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REVIEW 4 major objections 5 minor 40 references

KI4Demokratie: An AI-Based Platform for Monitoring and Fostering Democratic Discourse

T0 review · 4 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read The paper presents KI4Demokratie, a dashboard that combines sentiment analysis, hate speech detection, dynamic topic modeling, network analysis, and fact-checking to monitor right-wing discourse in the German digital sphere, and argues…

desk verdict Plausible system paper whose central event-driven claim is undercut by an unvalidated translation pipeline and a low-precision hate speech classifier. read the letter →

arxiv 2506.09947 v2 pith:BVDCUPNU submitted 2025-06-11 cs.CY cs.SI

Rudy Alexandro Garrido Veliz , Till Nikolaus Schaland , Simon Bergmoser , Florian Horwege , Somya Bansal , Ritesh Nahar , Martin Semmann , Jörg Forthmann
show 1 more author
Seid Muhie Yimam
This is my paper · ORCID
classification cs.CYcs.SI
keywords right-wingextremismdiscoursemonitoringsentimentanalysishatespeechdetectiondynamictopicmodelingsocialnetworkfact-checkingGermandigitalsphere
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

KI4Demokratie is a prototype dashboard for monitoring right-wing discourse in German social media and news. The authors argue that no single existing tool integrates the five analytical layers needed to follow extremist narratives as they move across platforms: sentiment, hate speech, topics, networks, and factual claims. Their central evidence is a case study around the January 2025 Aschaffenburg incident, where daily topic trends show a hate-speech surge and a subsequent rise in migration-policy themes that line up with real-world events. If the approach holds up, journalists, researchers, and policymakers would get a practical, platform-independent early-warning system for antidemocratic narratives. The paper is explicit that this is early, in-progress work, not a deployed or validated service.

What carries the argument

The load-bearing mechanism is a daily ingestion and analysis loop. A keyword-filtered stream of German posts is passed through four parallel analyses: sentiment scoring and hate speech classification using English-trained small language models applied to machine-translated text; dynamic topic modeling that embeds posts in a semantic vector space, clusters them, and tracks cluster frequencies over time; a network graph in which edges are labeled intentional (tagged users), inferred (named entities), or passive-mutual (co-mentioned by a third party) and weighted by occurrence; and a three-stage fact-checking chain that extracts claims, retrieves evidence from a news search, and produces a five-level truthfulness verdict. The topic-modeling time series is the component that carries the paper's event-linkage argument, because it converts narrative shifts into curves that can be visually aligned with dates such as the Aschaffenburg incident.

What would settle it

Take the same German posts around the Aschaffenburg incident, score them with German-native sentiment and hate speech classifiers, and compare the daily curves to the translated-English-model curves; if the hate-speech spike around January 22, 2025 disappears or changes sign, the central demonstration is an artifact of the translation pipeline. A simpler observational check is to run the pipeline on a matched set of dates with no major incident and ask whether comparable spikes occur.

Watch

Extended reading notes

Core claim

The authors' central claim is that KI4Demokratie, a dashboard combining sentiment analysis, hate speech detection, dynamic topic modeling, network analysis, and fact-checking, can track right-wing discourse in the German digital sphere on a daily basis. The demonstration case is the January 2025 Aschaffenburg incident: the topic-modeling visualizations show hate-speech-related themes rising immediately after the event and migration-policy topics rising in parallel with a conservative party's subsequent proposal, which the authors take as evidence that their models can pinpoint dominant themes at specific times and link fluctuations to real-world events. The paper also reports a 100-post human-annotated evaluation in which the full pipeline reaches moderate accuracy, with a general-purpose large language model outperforming the two specialized small models on both hate speech and sentiment tasks.

Load-bearing premise

The whole sentiment and hate speech layer depends on the untested premise that English-trained models, applied after machine translation, capture German tone well enough that daily trend lines and event-linked spikes are meaningful.

Editorial extensions

If this is right

  • Journalists and researchers could watch hate speech and sentiment trends across platforms in one dashboard, rather than stitching together separate analyses.
  • Daily topic time series would let analysts date when a narrative, for example 'remigration', enters mainstream political discourse and when it fades.
  • The network graph could expose influential actors who are rarely active but frequently mentioned, using the passive-mutual edge type and centrality scaling.
  • The fact-checking module would give a per-author breakdown of evidence-supported versus unsupported claims, making it possible to compare political actors by their truthfulness scores.
  • The evaluation results suggest that general-purpose language models, not specialized English-trained small models, are currently the stronger route for German hate speech and sentiment detection.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Editorial inference: the authors' event-linkage claim would be much stronger with control periods; running the same topic-modeling pipeline on dates without major incidents would show whether hate-speech themes spike at random times too.
  • Editorial inference: because the sentiment and hate speech models are English-trained and machine-translated, the dashboard may systematically misread German irony and dialect, and a direct comparison against German-native classifiers would reveal whether the trend lines are artifacts of translation.
  • Editorial inference: the architecture is portable to other languages and political contexts once the keyword list, translation target, and fact-checking sources are swapped, so the key contribution is the integration pattern rather than any single model.
  • Editorial inference: the passive-mutual edge type is a testable extension, since co-mention networks could reveal narrative alliances between accounts that never interact directly.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper presents KI4Demokratie, a dashboard platform for monitoring right-wing discourse in the German digital sphere. The system integrates sentiment analysis (TimeLM), hate speech detection (LFTW R4 Target), BERTopic-based dynamic topic modeling, network graph analysis, and a GPT-3.5-based fact-checking prototype. The pipeline translates German text to English before applying the English-trained sentiment and hate speech models. Evaluation is based on 100 randomly selected posts annotated by German native speakers, with inter-annotator agreement around Kappa 0.43; Table 1 reports hate speech F1 of 0.56 (LFTW) and 0.58 (GPT-4o-mini) and sentiment F1 of 0.74 (TimeLM) and 0.81 (GPT-4o-mini). The authors present two topic-modeling visualizations around the January 2025 Aschaffenburg incident and claim that they demonstrate the platform's ability to link discourse fluctuations to real-world events. The central claim is that the integrated platform shows promise for monitoring and fostering democratic discourse, though the authors acknowledge the project is still in progress.

Significance. If the platform's monitoring claims hold, it would offer an integrated, practitioner-facing tool for journalists, researchers, and policymakers, combining sentiment, hate speech, topic, network, and fact-checking analytics on German-language social media and news data. The paper has notable strengths: it uses independent human gold labels, includes a comparison against GPT-4o-mini, provides an explicit limitations section, and addresses ethical considerations around access control and public speech. However, the current evidence is insufficient to establish the central monitoring claim: the translation pipeline is unvalidated, the hate speech model has low precision, the evaluation is very small, and the event-linked topic-modeling conclusions are supported only by qualitative visualizations. These gaps are addressable with additional validation and more cautious claims, so the result is promising but not yet demonstrated.

major comments (4)
  1. [Section 3.1] The core sentiment and hate speech pipeline applies English-trained TimeLM and LFTW R4 Target models to German text via translation, yet the paper provides no validation of translation quality, no cross-lingual transfer analysis, and no comparison with German-native or multilingual models. Because all downstream trend analyses and the Section 4 event-linked conclusions depend on these model outputs, this is a load-bearing gap. A concrete test on a German hate speech benchmark such as GermEval 2018, together with translation quality scores and an error analysis for irony, slurs, and regionalisms, is needed before the monitoring claims can be supported.
  2. [Section 4, Table 1] The hate speech model achieves precision 0.42 and F1 0.56 on the 100-post gold set, meaning that at the current operating point most of its positive flags are false positives. Section 4 nevertheless states that the dashboard visualizations 'clearly demonstrate' an 'increase in hate speech-related themes' around the Aschaffenburg incident. Without confidence intervals and without checking that the temporal increase is not a classifier artifact, this conclusion is unsupported. The authors should report uncertainty, show precision-recall tradeoffs at the deployed threshold, and either verify a sample of flagged posts around the event or reframe the claim as an increase in model-flagged hate speech rather than actual hate speech.
  3. [Section 4 and Appendix B] The dynamic topic modeling results are presented as two qualitative plots, with the assertion that they 'clearly demonstrate' the incident's impact on social media discourse. No topic coherence scores, human validation of topic labels, or statistical tests linking topic frequency changes to the event are provided. The figures should either be accompanied by quantitative topic prevalence and effect sizes or be explicitly described as illustrative examples rather than demonstrations of capability.
  4. [Section 4] The evaluation uses only 100 posts, with inter-annotator agreement of about 0.43 for both sentiment and hate speech, and no confidence intervals are reported for the model performance figures in Table 1. This sample is too small to support general claims about monitoring 'large-scale German online data' or to reliably distinguish between the models. A larger evaluation stratified by platform and time period, or an explicit statement that the reported numbers are preliminary pilot results, is necessary.
minor comments (5)
  1. [Title page] There is a typo in the affiliation: 'Universtät Hamburg' should be 'Universität Hamburg', and the author name contains an irregular spacing in 'Garrido V eliz'.
  2. [Table 1] The model name 'LTFTW' in the table row should be 'LFTW' to match 'LFTW R4 Target' used in Section 3.1 and in the sentence following the table.
  3. [Section 3.4] The sentence 'GPT-3.5 was supplied with the claim, context information and context information' appears to duplicate 'context information' and should be corrected.
  4. [Appendix B, Figure 2 caption] The caption contains an ungrammatical phrase: 'from January 20 to February to January 31, 2025' should be 'from January 20 to January 31, 2025'.
  5. [Section 3.4] The fact-checking prototype is described in detail, but no evaluation of claim detection, evidence retrieval, or verdict accuracy is provided; a brief qualification that these outputs are not yet validated would help readers calibrate the dashboard claims.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation; central results are evaluated against independent human labels and external models.

full rationale

The paper's derivation chain is self-contained with respect to circularity. The sentiment and hate-speech components use pre-trained external models (TimeLM and LFTW R4 Target) as black boxes; these are not fitted to the paper's target data, and their outputs are checked against a gold standard of 100 posts labeled by German native speakers. The hate-speech and sentiment precision/F1 numbers in Table 1 are therefore independent measurements, not fitted quantities renamed as predictions. The topic-modeling demonstrations in Section 4 and Appendix B are qualitative, unsupervised BERTopic outputs; the paper does not define topic labels in terms of the event outcome or tune the model on the Aschaffenburg period, so the observed rise in migration and hate-speech themes is an empirical illustration rather than a construction. The claim that English-trained models are applied to German via translation is an unvalidated transfer assumption, and the low hate-speech precision weakens the event-linked conclusion, but these are correctness and validity risks rather than circular reasoning. Self-citations (Yimam et al. 2024; Sevgili et al. 2024) appear only in related work or as an alternative fact-checking approach; neither is load-bearing for the platform's results. No equation, fitted parameter, or cited uniqueness theorem reduces the outputs to the inputs.

Assumptions & free parameters 2 free parameters · 3 assumptions · 0 invented entities

The paper introduces no new physical entities or mechanisms. It relies on external pre-trained models and standard NLP libraries. The main free choices are the keyword list and topic-model hyperparameters, both undiscosed. The key domain assumptions are the validity of English-model-on-German translation, the reliability of majority-vote annotations despite low kappa, and the representativeness of the keyword-based data collection.

free parameters (2)
  • Keyword set for data collection = 500 keywords distilled from human experts (Section 3)
    The entire dataset depends on this hand-selected list. No keyword list is provided, so the sampling of right-wing discourse is not independently reproducible and may systematically miss or over-represent certain narratives.
  • BERTopic hyperparameters = UMAP and HDBSCAN settings chosen manually (Section 3.2)
    Parameters were selected to balance cluster granularity, but exact values are not given. This makes the topic clusters non-reproducible and potentially unstable across runs or data updates.
assumptions (3)
  • domain assumption English-language sentiment and hate speech models remain valid when applied to machine-translated German text.
    Used in Section 3.1. No evaluation of translation quality or cross-lingual validity is provided, yet the entire sentiment and hate speech pipeline depends on this transfer.
  • domain assumption Majority vote among four (or five) native speakers yields reliable gold labels for sentiment and hate speech.
    Section 4 reports Cohen's kappa around 0.43, indicating low agreement, yet the majority labels are treated as ground truth for model evaluation.
  • domain assumption The 500-keyword filtered collection captures the relevant right-wing discourse in the German digital sphere.
    Data collection relies on this human-curated keyword set, but no recall or coverage analysis is given, so the platform may miss significant portions of the discourse it claims to monitor.

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Cite this review

Pith. "Pith review of KI4Demokratie: An AI-Based Platform for Monitoring and Fostering Democratic Discourse." pith.science (2026). https://pith.science/paper/BVDCUPNU

@misc{pith2026250609947,
  author       = {Pith},
  title        = {Pith review of: KI4Demokratie: An AI-Based Platform for Monitoring and Fostering Democratic Discourse},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/BVDCUPNU}},
  note         = {Machine review of arXiv:2506.09947}
}
read the original abstract

Social media increasingly fuel extremism, especially right-wing extremism, and enable the rapid spread of antidemocratic narratives. Although AI and data science are often leveraged to manipulate political opinion, there is a critical need for tools that support effective monitoring without infringing on freedom of expression. We present KI4Demokratie, an AI-based platform that assists journalists, researchers, and policymakers in monitoring right-wing discourse that may undermine democratic values. KI4Demokratie applies machine learning models to a large-scale German online data gathered on a daily basis, providing a comprehensive view of trends in the German digital sphere. Early analysis reveals both the complexity of tracking organized extremist behavior and the promise of our integrated approach, especially during key events.

Figures

Figures reproduced from arXiv: 2506.09947 by the authors.

Figure 1
Figure 1. Complete pipeline of the KI4DEMOKRATIE project. topic modeling to provide a holistic view of polit￾ical discourse. Second, we demonstrate the appli￾cation of artificial intelligence in monitoring and countering extremism, thereby offering a blueprint for future initiatives in media oversight and demo￾cratic safeguarding. Third, we provide empirical insights into online extremist rhetoric dynamics, contributing to ac… view at source ↗
Figure 2
Figure 2. Temporal evolution of a wide range of political and extremist discourse topics on social media from [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. Evolution of key political discourse topics on social media from two days before the incident in [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗

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Reference graph

Works this paper leans on

40 extracted references · 37 canonical work pages

  1. [1]

    Murat Aktas. 2024. The rise of populist radical right parties in Europe . International Sociology, 39(6):591--605

    work page 2024

  2. [2]

    Jamie Bartlett, Jonathan Birdwell, and Mark Littler. 2011. The New Face of Digital Populism. Demos, London

    work page 2011

  3. [3]

    Blei, Andrew Y

    David M. Blei, Andrew Y. Ng, and Michael I. Jordan. 2003. Latent Dirichlet Allocation . Journal of Machine Learning Research, 3:993--1022

    work page 2003

  4. [4]

    Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, and 12 others. 2020. Language models are few-shot learner...

    work page 2020

  5. [5]

    We Want You!

    Yi Ting Chua. 2024. “We Want You!” Applying Social Network Analysis to Online Extremist Communities . Terrorism and Political Violence, 37(3):409--423

    work page 2024

  6. [6]

    Maura Conway, Ryan Scrivens, and Logan Macnair. 2019. Right-Wing Extremists’ Persistent Online Presence: History and Contemporary Trends . International Centre for Counter-Terrorism

    work page 2019

  7. [7]

    Maarten Grootendorst. 2022. BERTopic: Neural topic modeling with a class-based TF-IDF procedure . arXiv preprint arXiv:2203.05794

    arXiv 2022

  8. [8]

    Zhijiang Guo, Michael Schlichtkrull, and Andreas Vlachos. 2022. A Survey on Automated Fact-Checking . Transactions of the Association for Computational Linguistics, 10:178--206

    work page 2022

Show all 40 references
  1. [9]

    Jonathan Heawood. 2018. Pseudo-public political speech: Democratic implications of the Cambridge Analytica scandal . Information Polity, 23:429--434

    2018

  2. [10]

    Howard and Bence Kollanyi

    Philip N. Howard and Bence Kollanyi. 2016. Bots, \#StrongerIn, and \#Brexit: Computational Propaganda during the UK-EU Referendum . arXiv preprint arXiv:1606.06356

    2016 arXiv

  3. [11]

    Carina Jacobi, Wouter van Atteveldt, and Kasper Welbers. 2016. Quantitative analysis of large amounts of journalistic texts using topic modelling. Digital Journalism, 4(1):89--106

    2016

  4. [12]

    Shaleen Khanal, Hongzhou Zhang, and Araz Taeihagh. 2025. Why and how is the power of Big Tech increasing in the policy process? The case of generative AI . Policy and Society, 44:52--69

    2025

  5. [13]

    Philip Leifeld. 2016. Discourse network analysis: Policy debates as dynamic networks. pages 301--326

    2016

  6. [14]

    Daniel Loureiro, Francesco Barbieri, Leonardo Neves, Luis Espinosa Anke, and Jose Camacho-collados. 2022. TimeLMs: Diachronic Language Models from Twitter . In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pages...

    2022

  7. [15]

    Leland McInnes, John Healy, and Steve Astels. 2017. hdbscan: Hierarchical density based clustering. The Journal of Open Source Software, 2(11):205

    2017

  8. [16]

    Leland McInnes, John Healy, Nathaniel Saul, and Lukas Grossberger. 2018. UMAP: Uniform Manifold Approximation and Projection . The Journal of Open Source Software, 3(29):861

    2018

  9. [17]

    Fred Morstatter, J \"u rgen Pfeffer, Huan Liu, and Kathleen M. Carley. 2013. Is the Sample Good Enough? Comparing Data from Twitter’s Streaming API with Twitter’s Firehose . In Proceedings of the Seventh International AAAI Conference on Weblogs and Social Media (ICWSM 2013), p...

    2013

  10. [18]

    Cas Mudde. 2019. The Far Right Today. Polity

    2019

  11. [19]

    Preslav Nakov, David Corney, Maram Hasanain, Firoj Alam, Tamer Elsayed, Alberto Barrón-Cedeño, Paolo Papotti, Shaden Shaar, and Giovanni Da San Martino. 2021. Automated Fact-Checking for Assisting Human Fact-Checkers . In Proceedings of the Thirtieth International Joint Confer...

    2021

  12. [20]

    Mark Newman. 2018. Networks: An Introduction, reprinted edition. Oxford University Press, Oxford

    2018

  13. [21]

    Stefan R \"a biger and Myra Spiliopoulou. 2015. A framework for validating the merit of properties that predict the influence of a twitter user. Expert Systems with Applications, 42(5):2824--2834

    2015

  14. [22]

    Nils Reimers and Iryna Gurevych. 2019. Sentence- BERT : Sentence Embeddings using S iamese BERT -Networks . In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNL...

    2019

  15. [23]

    Manoel Horta Ribeiro, Raphael Ottoni, Robert West, Virg\' lio A. F. Almeida, and Wagner Meira. 2020. Auditing radicalization pathways on YouTube . In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, page 131–141, New York, NY, USA

    2020

  16. [24]

    Martin Riedl. 2024. Political deepfakes and misleading chatbots: understanding the use of genai in recent european elections. Center for Media Engagement (The University of Texas at Austin)

    2024

  17. [25]

    Anna Schmidt and Michael Wiegand. 2017. A Survey on Hate Speech Detection using Natural Language Processing . In Proceedings of the Fifth International Workshop on Natural Language Processing for Social Media, pages 1--10, Valencia, Spain

    2017

  18. [26]

    Juan Carlos Medina Serrano, Morteza Shahrezaye, Orestis Papakyriakopoulos, and Simon Hegelich. 2019. The rise of germany's afd: A social media analysis. In Proceedings of the 10th International Conference on Social Media and Society, SMSociety '19, page 214–223, New York, NY, ...

    2019

  19. [27]

    Kirill Solovev and Nicolas Pr\" o llochs. 2022. Hate Speech in the Political Discourse on Social Media: Disparities Across Parties, Gender, and Ethnicity . In Proceedings of the ACM Web Conference 2022, WWW '22, page 3656–3661, Virtual Event, Lyon, France

    2022

  20. [28]

    Megha Sundriyal, Tanmoy Chakraborty, and Preslav Nakov. 2023. From Chaos to Clarity: Claim Normalization to Empower Fact-Checking . In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 6594--6609, Singapore

    2023

  21. [29]

    Kalicharan Veera Singam and Raffaello Pantucci. 2024. Extreme Right-Wing in the West . Counter Terrorist Trends and Analyses, 16(1):pp. 106--111

    2024

  22. [30]

    Bertie Vidgen, Alex Harris, Dong Nguyen, Rebekah Tromble, Scott Hale, and Helen Margetts. 2019. Challenges and frontiers in abusive content detection . In Proceedings of the Third Workshop on Abusive Language Online, pages 80--93, Florence, Italy

    2019

  23. [31]

    Bertie Vidgen, Tristan Thrush, Zeerak Waseem, and Douwe Kiela. 2021. Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection . In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International...

    2021

  24. [32]

    Andreas Vlachos and Sebastian Riedel. 2014. Fact Checking: Task definition and dataset construction . In Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science , pages 18--22, Baltimore, MD, USA

    2014

  25. [33]

    Michael Wiegand, Josef Ruppenhofer, Anna Schmidt, and Christine Greenberg. 2018 a . Inducing a Lexicon of Abusive Words --- A Feature-Based Approach . In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human La...

    2018

  26. [34]

    Michael Wiegand, Melanie Siegel, and Josef Ruppenhofer. 2018 b . Overview of the GermEval 2018 Shared Task on the Identification of Offensive Language . In Proceedings of GermEval 2018, 14th Conference on Natural Language Processing (KONVENS 2018), pages 1 -- 10, Vienna, Austria

    2018

  27. [35]

    Sifan Xu and Ying Xiong. 2020. Setting socially mediated engagement parameters: A topic modeling and text analytic approach to examining polarized discourses on Gillette’s campaign . Public Relations Review, 46(5):1--12

    2020

  28. [36]

    Seid Muhie Yimam, Daryna Dementieva, Tim Fischer, Daniil Moskovskiy Skoltech, Naquee Rizwan, Iit Kharagpur, Punyajoy Saha, Sarthak Roy, Martin Semmann, Alexander Panchenko, Chris Biemann, and Animesh Mukherjee. 2024. Demarked: A Strategy for Enhanced Abusive Speech Moderation ...

    2024 arXiv

  29. [37]

    Savvas Zannettou, Tristan Caulfield, Emiliano De Cristofaro, Nicolas Kourtelris, Ilias Leontiadis, Michael Sirivianos, Gianluca Stringhini, and Jeremy Blackburn. 2017. The Web Centipede: Understanding How Web Communities Influence Each Other Through the Lens of Mainstream and ...

    2017

  30. [38]

    Özge Sevgili, Irina Nikishina, Seid Muhie Yimam, Martin Semmann, and Chris Biemann. 2024. UHH at AVeriTeC: RAG for Fact-Checking with Real-World Claims . pages 55--63, Stroudsburg, PA, USA

    2024

  31. [39]

    online" 'onlinestring :=

    ENTRY address archivePrefix author booktitle chapter edition editor eid eprint eprinttype howpublished institution journal key month note number organization pages publisher school series title type volume year doi pubmed url lastchecked label extra.label sort.label short.list...

  32. [40]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 gl...

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Reviewed August 7, 2026 · model on record in the stance chip above.