The paper presents a method for building a network map of the artworld using tweets from a manually selected set of exemplar actors and NLP techniques. It identifies significant artworld actors, even those without Twitter accounts, and groups them into communities via the Louvain method while assessing their authority with HITS centrality. The resulting network demonstrates that starting from a small set of exemplars can uncover new actors and yield data that would otherwise be difficult to gather.
The studying of opinion dynamics and its propagation within social networks is crucial for addressing a wide range of challenges, including political polarization, public health, and marketing strateg...
The paper proposes a Friedkin‑Johnsen based framework to identify influential users in online social networks and assess how they shape community opinion. By manipulating initial opinions in experiments, the authors show that top influencers can significantly shift overall community sentiment, and their influence extends beyond direct neighbors to second‑degree contacts. The framework is validated on a tweet dataset from the U.S. presidential election, illustrating the power of digital influencers to alter public opinion.
OpenAI banned likely China-origin accounts using AI to generate English social posts and Spanish-language articles.
The paper reviews how large language models (LLMs) are being used to model social networks, highlighting their ability to represent users, relationships, and interactions through natural language. It categorizes existing work into network generative models—split into selection‑based and interaction‑based approaches—and dynamic process models, which cover opinion dynamics, information diffusion, and rumor propagation. The survey also discusses the advantages of LLMs for realistic, context‑aware social behavior, while noting limitations such as social biases and prompt sensitivity, and outlines open research challenges and future directions.
The paper presents a method for automatically distinguishing scientists from non‑scientists on X/Twitter using user biographies and tweets. It reports classification performance up to an F1 score of 0.88 with Random Forests and 0.96 with a contrastively fine‑tuned DeBERTa model in an ensemble. Additionally, the authors release two labeled datasets of X users and their content for further research.
arXiv:2608. 05259v1 Announce Type: cross Abstract: Online social networks increasingly expose people to users who propagate discriminatory, hateful, and violent content.
OpenAI banned accounts using AI to generate posts criticizing a Taiwanese social media influencer and related US topics.
arXiv:2607. 15968v1 Announce Type: cross Abstract: We study a society of agents belonging to a number of occupational or cultural groups that form opinions about others' situation in the same or different group.
The paper reports the first large‑scale empirical comparison of AI‑agent and human online communities, analyzing 73,899 Moltbook and 189,838 Reddit posts across five matched communities. It finds that Moltbook shows extreme participation inequality (Gini = 0.84 vs. 0.47) and high cross‑community author overlap (33.8% vs. 0.5%). Linguistically, AI‑generated content is emotionally flattened, more assertive than exploratory, and socially detached, leading to community‑level homogenization that is largely a structural artifact of shared authorship. At the individual level, AI agents are more identifiable than human users due to outlier stylistic profiles amplified by their extreme posting volume.
The paper introduces ODCBNA, an ontology-driven framework for analyzing Bengali cyberbullying that integrates ontology design, knowledge graph construction, behavioral modeling, community detection, and influence modeling. Using the BenCoDiC corpus, it identifies 1,063 perpetrators, builds a 64‑dimensional behavioral profile for each, and constructs a weighted network with 27,554 edges. Louvain clustering reveals six communities and 27 sub‑communities, with Video, Impact, and User Profile features driving similarity, and influence analysis shows modest gains across communities with various seed‑selection methods.
arXiv:2608.00929v2 Announce Type: replace Abstract: Social dynamics encode the process in which individual network and discourse interactions aggregate into collective influence, narrative dominance...
The paper presents an agentic framework for detecting conspiratorial content in social media by inferring the speaker’s intent rather than merely identifying explicit claims. It leverages social context and adaptive tool use, demonstrating superior performance over text-only and non-agentic models on a large Hebrew tweet dataset spanning election cycles and the COVID pandemic. The study highlights the importance of context-aware, reasoning-driven approaches for accurate conspiracy detection.