---
_id: '58076'
abstract:
- lang: eng
text: 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.
alternative_title:
- Utilizing Artificial Intelligence, Scenario-Technique and Digital Twins to solve
challenges of product creation for Circular Economy
article_type: original
author:
- first_name: Iris
full_name: Gräßler, Iris
id: '47565'
last_name: Gräßler
orcid: 0000-0001-5765-971X
- first_name: Michael
full_name: Weyrich, Michael
last_name: Weyrich
- first_name: Jens
full_name: Pottebaum, Jens
id: '405'
last_name: Pottebaum
orcid: http://orcid.org/0000-0001-8778-2989
- first_name: Simon
full_name: Kamm, Simon
last_name: Kamm
citation:
ama: Gräßler I, Weyrich M, Pottebaum J, Kamm S. Information Circularity Assistance
based on extreme data. at - Automatisierungstechnik. 2025;73(1):3-21. doi:10.1515/auto-2024-0039
apa: Gräßler, I., Weyrich, M., Pottebaum, J., & Kamm, S. (2025). Information
Circularity Assistance based on extreme data. At - Automatisierungstechnik,
73(1), 3–21. https://doi.org/10.1515/auto-2024-0039
bibtex: '@article{Gräßler_Weyrich_Pottebaum_Kamm_2025, title={Information Circularity
Assistance based on extreme data}, volume={73}, DOI={10.1515/auto-2024-0039},
number={1}, journal={at - Automatisierungstechnik}, publisher={Walter de Gruyter
GmbH}, author={Gräßler, Iris and Weyrich, Michael and Pottebaum, Jens and Kamm,
Simon}, year={2025}, pages={3–21} }'
chicago: 'Gräßler, Iris, Michael Weyrich, Jens Pottebaum, and Simon Kamm. “Information
Circularity Assistance Based on Extreme Data.” At - Automatisierungstechnik
73, no. 1 (2025): 3–21. https://doi.org/10.1515/auto-2024-0039.'
ieee: 'I. Gräßler, M. Weyrich, J. Pottebaum, and S. Kamm, “Information Circularity
Assistance based on extreme data,” at - Automatisierungstechnik, vol. 73,
no. 1, pp. 3–21, 2025, doi: 10.1515/auto-2024-0039.'
mla: Gräßler, Iris, et al. “Information Circularity Assistance Based on Extreme
Data.” At - Automatisierungstechnik, vol. 73, no. 1, Walter de Gruyter
GmbH, 2025, pp. 3–21, doi:10.1515/auto-2024-0039.
short: I. Gräßler, M. Weyrich, J. Pottebaum, S. Kamm, At - Automatisierungstechnik
73 (2025) 3–21.
date_created: 2025-01-07T13:30:45Z
date_updated: 2025-02-15T09:41:54Z
department:
- _id: '152'
doi: 10.1515/auto-2024-0039
intvolume: ' 73'
issue: '1'
keyword:
- Scenario-Technique
- Artificial Intelligence
- Digital Twin
- Large Language Models
language:
- iso: eng
main_file_link:
- open_access: '1'
oa: '1'
page: 3-21
publication: at - Automatisierungstechnik
publication_identifier:
issn:
- 0178-2312
- 2196-677X
publication_status: published
publisher: Walter de Gruyter GmbH
quality_controlled: '1'
status: public
title: Information Circularity Assistance based on extreme data
type: journal_article
user_id: '405'
volume: 73
year: '2025'
...
---
_id: '57892'
abstract:
- lang: eng
text: '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.'
article_number: '103537'
article_type: original
author:
- first_name: Oliver
full_name: Wieczorek, Oliver
last_name: Wieczorek
- first_name: Isabel
full_name: Steinhardt, Isabel
id: '90339'
last_name: Steinhardt
orcid: https://orcid.org/0000-0002-2590-6189
- first_name: Rebecca
full_name: Schmidt, Rebecca
id: '94416'
last_name: Schmidt
orcid: https://orcid.org/0000-0002-2516-359X
- first_name: Sylvi
full_name: Mauermeister, Sylvi
id: '98032'
last_name: Mauermeister
- first_name: Christian
full_name: Schneijderberg, Christian
last_name: Schneijderberg
citation:
ama: Wieczorek O, Steinhardt I, Schmidt R, Mauermeister S, Schneijderberg C. The
Bot Delusion. Large language models and anticipated consequences for academics’
publication and citation behavior. Futures. 2024;166. doi:10.1016/j.futures.2024.103537
apa: Wieczorek, O., Steinhardt, I., Schmidt, R., Mauermeister, S., & Schneijderberg,
C. (2024). The Bot Delusion. Large language models and anticipated consequences
for academics’ publication and citation behavior. Futures, 166,
Article 103537. https://doi.org/10.1016/j.futures.2024.103537
bibtex: '@article{Wieczorek_Steinhardt_Schmidt_Mauermeister_Schneijderberg_2024,
title={The Bot Delusion. Large language models and anticipated consequences for
academics’ publication and citation behavior}, volume={166}, DOI={10.1016/j.futures.2024.103537},
number={103537}, journal={Futures}, publisher={Elsevier BV}, author={Wieczorek,
Oliver and Steinhardt, Isabel and Schmidt, Rebecca and Mauermeister, Sylvi and
Schneijderberg, Christian}, year={2024} }'
chicago: Wieczorek, Oliver, Isabel Steinhardt, Rebecca Schmidt, Sylvi Mauermeister,
and Christian Schneijderberg. “The Bot Delusion. Large Language Models and Anticipated
Consequences for Academics’ Publication and Citation Behavior.” Futures
166 (2024). https://doi.org/10.1016/j.futures.2024.103537.
ieee: 'O. Wieczorek, I. Steinhardt, R. Schmidt, S. Mauermeister, and C. Schneijderberg,
“The Bot Delusion. Large language models and anticipated consequences for academics’
publication and citation behavior,” Futures, vol. 166, Art. no. 103537,
2024, doi: 10.1016/j.futures.2024.103537.'
mla: Wieczorek, Oliver, et al. “The Bot Delusion. Large Language Models and Anticipated
Consequences for Academics’ Publication and Citation Behavior.” Futures,
vol. 166, 103537, Elsevier BV, 2024, doi:10.1016/j.futures.2024.103537.
short: O. Wieczorek, I. Steinhardt, R. Schmidt, S. Mauermeister, C. Schneijderberg,
Futures 166 (2024).
date_created: 2024-12-31T08:30:51Z
date_updated: 2024-12-31T08:36:28Z
department:
- _id: '121'
doi: 10.1016/j.futures.2024.103537
intvolume: ' 166'
keyword:
- Large Language Models
- Matthew Effect
- Academic Publishing and Citation Systems
- Scientific Norms
- Thought Experiment
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://www.sciencedirect.com/science/article/pii/S0016328724002209?via%3Dihub
oa: '1'
publication: Futures
publication_identifier:
issn:
- 0016-3287
publication_status: published
publisher: Elsevier BV
quality_controlled: '1'
status: public
title: The Bot Delusion. Large language models and anticipated consequences for academics’
publication and citation behavior
type: journal_article
user_id: '90339'
volume: 166
year: '2024'
...
---
_id: '56983'
abstract:
- lang: eng
text: 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:
- first_name: Umair
full_name: Qudus, Umair
id: '83392'
last_name: Qudus
orcid: 0000-0001-6714-8729
- first_name: Michael
full_name: Röder, Michael
id: '67199'
last_name: Röder
orcid: https://orcid.org/0000-0002-8609-8277
- first_name: Daniel
full_name: Vollmers, Daniel
last_name: Vollmers
- first_name: Axel-Cyrille
full_name: Ngonga Ngomo, Axel-Cyrille
id: '65716'
last_name: Ngonga Ngomo
citation:
ama: 'Qudus U, Röder M, Vollmers D, Ngonga Ngomo A-C. ExPrompt: Augmenting Prompts
Using Examples as Modern Baseline for Stance Classification. In: Proceedings
of the 33rd ACM International Conference on Information and Knowledge Management.
Vol 9. ACM; 2024:3994-3999. doi:10.1145/3627673.3679923'
apa: 'Qudus, U., Röder, M., Vollmers, D., & Ngonga Ngomo, A.-C. (2024). ExPrompt:
Augmenting Prompts Using Examples as Modern Baseline for Stance Classification.
Proceedings of the 33rd ACM International Conference on Information and Knowledge
Management, 9, 3994–3999. https://doi.org/10.1145/3627673.3679923'
bibtex: '@inproceedings{Qudus_Röder_Vollmers_Ngonga Ngomo_2024, title={ExPrompt:
Augmenting Prompts Using Examples as Modern Baseline for Stance Classification},
volume={9}, DOI={10.1145/3627673.3679923},
booktitle={Proceedings of the 33rd ACM International Conference on Information
and Knowledge Management}, publisher={ACM}, author={Qudus, Umair and Röder, Michael
and Vollmers, Daniel and Ngonga Ngomo, Axel-Cyrille}, year={2024}, pages={3994–3999}
}'
chicago: 'Qudus, Umair, Michael Röder, Daniel Vollmers, and Axel-Cyrille Ngonga
Ngomo. “ExPrompt: Augmenting Prompts Using Examples as Modern Baseline for Stance
Classification.” In Proceedings of the 33rd ACM International Conference on
Information and Knowledge Management, 9:3994–99. ACM, 2024. https://doi.org/10.1145/3627673.3679923.'
ieee: 'U. Qudus, M. Röder, D. Vollmers, and A.-C. Ngonga Ngomo, “ExPrompt: Augmenting
Prompts Using Examples as Modern Baseline for Stance Classification,” in Proceedings
of the 33rd ACM International Conference on Information and Knowledge Management,
Boise, ID, USA, 2024, vol. 9, pp. 3994–3999, doi: 10.1145/3627673.3679923.'
mla: 'Qudus, Umair, et al. “ExPrompt: Augmenting Prompts Using Examples as Modern
Baseline for Stance Classification.” Proceedings of the 33rd ACM International
Conference on Information and Knowledge Management, vol. 9, ACM, 2024, pp.
3994–99, doi:10.1145/3627673.3679923.'
short: 'U. Qudus, M. Röder, D. Vollmers, A.-C. Ngonga Ngomo, in: Proceedings of
the 33rd ACM International Conference on Information and Knowledge Management,
ACM, 2024, pp. 3994–3999.'
conference:
end_date: 2024-10-25
location: Boise, ID, USA
name: 'CIKM ''24: Proceedings of the 33rd ACM International Conference on Information
and Knowledge Management'
start_date: 2024-10-21
date_created: 2024-11-11T13:15:25Z
date_updated: 2025-09-11T09:49:07Z
ddc:
- '006'
doi: 10.1145/3627673.3679923
file:
- access_level: closed
content_type: application/pdf
creator: uqudus
date_created: 2024-11-11T13:24:19Z
date_updated: 2024-11-11T13:24:19Z
file_id: '56984'
file_name: public.pdf
file_size: 531579
relation: main_file
success: 1
file_date_updated: 2024-11-11T13:24:19Z
has_accepted_license: '1'
intvolume: ' 9'
keyword:
- Stance Classification
- Few-shot in-context learning
- Pre-trained large language models
language:
- iso: eng
main_file_link:
- url: https://dl.acm.org/doi/10.1145/3627673.3679923
page: 3994 - 3999
popular_science: '1'
project:
- _id: '412'
name: 'NEBULA: Nutzerzentrierte KI-basierte Erkennung von Fake-News und Fehlinformationen'
publication: Proceedings of the 33rd ACM International Conference on Information and
Knowledge Management
publication_identifier:
isbn:
- 79-8-4007-0436-9/24/10
publication_status: published
publisher: ACM
quality_controlled: '1'
status: public
title: 'ExPrompt: Augmenting Prompts Using Examples as Modern Baseline for Stance
Classification'
type: conference
user_id: '83392'
volume: 9
year: '2024'
...
---
_id: '52865'
abstract:
- lang: eng
text: 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:
- first_name: Britta
full_name: Grimme, Britta
last_name: Grimme
- first_name: Janina
full_name: Pohl, Janina
last_name: Pohl
- first_name: Hendrik
full_name: Winkelmann, Hendrik
last_name: Winkelmann
- first_name: Lucas
full_name: Stampe, Lucas
last_name: Stampe
- first_name: Christian
full_name: Grimme, Christian
last_name: Grimme
citation:
ama: 'Grimme B, Pohl J, Winkelmann H, Stampe L, Grimme C. Lost in Transformation:
Rediscovering LLM-Generated Campaigns in Social Media. In: Disinformation in
Open Online Media: 5th Multidisciplinary International Symposium, MISDOOM 2023,
Amsterdam, The Netherlands, November 21–22, 2023, Proceedings. Springer-Verlag;
2023:72–87. doi:10.1007/978-3-031-47896-3_6'
apa: 'Grimme, B., Pohl, J., Winkelmann, H., Stampe, L., & Grimme, C. (2023).
Lost in Transformation: Rediscovering LLM-Generated Campaigns in Social Media.
Disinformation in Open Online Media: 5th Multidisciplinary International Symposium,
MISDOOM 2023, Amsterdam, The Netherlands, November 21–22, 2023, Proceedings,
72–87. https://doi.org/10.1007/978-3-031-47896-3_6'
bibtex: '@inproceedings{Grimme_Pohl_Winkelmann_Stampe_Grimme_2023, place={Berlin,
Heidelberg}, title={Lost in Transformation: Rediscovering LLM-Generated Campaigns
in Social Media}, DOI={10.1007/978-3-031-47896-3_6},
booktitle={Disinformation in Open Online Media: 5th Multidisciplinary International
Symposium, MISDOOM 2023, Amsterdam, The Netherlands, November 21–22, 2023, Proceedings},
publisher={Springer-Verlag}, author={Grimme, Britta and Pohl, Janina and Winkelmann,
Hendrik and Stampe, Lucas and Grimme, Christian}, year={2023}, pages={72–87} }'
chicago: 'Grimme, Britta, Janina Pohl, Hendrik Winkelmann, Lucas Stampe, and Christian
Grimme. “Lost in Transformation: Rediscovering LLM-Generated Campaigns in Social
Media.” In Disinformation in Open Online Media: 5th Multidisciplinary International
Symposium, MISDOOM 2023, Amsterdam, The Netherlands, November 21–22, 2023, Proceedings,
72–87. Berlin, Heidelberg: Springer-Verlag, 2023. https://doi.org/10.1007/978-3-031-47896-3_6.'
ieee: 'B. Grimme, J. Pohl, H. Winkelmann, L. Stampe, and C. Grimme, “Lost in Transformation:
Rediscovering LLM-Generated Campaigns in Social Media,” in Disinformation in
Open Online Media: 5th Multidisciplinary International Symposium, MISDOOM 2023,
Amsterdam, The Netherlands, November 21–22, 2023, Proceedings, 2023, pp. 72–87,
doi: 10.1007/978-3-031-47896-3_6.'
mla: 'Grimme, Britta, et al. “Lost in Transformation: Rediscovering LLM-Generated
Campaigns in Social Media.” Disinformation in Open Online Media: 5th Multidisciplinary
International Symposium, MISDOOM 2023, Amsterdam, The Netherlands, November 21–22,
2023, Proceedings, Springer-Verlag, 2023, pp. 72–87, doi:10.1007/978-3-031-47896-3_6.'
short: 'B. Grimme, J. Pohl, H. Winkelmann, L. Stampe, C. Grimme, in: Disinformation
in Open Online Media: 5th Multidisciplinary International Symposium, MISDOOM 2023,
Amsterdam, The Netherlands, November 21–22, 2023, Proceedings, Springer-Verlag,
Berlin, Heidelberg, 2023, pp. 72–87.'
date_created: 2024-03-25T14:38:01Z
date_updated: 2026-03-19T07:48:51Z
doi: 10.1007/978-3-031-47896-3_6
keyword:
- Social Media
- Campaign Detection
- Large Language Models
- Siamese Neural Networks
page: 72–87
place: Berlin, Heidelberg
publication: 'Disinformation in Open Online Media: 5th Multidisciplinary International
Symposium, MISDOOM 2023, Amsterdam, The Netherlands, November 21–22, 2023, Proceedings'
publication_identifier:
isbn:
- 978-3-031-47895-6
publisher: Springer-Verlag
status: public
title: 'Lost in Transformation: Rediscovering LLM-Generated Campaigns in Social Media'
type: conference
user_id: '103682'
year: '2023'
...