@inproceedings{schwager-etal-2026-towards,
    title = "Towards Simulating Social Media Users with {LLM}s: Evaluating the Operational Validity of Conditioned Comment Prediction",
    author = {Schwager, Nils  and
      M{\"u}nker, Simon  and
      Plum, Alistair  and
      Rettinger, Achim},
    editor = "Barnes, Jeremy  and
      Barriere, Valentin  and
      De Clercq, Orph{\'e}e  and
      Klinger, Roman  and
      Nouri, C{\'e}lia  and
      Nozza, Debora  and
      Singh, Pranaydeep",
    booktitle = "The Proceedings for the 15th Workshop on Computational Approaches to Subjectivity, Sentiment Social Media Analysis ({WASSA} 2026)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.wassa-1.16/",
    pages = "208--221",
    ISBN = "979-8-89176-378-4",
    abstract = "The transition of Large Language Models (LLMs) from exploratory tools to active ``silicon subjects'' in social science lacks extensive validation of operational validity. This study introduces Conditioned Comment Prediction (CCP), a task in which a model predicts how a user would comment on a given stimulus by comparing generated outputs with authentic digital traces. This framework enables a rigorous evaluation of current LLM capabilities with respect to the simulation of social media user behavior. We evaluated open-weight 8B models (Llama-3.1, Qwen3, Ministral) in English, German, and Luxembourgish language scenarios. By systematically comparing prompting strategies (explicit vs. implicit) and the impact of Supervised Fine-Tuning (SFT), we identify a critical form vs. content decoupling in low-resource settings: while SFT aligns the surface structure of the text output (length and syntax), it degrades semantic grounding. Furthermore, we demonstrate that explicit conditioning (generated biographies) becomes redundant under fine-tuning, as models successfully perform latent inference directly from behavioral histories. Our findings challenge current ``naive prompting'' paradigms and offer operational guidelines prioritizing authentic behavioral traces over descriptive personas for high-fidelity simulation."
}