The phenomenon of Russo-Ukrainian War memes has attracted the attention of media, researchers, and digital archivists since the beginning of the full-scale invasion in February 2022 (Rakityanskaya, 2025). These memes are characterized by their abundance, varied focus on multiple aspects of the war, and high degree of ephemerality. Because of the sheer number of memes and the fact that they react to rapidly changing developments both in the war and the public discourse about it, their meaning quickly fades. Moreover, the specific cultural context (pivotal to an understanding of Russo-Ukrainian War memes) is quite complex, multi-layered, and sometimes exclusive, which makes some of its components inaccessible even to their immediate consumers. An understanding of the humor of these memes is crucial for more than just entertainment purposes. More importantly, it is a condition sine qua non for preserving the meme’s integrity as a communication unit in which the meaning emerges as a result of the processing of both explicit properties (image and text) and the implicit message, often rooted in contextual references. In this study, we focus on finding a solution for preserving the meaning of memes from the standpoint of digital archiving by testing the use of a generative artificial intelligence chatbot for interpreting them in correlation with the chatbot’s ability to process contextual information.

The term ‘meme’ was coined by Richard Dawkins in 1976 in his application of gene theory to the evolution of human culture. Dawkins (1976: 206) used the term to describe ‘a unit of cultural transmission,’ that propagates itself ‘by leaping from brain to brain’ through imitation. L. Shifman (2014: 41) developed the concept of internet memes, pointing out their dynamic nature as ‘a group of digital items’ that are shared and updated by many Internet users.

When discussing the communicative aspect of memes, it is crucial to acknowledge that, as any genre of (multimodal) humorous text, they assume a prepared audience familiar with the many layers of relevant context (Wiggins, 2019; Milner, 2016). Many scholars underscore the essential role of cultural background in the perception of any images (Boylan, 2020) and even more so, for understanding political memes (Anderau and Barbarrusa, 2024; Lynch, 2022; Kirner-Ludwig, 2022).

When it comes to memes that have emerged around a specific event in a non-English language environment, comprehending them requires an even more complex form of background knowledge, as they combine two types of context: the general context inherent to international meme-making practices and the local context rooted in the regional culture. A. Denisova (2019: 54–68) points out the need to understand the local media context, which includes contemporary history, national identity, political environment, media environment, and media audience. L. Laineste and P. Voolaid (2016: 26) in their case study of Estonian memes dissect their intertextuality, which assumes the local ‘cultural memory’ of the Estonian-language community (including Soviet-era cultural practices), as well as familiarity with Russian- and English-language culture.

The situation is further complicated by the exclusive nature of online humor in general. In the multidimensional world of information, understanding memes is dependent not only on knowledge of factual content and familiarity with both general and regional cultural references but also on access to subcultural references specific to certain social networks and online communities. These communities create and share jokes and memes that are understood within the group but impenetrable to outsiders (Milner, 2016; Phillips, 2015; Gal, Shifman, and Kampf, 2016).

The notion of context becomes critical when viewed in relation to the problem of the memes’ ‘afterlife’ (a term coined by Nahon and Hemsley (2013: 124–142) to describe a stage in the existence of any viral piece of internet communication when it is forgotten and no longer communicates information). The digital ‘afterlife’ of memes occurs when all or most of the semiotic strings that link the meme to social, political, and technological context disintegrate due to their loss of relevance and the selectiveness of human memory. No longer linked to its context, a meme ceases to exist as a multimodal and multitextual unit, loses its humor, and becomes a meaningless digital image.

The task of archival preservation of internet memes entails extracting them from the original semi-closed online environments (in which memes might be available physically, but not necessarily intellectually) and making them intellectually accessible to virtually anyone. This means that, in addition to producing regular metadata describing the visible aspects of a meme and its general subject, archivists must also preserve its meaning and unique humor, a daunting task that goes beyond traditional archiving practice. In the words of García López and Martínez Cardama (2020: 896), ‘memes cannot be preserved as isolated digital objects devoid of the context that affords them full meaning.’ This task is well understood by the administrators of Know Your Meme, a ‘cyber warehouse’ [Serb. сајбер складиште] of mostly English-language memes, as pointed out by Knežević (2023).

Memes that form part of public discourse during a specific event (like the Russo-Ukrainian War) are important historical documents, and they must be preserved as primary sources. Details of day-to-day combat and civilian experience, viral words and concepts fill social media in some cases only for a few days and then disappear from collective memory, but remain encoded in memes. However, without their context, these memes can appear completely opaque (as demonstrated by Rossi and Bondarenko, 2024) or be interpreted in a way that is directly opposite to their original meaning, even by those internet users who share the political views of the meme creators. One example is the meme in Figure 1. Its earliest known version was published on Reddit on February 27, 2022 (Busdriver242, 2022). The meme references a video published as early as February 25, 2022 (the second day of the full-scale invasion). In the video (The Guardian, 2022) a Ukrainian woman tells Russian occupant soldiers to put sunflower seeds in their pockets, ‘so at least sunflowers will grow when [your bodies] are lying here’ (translation my own). Thereafter, in the semiotic system of Ukrainian memes, an image of a field full of sunflowers has stood for ‘dead Russian invaders.’ The memetic concept of sunflowers growing from the graves of Russian invaders is an example of context that is essential for understanding and properly categorizing the meme. In the absence of that context, the meme is occasionally interpreted in a directly opposite way, as supportive of the Russian propaganda narrative, suggesting that Russian troops bring peace to Ukraine: they enter bearing arms and leave it flourishing.1

Given the vital importance of preserving the meaning of memes in meme archiving, particularly in light of the vast number of memes produced around the Russo-Ukrainian War, we are particularly interested in exploring the potential of artificial intelligence (AI) for processing them. Fortunately, in recent years, many researchers have turned their attention to the application of AI in the humanities and social studies (Thapa et al., 2025). Significant progress has been made in research on using AI to recognize stance (particularly hate: Alam et al., 2024; Hossain et al., 2024, Karim et al., 2023), offensive content (Sharma et al., 2020), humor (Wang, 2023) and its specific forms (like sarcasm: Kumari et al., 2024; Cai, Cai, and Wan, 2019) in social media posts and memes. Of particular interest are studies conducted on non-English material, like Telugu (Bellamkonda, Lohakare, and Patel, 2022) or Spanish (Chiruzzo et al., 2021). Equally relevant are the studies of the ability of different AI tools to recognize objects in images and to produce metadata for the images (Thammastitkul, 2023). L. Soriano-Gonzalez and J. Belda-Medina (2024) undertook an impressive experiment exploring the use of different Large Language Models (LLMs) to interpret memes from the standpoint of pragmatics, which involved not only processing the explicit elements of the memes, but also understanding their implicit references.

While scholars overall find the application of AI in the interpretation of memes a beneficial and promising technology, many note its lack of cultural context as a significant drawback (Soriano-Gonzalez and Belda-Medina, 2024; Wang, 2023). V. Prabhakaran, R. Qadri, and B. Hutchinson (2022) point out ‘cultural incongruencies’ resulting from AI systems being developed in a small number of countries and trained on data limited to certain dominant cultures. Our study contributes to the diversification of research into the applications of AI in the humanities, as it is concerned with memes produced in connection with a specific contemporary historical event (the Russo-Ukrainian War), taking place in a specific geographical area (Ukraine and neighboring countries).

This study employs as its theoretical framework J. Suls’ incongruity resolution (IR) theory and F. Yus’s incongruity patterns taxonomy. Suls’ (1972: 81–100) IR theory describes the process of humor appreciation in verbal jokes and cartoons as a sequence of two stages: 1. the ‘joke recipient’ encounters an incongruity (which in the case of a cartoon means that the caption ‘disconfirms’ the image in some way); 2. the recipient finds a ‘cognitive rule’ that reconciles the incongruity. F. Yus (2017: 105) further developed the IR theory by discovering two basic patterns of incongruity in jokes: frame-based (when a joke clashes with ‘the hearer’s construction of an appropriate mental situation (frame, schema, script, etc.)’) and discourse-centered (when a semantic conflict occurs in processing ‘the verbal content of the joke’, producing word-play jokes). Later, in his study of meme-specific humor perception, Yus (2021: 137) introduced a third type of incongruity, discourse-image based, which ‘involves an inferential clash when inferring meanings that need a convergence between the partial meanings of the text and the image’.

We propose a fourth type of incongruity in memes: frame-image based incongruity, where the image itself creates an inferential clash with the perceived mental frame. Yus did not use this term, although he described an IR pattern in memes where an image plays an essential role in combination with frame-based incongruity. Not surprisingly, the researcher found a negligible percentage of such memes in his sample, which contained only memes constructed as a combination of image and text. Memes that have no text were not included in the sample.

The meme in Figure 2 is an example of a frame-image incongruity meme. The meme is composed of two photos featuring a headshot of a man and a meme macro ‘monkey with lips’ (Meme-arsenal, 2025). In the total absence of captions, however, the meme lends itself to a certain level of comic interpretation even without context, because it offers an obvious visual incongruity: a man reacting to having a conversation with a monkey. On this very basic level, the resolution comes from the idea that a man and a monkey cannot have a meaningful conversation. However, this meme was created in the context of the Russo-Ukrainian War, and its actual meaning is more specific. The meme is a response to the interview with Vladimir Putin conducted by the American journalist Tucker Carlson that aired online on February 8, 2024. The photograph of the man is a still of Carlson during this interview. Putin, however, contrary to the viewer’s expectations, is replaced by a monkey, which creates the meme’s incongruity that is resolved by mentally equating Putin’s pompous rants with the unintelligible vocalizations of a monkey. Thus, this frame-image incongruity meme requires knowledge of the context to fully resolve the incongruity created by the combination of the image and the frame.

The study was conducted using ChatGPT 4o, a generative AI chatbot with a proven capability for understanding humor (Soriano-Gonzalez and Belda-Medina, 2024) and processing Ukrainian language texts (Syromiatnikov, Ruvinskaya, and Troynina, 2024). We selected a sample of 120 memes from the SUCHO Meme Wall dataset.3 The guiding principle for the selection was a balanced representation of basic structural elements and publication dates. The resulting sample included 58 memes built on contextual images and 62 using non-contextual ones. Forty memes were published in 2022, 35 in 2023, 37 in 2024, and 8 in 2025 (the number reflects the share of 2025 memes at the time of the testing in the spring of that year).

For each meme, the researcher manually assigned an incongruity pattern and a CS level.4 The chatbot was introduced to the theory of incongruity patterns through the text of Incongruity-Resolution Humorous Strategies in Image Macro Memes (Yus, 2021) and the researcher’s own presentation of frame-image-based incongruity. The chatbot was then subjected to a series of tests aimed at evaluating its performance in two major testing categories:

The results of the chatbot’s performance in each of the seven tests were evaluated for accuracy on a scale of 1–3, where 1 was assigned to wrong answers, 2 to partially correct, and 3 to correct answers (including no answer if no data were available in the meme). Average scores in each category were then studied in correlation with CS level and the year the meme was posted.

The following standard prompt was used for the test:

Please analyze this meme related to the Russo-Ukrainian War and provide the following information:

We will now summarize ChatGPT’s overall performance over the course of our study and draw some conclusions.

In the course of our study, we asked ChatGPT to identify memes’ structural elements and write a narrative description of the comic effect. An average score of all seven testing categories was calculated to obtain a combined rating of the chatbot’s performance analyzing each meme (Figure 18). Overall, we observe a decline in the rating towards the higher CS end of the spectrum, which confirms our general hypothesis that memes with a higher context concentration would present more difficulty for analysis. We also see the most balanced performance in that segment. It appears that cumulative performance follows a specific pattern in all three segments of the CS spectrum: the lowest score was assigned to analysis of memes built only on a visual template, with no MI or MWP. It appears that the presence of an MI and an MWP improves Chat’s performance even if it struggles with contextual image template recognition.

The CS patterns that ChatGPT handled with the most accuracy were NCI + MWP (level 3 on our CS scale) and NCI + CI + MWP (level 6). The lowest performance was recorded for the memes based on a combination of NCI +CI (level 4) and CI alone (level 7).

When average performance scores in each category are compared, the data shows that ChatGPT performed best in identifying the two tangible context-based elements of the meme: persons featured (average score 2.78 out of 3) and visual templates used (2.62, Figure 19). Both of these elements are often presented explicitly and require primarily visual processing. More abstract elements were identified with greater difficulty, ranging from events or news items referred to in a meme (2.32) to viral words/phrases used in a meme (1.74). The verbal templates emerged as the most challenging element for ChatGPT to analyze. In the next stage of this research, it would be reasonable to attempt to improve the Chatbot’s performance by presenting it with sets of verbal and cultural references. The present study was designed to test the Chatbot’s raw ability to identify them.

We also compared the overall performance rating across the four incongruity patterns: frame, discourse, image-frame, or discourse-image based (coded, respectively, FB, DB, FIB, and DIB). The results (Figure 20) show that frame-based memes are the easiest for Chat to interpret (average score 2.39), and discourse-based ones are the most difficult (2.05). It appears that adding an image as an incongruity-forming element improves the bot’s understanding of both the discourse-based and the frame-based memes’ humor. This is consistent with the Chatbot’s demonstrated overall difficulty in interpreting verbal references.

We also investigated a possible correlation between ChatGPT’s performance in identifying specific contextual elements of the meme structure and content and the accuracy of the comic effect narrative (a prototype for a descriptive metadata element of meme archiving). We discovered that in our sample, this correlation is, in fact, direct for most variables: the more accurately the chatbot identifies specific structural elements, the better the narrative it writes, with two exceptions (Figure 21). There is a slight dip in identifying the visual template for the narrative score 2, which then rises again to become the chatbot’s second most successful skill (behind person identification). Strikingly, the viral word/phrase identification score falls dramatically from an average of 1.76 for partially correct narratives to 1.64 without affecting entirely correct narratives.

Finally, assessing ChatGPT’s ability to analyze retrospective information accurately was a very important area of testing. Positive results could strongly support using AI to generate archival metadata for memes and other born-digital multimodal and multi-textual documents, if it is demonstrated that, unlike the human brain, artificial intelligence retains more rare and obscure information over time. Our test results indicate a generally consistent performance from 2022 to 2025, with minor increases in 2023 and 2024 (Figure 22). However, as previously noted, performance on specific structural elements like memetic concept or event identification declined in 2024. Whether these results reflect the development trajectory of ChatGPT itself (notably launched in 2022, coinciding with the Russian invasion and the earliest year of our testing materials: Marr, 2023), showing an uneven performance curve (Chen, Zaharia, and Zou, 2024), the influence of the Ukrainian AI training dataset’s development, or the dynamics of the broader online media coverage of the Russo-Ukrainian War in 2023 (Statista, 2025), remains a question for future research.

The results of the study show clearly that there is a direct correlation between ChatGPT’s ability to interpret memes and its access to contextual information. In general, the study revealed that interpreting verbal humor (extracting information from memetic words and phrases and analyzing the structure of discourse-based memes) was the most challenging task for ChatGPT, while recognizing persons mentioned either explicitly or implicitly was the easiest. We also discovered that memes with a more complex template structure (that includes not only a memetic image, but also a memetic concept or a verbal meme) are easier for the Chatbot to interpret than simpler memes based on an image template alone.

We strongly believe that internet memes must be archived thoughtfully and with meaningful metadata that describes both explicit and implicit elements. Currently, generative AI is not capable of creating detailed metadata for two-dimensional internet memes without human help. It is important to remember that accuracy standards for archival metadata are very high, and although we used a three-level assessment system, only the correct results (score 3) are practically usable. All other results will distort a meme’s meaning and effectively end the meme’s life.

These conclusions should not be discouraging. We firmly believe that generative AI will be able to play a key role in archival preservation of digital ephemera in the future. It is important to remember that AI technology is evolving quickly and therefore, the results of this study will ultimately become only a snapshot of a specific moment in AI’s history.