid, emotionally charged, and often polarized environment of social media. Few studies have explored how AI-generated images are received in real-world social feeds, where they are encountered, shared, and interpreted under naturalistic conditions. Addressing this gap is critical for understanding the public impact of AI-generated images.

We leverage an existing dataset of tweets related to the 2024 U.S. Election [3]. The dataset is generated by querying targeted keywords related to political figures, events, and emerging issues of the Presidential Election to retrieve data effectively. In this study, we analyze data spanning a three-month period leading up to the election, from July 1, 2024, to September 30, 2024. The resulting dataset includes 2.5M images shared by 414K spreaders. Given that our analysis centers on characterizing the behavior of users sharing AI-generated content during the observation period, we first identify and quantify these AIGC spreaders. Users who share at least one AI-generated image are denoted as AI image spreaders (details on the detection of AIGC can be found in Section 3.2). This yields approximately 88K AI image spreaders.

Next, to address RQ2, we apply a filtering process to identify a subset of target users. To ensure that each user has sufficient data points to analyze their sharing behavior and received engagement, two criteria are applied. The first criterion sets a minimum threshold for the total number of tweets a user has posted during the observed time period. AI image spreaders who have posted fewer than five tweets with at least one image in each are excluded from the analysis. Second, an AI image spreader must have shared at least one retweeted post containing AIGC, i.e., h-index > 0 (details on the h-index are provided in Section 3.4). These criteria collectively ensure a robust and meaningful dataset for evaluating user activity and influence. Finally, we retrieve a subset of target users consisting of 12,898 AI image spreaders. Subsequent analyses are based on this pool of target users.

With the proliferation of AI-generated content on platforms like X, accurately detecting such images has become increasingly challenging. Traditional deep learning models often struggle to distinguish AI-generated images [6]. In contrast, recent work demonstrates that large language models (LLMs) such as GPT-4o, when guided by carefully crafted prompts, offer robust multimodal detection capabilities [7, 10, 11]. Drawing on these advances, we employ GPT-4o to identify AI-generated images in this study. Figure 1 presents an overview of our detection and validation pipeline.

We leverage GPT-4o to classify images as AI-generated or non-AI-generated. To evaluate its performance, following Epstein et al. [17], we construct a validation set of 2,400 images, evenly split between 1,200 AI-generated and 1,200 non-AI-generated samples. The non-AI images are randomly sampled from the LAION-400M dataset [43], while the AI-generated set comprises 400 DiffusionDB samples [48], 400 DALL·E images collected from the subreddit r/dalle², and 400 Midjourney images crawled from the Explore page³.

We extract image URLs from tweet metadata and send the following prompt to GPT-4o (temperature set to 0 to minimize variability):

Of the 2,400 images to be validated, the model returns responses for 2,367 and fails on 33 (often due to sensitive‐content filters), achieving an F1‐score of 0.96. To assess robustness, we employ two additional transformations to the same 2,400 images:

Rotation: We split the images into three equal groups, rotating them by 90°, 180°, or 270°. GPT-4o processes 2,380 images (20 failures) and reaches an F1‐score of 0.92.

Scaling: We resize half of the images to 200% and the other half to 50% of their original sizes. GPT-4o processes 2,366 images (34 failures) and achieves an F1‐score of 0.94.

These results demonstrate GPT-4o’s high accuracy and resilience to common image transformations. We then apply GPT-4o detection to our 2024 U.S. Election dataset using the OpenAI BatchAPI⁴ to optimize time and cost. We categorize each response as: (i) valid (“yes” or “no”), (ii) download failure due to content moderation, or (iii) inability to respond (e.g., “Sorry, I cannot answer this.”). Categories (ii) and (iii) are treated as invalid answers. Of the 2,462,132 images processed, approximately 90.5% receive valid responses. Examples of detected AI-generated images are shown in Appendix. All subsequent analyses focus on these valid detections and their associated users. The images and detection results are available at: https://github.com/angelayejinyi/AIGC-Election-2024.

Although GPT-4o performs well on our constructed validation set, its accuracy on real-world social media images remains uncertain due to the variability and complexity of online content. To assess robustness in the wild, we adopt an annotation workflow inspired by Ricker et al. [42] that introduces annotation guidelines and heuristic-based training for annotators, followed by validation of samples from model-labeled datasets. Following a similar approach, we randomly sample 1,000 images labeled as AI-generated and 1,000 labeled as non-AI-generated by GPT-4o from our large-scale dataset. We then provide annotators with a detailed guideline to validate model’s performance in the wild.

To facilitate consistent annotation, we provide annotators with a set of heuristic strategies: (i) side-by-side comparison, where annotators compare the current image to ground-truth-labeled examples, focusing on their visual patterns and framing; (ii) zoom-in review of visual cues, which encourages annotators to identify common generation-related artifacts such as unnatural textures and object distortions; and (iii) contextual reasoning, which prompts annotators to apply basic logics to check the plausibility of the scene. These techniques, which have proven effective in previous work  [42], are aimed at improving both accuracy and inter-coder reliability.

Beyond these heuristics, we integrate an additional technique inspired by  Qian et al. [41]: Google reverse image search. Their study has shown that introducing reverse image search into a digital media literacy intervention significantly increases users’ intention and ability to verify images. The underlying rationale is that this tool allows users to verify the origins and context of an image by searching with the image itself rather than a keyword. When an exact or near-duplicate match is found online, the accompanying metadata, such as early publication dates or credited painters or photographers, can serve as strong evidence that the image is non-AI-generated. By incorporating this step, we not only provide annotators with external validation cues but also align our workflow with proven strategies to enhance media discernment and validation robustness.

Following the annotation guidelines, each image is independently labeled by two different annotators. We first assess inter-coder reliability, obtaining a Cohen’s κ of 0.87, which indicates a high level of agreement. We then evaluate the model’s performance by calculating its misclassification rates, yielding a false positive rate (FPR) of 6.5% and a false negative rate (FNR) of 1.6%. These results confirm the robustness of our detector when applied to the large-scale dataset in real-world scenarios. While the error rates are not zero, they are sufficiently low to support the validity of the subsequent analysis.

Superspreaders are typically identified using influence metrics, including k-core centrality, the sum of nearest neighbors’ degrees, and the engagement driven by their original content [15, 40]. Here, we adopt the h-index, originally designed to measure scholarly impact [26], and later adapted for identifying superspreaders of low-credibility content on X [15]. In our scenario, the h-index for a user i is defined as the maximum value of h such that user i posted at least h tweets containing AIGC, each retweeted at least h times. This metric captures both the volume of AI-generated content shared and the engagement received by each post. A superspreader must meet both criteria: a high number of tweets containing AI-generated content and a significant volume of received retweets. This ensures that superspreaders are identified based on both their activity and the broader impact of their AI-generated content on the platform.

We characterize superspreaders of AI-generated content based on two key metrics: The AI score quantifies the proportion of AI-generated content in a user’s posts. The bot score evaluates the probability of an account being automated based on its historical tweet activity.

AI Score.