Bhuvanesh Verma

@inproceedings{Verma:Mehler:2026,
  title     = {Predicting Topic (Co-)Occurrence Using Topic Networks Built from
               the Project Gutenberg Corpus},
  booktitle = {Proceedings of the 15th International Conference on Language Resources
               and Evaluation (LREC 2026)},
  year      = {2026},
  author    = {Verma, Bhuvanesh and Mehler, Alexander},
  keywords  = {Topic Evolution, Topic Network,Time-aware Networks, Temporal Autocorrelation, Project Gutenberg, satek},
  abstract  = {Although temporal topic modeling has been widely applied to scientific
               and legal texts, literary corpora have largely been overlooked
               in this regard. To address this issue, we analyze topic evolution
               in a subset of the Project Gutenberg (PG) corpus. We model this
               subset as a sequence of topic networks that capture the emergence,
               persistence, and interaction of thematic structures over decades.
               Using supervised topic representations, we predict nodes (topics)
               and edges (topic pairings) to forecast future topics and their
               co-occurrence. Our experiments demonstrate moderate to strong
               temporal persistence in topic connectivity patterns across three
               topic systems, with ROC-AUC and AP values consistently above 0.85.
               We find that the temporal span of topic networks significantly
               impacts predictive performance: longer spans improve the stability
               and recall of topic presence, while shorter spans better capture
               evolving topic relationships. Overall, our findings demonstrate
               the predictability of topics in literary texts over time.},
  note      = {accepted}
}

@inproceedings{Verma:et:al:2026,
  title     = {Predicting Convincingness in Political Speech: How Emotional Tone
               Shapes Persuasive Strength},
  booktitle = {Proceedings of the 15th Workshop on Computational Approaches to
               Subjectivity, Sentiment, \& Social Media Analysis},
  year      = {2026},
  author    = {Verma, Bhuvanesh and Marreddy, Mounika and Mehler, Alexander},
  keywords  = {Argument Detection, Argument Quality Assessment,Topic Modelling, Persuasiveness, Convincingness, Emotion Analysis, Argument Mining},
  abstract  = {Emotional tone plays a central role in persuasion, yet its impact
               on computational assessments of political argument quality in
               real world election campaign speeches remains understudied. In
               this work, we investigate whether positive emotional framing correlates
               with higher perceived convincingness in political arguments. We
               fine-tune language models on argument quality datasets and test
               their ability to transfer convincingness predictions to real-world
               campaign speeches. Using a corpus of U.S. presidential campaign
               speeches, we analyze emotional polarity in relation to predicted
               persuasive strength to test whether positively framed arguments
               are judged more convincing than neutral or negative ones. Our
               empirical analysis shows that political parties rely heavily on
               argumentation during their election campaigns. Also, we found
               the evidence that politicians strategically employ emotional cues
               within their arguments during these campaign speeches, with positive
               emotions being more strongly associated with persuasive strength,
               for example in topics such as USMCA’s Effect on American Jobs
               and Agriculture, Border Control Policies, Progressive Tax Reforms.
               At the same time, we find that negative emotions have a weaker
               yet still non-negligible influence on voter persuasion in topics
               such as City Crime and Civil Unrest and White Supremacist Violence
               (Charlottesville Incident).},
  note      = {accepted}
}

@article{Owoyele:et:al:2020,
  title     = {Socio-Semantic X-Ray of Multi-Actor Constellations using Topics
               and Interstitial Authors: A Toolkit for Augmenting Computational
               Literature Reviews},
  author    = {Owoyele, Babajide and Verma, Bhuvanesh and Omolaoye, Victor and Edelman, Jonathan Antonio
               and Loorbach, Derk and de Melo, Gerard},
  journal   = {Available at SSRN 4713155},
  doi       = {10.2139/ssrn.4713155},
  url       = {https://dx.doi.org/10.2139/ssrn.4713155},
  year      = {2024}
}

@misc{Owoyele:et:al:2024,
  title     = {MaskAnyone Toolkit: Offering Strategies for Minimizing Privacy
               Risks and Maximizing Utility in Audio-Visual Data Archiving},
  author    = {Babajide Alamu Owoyele and Martin Schilling and Rohan Sawahn and Niklas Kaemer
               and Pavel Zherebenkov and Bhuvanesh Verma and Wim Pouw and Gerard de Melo},
  year      = {2024},
  eprint    = {2408.03185},
  archiveprefix = {arXiv},
  primaryclass = {cs.CR},
  url       = {https://arxiv.org/abs/2408.03185}
}

@misc{Verma:Raithel:2024,
  title     = {DFKI-NLP at SemEval-2024 Task 2: Towards Robust LLMs Using Data
               Perturbations and MinMax Training},
  author    = {Bhuvanesh Verma and Lisa Raithel},
  year      = {2024},
  eprint    = {2405.00321},
  archiveprefix = {arXiv},
  primaryclass = {cs.CL},
  url       = {https://arxiv.org/abs/2405.00321}
}

@inproceedings{Raithel:et:al:2024,
  title     = {Overview of {\#}{SMM}4{H} 2024 {--} Task 2: Cross-Lingual Few-Shot
               Relation Extraction for Pharmacovigilance in {F}rench, {G}erman,
               and {J}apanese},
  author    = {Raithel, Lisa and Thomas, Philippe and Verma, Bhuvanesh and Roller, Roland
               and Yeh, Hui-Syuan and Yada, Shuntaro and Grouin, Cyril and Wakamiya, Shoko
               and Aramaki, Eiji and M{\"o}ller, Sebastian and Zweigenbaum, Pierre},
  editor    = {Xu, Dongfang and Gonzalez-Hernandez, Graciela},
  booktitle = {Proceedings of The 9th Social Media Mining for Health Research
               and Applications (SMM4H 2024) Workshop and Shared Tasks},
  month     = {aug},
  year      = {2024},
  address   = {Bangkok, Thailand},
  publisher = {Association for Computational Linguistics},
  url       = {https://aclanthology.org/2024.smm4h-1.39/},
  pages     = {170--182},
  abstract  = {This paper provides an overview of Task 2 from the Social Media
               Mining for Health 2024 shared task ({\#}SMM4H 2024), which focused
               on Named Entity Recognition (NER, Subtask 2a) and the joint task
               of NER and Relation Extraction (RE, Subtask 2b) for detecting
               adverse drug reactions (ADRs) in German, Japanese, and French
               texts written by patients. Participants were challenged with a
               few-shot learning scenario, necessitating models that can effectively
               generalize from limited annotated examples. Despite the diverse
               strategies employed by the participants, the overall performance
               across submissions from three teams highlighted significant challenges.
               The results underscored the complexity of extracting entities
               and relations in multi-lingual contexts, especially from the noisy
               and informal nature of user-generated content. Further research
               is required to develop robust systems capable of accurately identifying
               and associating ADR-related information in low-resource and multilingual
               settings.}
}

@misc{Binder:et:al:2022,
  title     = {Full-Text Argumentation Mining on Scientific Publications},
  author    = {Arne Binder and Bhuvanesh Verma and Leonhard Hennig},
  year      = {2022},
  eprint    = {2210.13084},
  archiveprefix = {arXiv},
  primaryclass = {cs.CL},
  url       = {https://arxiv.org/abs/2210.13084}
}

@article{Paul:et:al:2020,
  title     = {Estimating electrification using multi-temporal DMSP/OLS night
               imagery as proxy measure of human well-being in India},
  author    = {Paul, Arati and Verma, Bhuvanesh and Chakraborty, Debasish},
  journal   = {Spatial Information Research},
  volume    = {28},
  issn      = {2366-3294},
  pages     = {469--473},
  year      = {2020},
  url       = {http://dx.doi.org/10.1007/s41324-019-00307-8},
  doi       = {10.1007/s41324-019-00307-8},
  publisher = {Springer}
}