@article{58076,
  abstract     = {{This paper presents the concept of Information Circularity Assistance, which provides decision support in the early stages of product creation for Circular Economy. Engineers in strategic product planning need to proactively predict the quantity, quality, and timing of secondary materials and returned components. For example, products with high recycled content will only be economically sustainable if the material is actually available in the future product life. Our assumption is that Information Circularity Assistance enables decision makers to incorporate insights from extreme data – high-volume, high-velocity, heterogeneous and distributed data from the product life – into product creation through intelligent Digital Twins. Artificial Intelligence can help to derive sustainable actions in favor of circular products by processing extreme data and enriching it with expert knowledge. The research contributes in three key dimensions. First, a comprehensive literature review is conducted. This review covers concepts of intelligence in Scenario-Technique for strategic product planning, Digital Twin-based analysis of extreme data and relevant technologies from Data Science and Artificial Intelligence. In all areas, the state of the art and emerging trends are identified. Secondly, the study identifies information needs along the steps of the Scenario-Technique and information offerings based on Digital Twins. The concept of Information Circularity Assistance results from the coupling of these demands and offerings, extending the Scenario-Technique beyond traditional expert-based methods. Third, we extend existing Digital Twin methods used in circularity and discuss the deployment of Data Science and Artificial Intelligence algorithms within the product creation process. Our approach uses extreme data to provide a strategic advantage in optimizing product life cycle planning, which is illustrated by two sample applications. The aim is to provide Information Circularity Assistance that will support experienced product planners, developers, and decision makers in the future.}},
  author       = {{Gräßler, Iris and Weyrich, Michael and Pottebaum, Jens and Kamm, Simon}},
  issn         = {{0178-2312}},
  journal      = {{at - Automatisierungstechnik}},
  keywords     = {{Scenario-Technique, Artificial Intelligence, Digital Twin, Large Language Models}},
  number       = {{1}},
  pages        = {{3--21}},
  publisher    = {{Walter de Gruyter GmbH}},
  title        = {{{Information Circularity Assistance based on extreme data}}},
  doi          = {{10.1515/auto-2024-0039}},
  volume       = {{73}},
  year         = {{2025}},
}

@article{57892,
  abstract     = {{The present paper discusses the extent to which Large Language Models (LLMs) may affect the scientific enterprise, reinforcing or mitigating existing structural inequalities expressed by the Matthew Effect and introducing a “bot delusion” in academia. In a theory-led thought experiment, we first focus on the academic publication and citation system and develop three scenarios of the anticipated consequences of using LLMs: reproducing content and status quo (Scenario 1), enabling content coherence evaluation (Scenario 2) and content evaluation (Scenario 3). Second, we discuss the interaction between the use of LLMs and academic (counter)norms for citation selection and their impact on the publication and citation system. Finally, we introduce communal counter-norms to capture academics’ loyal citation behavior and develop three future scenarios that academia may face when LLMs are widely used in the research process, namely status quo future of science, mixed-access future, and open science future.}},
  author       = {{Wieczorek, Oliver and Steinhardt, Isabel and Schmidt, Rebecca and Mauermeister, Sylvi and Schneijderberg, Christian}},
  issn         = {{0016-3287}},
  journal      = {{Futures}},
  keywords     = {{Large Language Models, Matthew Effect, Academic Publishing and Citation Systems, Scientific Norms, Thought Experiment}},
  publisher    = {{Elsevier BV}},
  title        = {{{The Bot Delusion. Large language models and anticipated consequences for academics’ publication and citation behavior}}},
  doi          = {{10.1016/j.futures.2024.103537}},
  volume       = {{166}},
  year         = {{2024}},
}

@inproceedings{56983,
  abstract     = {{Detecting the veracity of a statement automatically is a challenge the world is grappling with due to the vast amount of data spread across the web. Verifying a given claim typically entails validating it within the framework of supporting evidence like a retrieved piece of text. Classifying the stance of the text with respect to the claim is called stance classification. Despite advancements in automated fact-checking, most systems still rely on a substantial quantity of labeled training data, which can be costly. In this work, we avoid the costly training or fine-tuning of models by reusing pre-trained large language models together with few-shot in-context learning. Since we do not train any model, our approach ExPrompt is lightweight, demands fewer resources than other stance classification methods and can serve as a modern baseline for future developments. At the same time, our evaluation shows that our approach is able to outperform former state-of-the-art stance classification approaches regarding accuracy by at least 2 percent. Our scripts and data used in this paper are available at https://github.com/dice-group/ExPrompt.}},
  author       = {{Qudus, Umair and Röder, Michael and Vollmers, Daniel and Ngonga Ngomo, Axel-Cyrille}},
  booktitle    = {{Proceedings of the 33rd ACM International Conference on Information and Knowledge Management}},
  isbn         = {{79-8-4007-0436-9/24/10}},
  keywords     = {{Stance Classification, Few-shot in-context learning, Pre-trained large language models}},
  location     = {{Boise, ID, USA}},
  pages        = {{3994 -- 3999}},
  publisher    = {{ACM}},
  title        = {{{ExPrompt: Augmenting Prompts Using Examples as Modern Baseline for Stance Classification}}},
  doi          = {{10.1145/3627673.3679923}},
  volume       = {{9}},
  year         = {{2024}},
}

@inproceedings{52865,
  abstract     = {{This paper addresses new challenges of detecting campaigns in social media, which emerged with the rise of Large Language Models (LLMs). LLMs particularly challenge algorithms focused on the temporal analysis of topical clusters. Simple similarity measures can no longer capture and map campaigns that were previously broadly similar in content. Herein, we analyze whether the classification of messages over time can be profitably used to rediscover poorly detectable campaigns at the content level. Thus, we evaluate classical classifiers and a new method based on siamese neural networks. Our results show that campaigns can be detected despite the limited reliability of the classifiers as long as they are based on a large amount of simultaneously spread artificial content.}},
  author       = {{Grimme, Britta and Pohl, Janina and Winkelmann, Hendrik and Stampe, Lucas and Grimme, Christian}},
  booktitle    = {{Disinformation in Open Online Media: 5th Multidisciplinary International Symposium, MISDOOM 2023, Amsterdam, The Netherlands, November 21–22, 2023, Proceedings}},
  isbn         = {{978-3-031-47895-6}},
  keywords     = {{Social Media, Campaign Detection, Large Language Models, Siamese Neural Networks}},
  pages        = {{72–87}},
  publisher    = {{Springer-Verlag}},
  title        = {{{Lost in Transformation: Rediscovering LLM-Generated Campaigns in Social Media}}},
  doi          = {{10.1007/978-3-031-47896-3_6}},
  year         = {{2023}},
}