🧑‍🔬 Simon Münker

🏛️ Affiliations

🎓Universität TrierTrier · Germany2021–2026 · 12 papersEducation 2021–2023 · M.Sc. Natural Language Processing, Computational LinguisticsEmployment 2022–2023 · Research Assistant, Computational LinguisticsEmployment since 2023 · Research Fellow, Computational Linguisticscurrent🎓Heinrich Heine University DüsseldorfDüsseldorf · Germany2017–2021Education 2017–2021 · B. A. Computational Linguistics, Linguistics

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

🧭 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📅 Works by year

2020s

📄 Papers

The most cited first (OpenAlex and our library).

1. Don't Trust Generative Agents to Mimic Communication on Social Networks Unless You Benchmarked their Empirical Realism (2026 · 4 citations)
   LLM-driven agents are increasingly used as stand-ins for people in computational social science. However, studies disagree on whether they reproduce human behaviour faithfully. The authors examine this for social networks, where simulation could reveal how platform design choices such as ranking or filtering affect…
2. Towards Simulating Social Media Users with LLMs: Evaluating the Operational Validity of Conditioned Comment Prediction (2026 · 1 citation)
   The paper asks whether LLMs used as stand-ins for real people in social science can reproduce how specific individuals behave online, rather than just producing comments that look plausible.
3. Challenging the Myth: A Research Arc on LLMs as Human Simulacra (2026)
   This position-style paper examines a popular assumption in computational social science: that off-the-shelf, instruction-tuned open-source LLMs, given only light prompting, can stand in for human participants.
4. Next Reply Prediction X (NRP-X) Dataset: Linguistic Discrepancies in Naively Generated Content (2026)
   Social scientists increasingly use LLMs as stand-ins for human participants. When models are simply prompted without behavioral constraints, their writing may differ from real users' in ways that undermine research validity. The authors build NRP-X, a resource for testing this on authentic X (formerly Twitter) data…

All works

5 works of theirs in our library, the most cited first; 4 with a page here, the others link their original until we summarise them.

1. Zero-shot prompt-based classification: topic labeling in times of foundation models in German Tweets ↗Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop) · no summary yetSimon Münker, Kai Kugler, Achim Rettinger2025 · 5 citations
2. Don't Trust Generative Agents to Mimic Communication on Social Networks Unless You Benchmarked their Empirical RealismConference of the European Chapter of the Association for Computational Linguistics (EACL)Simon Münker, Nils Schwager, Achim Rettinger2026 · 4 citations
3. Towards Simulating Social Media Users with LLMs: Evaluating the Operational Validity of Conditioned Comment PredictionWorkshop on Computational Approaches to Subjectivity, Sentiment and Social Media AnalysisNils Schwager, Simon Münker, Alistair Plum, et al.2026 · 1 citation
4. Challenging the Myth: A Research Arc on LLMs as Human SimulacraBig Picture v2: Crafting a Research NarrativeSimon Münker, Achim Rettinger, Damian Trilling2026
5. Next Reply Prediction X (NRP-X) Dataset: Linguistic Discrepancies in Naively Generated ContentShaping Multilingual, Multimodal AI for the Social Sciences and Humanities @ LRECSimon Münker, Nils Schwager, Kai Kugler, et al.2026

🤝 Co-authors here

4 co-authors with a page, the most papers together first.

• Achim Rettinger ×4
• Nils Schwager ×3
• Alistair Plum
• Kai Kugler

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

• Big Picture v2: Crafting a Research Narrative: Big Picture v2: Crafting a Research Narrative 2026
• Conference of the European Chapter of the Association for Computational Linguistics (EACL): EACL 2026
• Shaping Multilingual, Multimodal AI for the Social Sciences and Humanities @ LREC: Shaping Multilingual, Multimodal AI for the Social Sciences and Humanities @ LREC 2026
• Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis: Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis 2026
• Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)

🔗 Cited by

Papers of our library cite their works 6 times (of the 149,439 papers whose bibliographies we read); 3 citing papers with a page here →.

Updated 1 November 2026

↑ back to top

This page in Markdown → https://md.research.elamur.ai/free/en/authors/simon-munker/