Jan Sawicki

(1) Background: Large language models (LLMs) are increasingly used to generate morally distinct non-player characters in interactive fiction and tabletop role-playing games. However, prior work shows that reinforcement learning from human feedback (RLHF) safety training suppresses morally extreme expression. Hence, whether models can reliably generate and evaluate speech with a fixed moral–ethical alignment, within an assigned fictional persona, remains an open question. (2) Methods: To explore this issue, eight generator LLMs each produced three-turn role-play conversations for 37 Dungeons & Dragons 5th Edition deities spanning all nine cells of the 3 × 3 moral–ethical alignment grid (888 conversations by design, 885 obtained after one generator’s midexperiment deprecation). Next, eight evaluator LLMs classified each conversation on both alignment axes using structured output evaluation (6951 evaluation records), assessed across 17 analysis metrics. (3) Results: The obtained results can be summarised as follows. Moral-axis accuracy (Good/Neutral/Evil) exceeded ethical-axis accuracy (Lawful/Neutral/Chaotic) by approximately 10 percentage points on average across all 64 generator–evaluator pairs, with the pair-level gap ranging up to 32 points and reversing sign for one generator (nemotron-120b). Moreover, the alignment-cell hardness ranged 27.6-fold, from Lawful Good (0.910) to Neutral Neutral (0.033). Furthermore, a strong good-ward moral drift, consistent with RLHF suppression effects reported in prior work, was observed for nemotron-120b (0.931 of Evil-target outputs classified as Good) and was near-absent for grok-4.1-fast (0.024). Finally, deity wiki prominence did not predict per-deity accuracy after controlling for alignment cell (partial ρ < 0.20, p > 0.20). (4) Conclusions: In summary, good-ward drift, consistent with RLHF suppression of morally extreme expression, turned out to be graded and model-specific. Moreover, iconic characters override it (Cyric, Chaotic Evil: 0.947, nearly matching the top Lawful Good score). Finally, alignment-cell identity is the dominant accuracy predictor, not the deity familiarity, within the role-played persona setting studied here.

Network dismantling—the targeted removal of nodes to degrade large-scale connectivity—plays a central role in resilience analysis, epidemic containment, and systemic-risk mitigation. Recent work shows that dismantling performance depends strongly on mesoscale modular structure, suggesting that community-aware strategies may offer advantages over classical centrality-based heuristics. In this work, we perform a large-scale, systematic evaluation of dismantling strategies and introduce gateways as a new mesoscale dismantling concept. While similar experiments exist using degree- and betweenness-based dismantling strategies, we check a new strategy based on gateways, which capture asymmetric entry points into communities and generalize the notion of inter-community connectors. Furthermore, we process a massive dataset of 568,584 LFR benchmark graphs, covering a wide range of degree distributions, community sizes, and mixing parameters. For evaluation, we use both extrinsic (ARI, NMI, FMI, VI) and intrinsic (Modularity, Coverage, Performance, Average Conductance, Average Internal Density) metrics. We find that across parameter regimes and evaluation metrics, classical strategies (degree, betweenness, community connections) and gateway-based dismantling exhibit broadly similar performance. Our results also corroborate recent findings that dismantling effectiveness is robust to the specific partitioning algorithm and that inter-community connectivity plays a dominant role in global fragmentation. The evaluation provides large-scale evidence that gateway-aware dismantling captures an operationally relevant mesoscale mechanism as good as previous approaches and motivates further empirical studies on real networks and cost-aware settings.

This study explores graph-based methods to model and analyze the semantic interplay between editorial taxonomies and user-generated folksonomies on the PornHub platform, using a dataset of over 97,000 videos (2015–2024). We construct and examine a graph of user-assigned tags and platform-defined categories, applying the Leiden community detection algorithm to uncover latent semantic groupings. To enrich the graph structure, we embed textual metadata using state-of-the-art language models (Qwen3-Embedding-4B and all-MiniLM-L6-v2), enabling the integration of natural language processing within graph-based learning. Our analysis reveals that folksonomies partially align with taxonomies through synonymous structures but also diverge by capturing nuanced attributes such as body features and aesthetic styles. These asymmetries highlight how folksonomies introduce higher-resolution semantic layers absent from fixed-category systems. By fusing graph mining, NLP-driven embeddings, and network-based clustering, this work contributes a hybrid methodology for semantic knowledge extraction in large-scale, user-generated content. It offers implications for graph-based recommendation, content moderation, and metadata enrichment—demonstrating the utility of graph-centric AI techniques in real-world multimedia data settings.

Meme analysis, particularly identifying top meme templates, is crucial for understanding digital culture, communication trends, and the spread of online humor, as memes serve as units of cultural transmission that shape public discourse. Tracking popular templates enables researchers to examine their role in social engagement, ideological framing, and viral dynamics within digital ecosystems. This study explored the viral nature of memes by analyzing a large dataset of over 1.5 million meme submissions from Reddit’s r/memes subreddit, spanning from January 2021 to July 2024. The focus was on uncovering the most popular meme templates by applying advanced image processing techniques. Apart from building an overall understanding of the memesphere, the main contribution was a selection of top meme templates providing a recipe for the best meme template for the meme creators (memesters). Using Vision Transformer (ViT) models, visual features of memes were analyzed without the influence of text, and memes were grouped into 1000 clusters that represented distinct templates. By combining image captioning and keyword extraction methods, key characteristics of the templates were identified, highlighting those with the most visual consistency. A deeper examination of the most popular memes revealed that factors like timing, cultural relevance, and references to current events played a significant role in their virality. Although user identity had limited influence on meme success, a closer look at contributors revealed an interesting pattern of a bot account and two prominent users. Ultimately, the study pinpointed the ten most popular meme templates, many of which were based on pop culture, offering insights into what makes a meme likely to go viral in today’s digital culture.

With a specific focus on the /r/MensRights subreddit, the linguistic landscape of the “manosphere” is examined to identify and analyze patterns of sexist rhetoric. The “manosphere” on Reddit refers to a collection of communities that discuss perceived men’s issues, often characterized by themes of men’s rights activism and anti-feminism. Employing natural language processing techniques and transformer models for sentiment analysis, the research uncovers prevalent language structures indicative of gender bias within this community. Utilizing n-gram analysis, the aim is to provide insights into the language dynamics contributing to the perpetuation of gender-based discourse. By examining the framing of gender issues and opinionated language, the research aims to shed light on how members of the men’s rights community discuss gender issues, including attitudes towards feminism, perceptions of male-related issues, and overall perspective surrounding men and women. This study strives to contribute to a broader understanding of online communities and their influence on shaping attitudes towards gender.

Multiple research directions have been proposed to study the information structure of Reddit. One of them is to model inter-subreddit relations but modeling user interactions in the form of a graph. Building upon prior work centered on political subreddits using pre-2020 data, we expand this investigation to include a more extensive dataset spanning 2022 and encompassing diverse topic areas. Employing NLP techniques such as text embeddings, we model subreddit content directly and construct a subreddit graph network based on cosine similarity. Community detection using the Louvain method reveals distinct subreddits and allows the analysis of inter-community connections via previous works’ concepts of “bridges” and “gateways”. Surprisingly, our findings indicate redundancy between bridges and gateways in the utilized dataset. Therefore, we introduce a new concept, “highways”. Highways, representing the most traversed paths between subreddits, unveil insights not captured by previous analyses, underscoring the significance of novel conceptual frameworks in uncovering latent knowledge within Reddit’s online community structures.

Reddit is the largest topically structured social network. Existing literature, reporting results of Reddit-related research, considers different phenomena, from social and political studies to recommender systems. The most common techniques used in these works, include natural language processing, e.g., named entity recognition, as well as graph networks representing online social networks. However, large-scale studies that take into account Reddit’s unique structure are scarce. In this contribution, similarity between subreddits is explored. Specifically, subreddit posts (from 3189 subreddits, spanning the year 2022) are processed using NER to build graph networks which are further mined for relations between subreddits. The evaluation of obtained results follows the state-of-the-art approaches used for a similar problem, i.e., recommender system metrics, and applies recall and AUC. Overall, the use of Reddit crossposts discloses previously unknown relations between subreddits. Interestingly, the proposed approach may allow for researchers to better connect their study topics with particular subreddits and shows promise for subreddit similarity mining.

As the largest open social medium on the Internet, Reddit is widely studied in the scientific literature. Due to its structured form and division into topical subfora (subreddits), conducted research often concerns connections and interactions between users and/or whole, subreddit-structure-based communities. Overall, the relations between communities are most often studied by applying graph networks, with various creation algorithms. In this work, a novel approach is proposed to build and understand the structure of Reddit. It is based on crossposts—posts that appeared on one subreddit and then were crossposted to another. After capturing one year of crossposts, a directed weighted graph network, using seven million posts from over 10,000 of the most popular subreddits, has been created. Using graph network algorithms, its characteristics are captured and compared to similar studies. We identify the information “sinks” and “sources”—the most active crossposting subreddits. Moreover, we obtained graph network metrics: the degree (modeled with the Power Law), clustering, community detection algorithms, and connected components structure network are compared to previous studies on Reddit network(s), yielding consistent, but also novel results. Finally, the relations between extensively studied subreddits (e.g., r/AITA, r/Parenting, r/politics) and new ones, which were not accounted for in previous research, opening new paths for data-driven studies, are summarized.

Nowadays, natural language processing (NLP) is one of the most popular areas of, broadly understood, artificial intelligence. Therefore, every day, new research contributions are posted, for instance, to the arXiv repository. Hence, it is rather difficult to capture the current “state of the field” and thus, to enter it. This brought the id-art NLP techniques to analyse the NLP-focused literature. As a result, (1) meta-level knowledge, concerning the current state of NLP has been captured, and (2) a guide to use of basic NLP tools is provided. It should be noted that all the tools and the dataset described in this contribution are publicly available. Furthermore, the originality of this review lies in its full automation. This allows easy reproducibility and continuation and updating of this research in the future as new researches emerge in the field of NLP.