Mining argumentative patterns in context. A large scale corpus study of Earnings Conference Calls of listed companies
People
(Responsible)
External people
Reed Chris
(Co-responsible)
Abstract
The proposed research is a large-scale study of argumentative patterns in a corpus of quarterly Earnings Conference Calls (ECCs), a key dialogical genre in the financial communication of listed companies. Traditionally, the study of argumentation in context has relied on the analytical reconstruction of individual discourses examined in relation to descriptions of activity types in order to outline how the goals, incentives and procedures of activities constrain the issues and the material and procedural starting points of argumentative discussions. Recently, researchers have advocated a shift towards larger corpus studies not only to test and refine hypotheses on the contextual constraints on argumentation, but also to map inherently more complex networks of arguments in multi-party discussions that shape broader debates in society. This shift is also necessary in order to start addressing the effects of argumentation on the context itself. Corpus research on argumentation, however, requires the development of theory-based annotation schemes and time-consuming annotation by trained analysts, which risks being an insurmountable bottleneck. Furthermore, it requires quantitative analytics to relate analyzed arguments to contextual parameters and, if the effects of argumentation are to be addressed, the possibility to measure change in contextual parameters at appropriate points in time. Recourse to Argumentation Mining and related Argumentation Analytics appears promising, but tools and techniques developed in this growing field have so far seen limited application to research on discourse in context. The project seeks to demonstrate how these challenges can be met with an Argumentation Mining approach designed to investigate the interdependency of argumentation and activity type through the notion of argumentative pattern (AP), which refers to significant constellations of argumentative moves whose occurrence can be explained in view of the goals and rules of the activity type.
Additional information
Publications
- Palmieri R., Lucchini C., D'Agostino G., Rocci A. (2025) Argumentation in shareholder activism: Context, issues, and argumentative patterns, Journal of Argumentation in Context
- D'Agostino G., Schad E., Maguire E., Lucchini C., Rocci A., Reed C. (2024) Superquestions and some ways to answer them, Journal of Argumentation in Context, 13 (3)
- D'Agostino G., Laskin A. V. (2024) Proactive adoption of LLM-based tools for performance enhancement by banking institutions: Disclosure, ethical concerns, and public reception. EUPRERA 2024. Bucharest. September 2024
- D'Agostino G., Rocci A. (2024) Argumentative patterns in the context of dialogical exchanges in the financial domain. Proceedings of the 24th Edition of the Workshop on Computational Models of Natural Argument. CEUR. CMNA24. Hagen, Germany. September 2024
- D'Agostino G., Reed C., Puccinelli D. (2024) Segmentation of Complex Question Turns for Argument Mining: A Corpus-based Study in the Financial Domain. Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024). ELRA and ICCL. Torino, Italia
- D'Agostino G., Lucchini C., Rocci A. (2024) Strategies are for boys; numbers are for women.. VALS-ASLA Conference 2024. Asymmetries and Inequalities in Language. Bern. February 2024
- Rocci A., Yaskorska-Shah O., D'Agostino G., Lucchini C. (2024) Argumentative patterns initiated by closed-list questions in accountability dialogues. A corpus study of financial conference calls. European Conference on Argumentation (ECA). ECA 2022. Roma. 2022
- Lucchini C., D'Agostino G. (2023) Good answers, better questions: building an annotation scheme for financial dialogues.
- D'Agostino G. (2023) Let's explain what we argue for: The argumentative function of explanations in Earnings Conference Calls. Proceedings of the 23rd Edition of the Workshop on Computational Models of Natural Argument. CEUR. CMNA23. virtual event. December 2023
- D'Agostino G., Rocci A., Reed C. (2023) Let's explain what we argue for: The argumentative function of explanations in Earnings Conference Calls. Proceedings of the 23rd Edition of the Workshop on Computational Models of Natural Argument. CMNA23. virtual event. December 2023
- D'Agostino G., Younis R., Konat B., Sviatsilnikava Y., Gajewska E. a. B. (2023) Robin-Hooding Language of Polarisation. Linguistically Analysing Polarisation on Social Media. The New Ethos Reports
- D'Agostino G. (2023) Opposition Without Argumentation. Online Handbook of Argumentation for AI. ArXiv
- D'Agostino G., Lucchini C. (2023) Do you think this? Constructing and suggesting preferable standpoints in questions. ISSA 2023. Leiden. July 2023
- D'Agostino G., Lucchini C., Rocci A. (2023) Transformation, exploitation, and complication of transcribed calls in the financial domain : the case of Earnings Conference Calls examined in the argumentative perspective. AILA 20th World Congress. Lyon. July 2023
- Lucchini C., Rocci A., D'Agostino G. (2022) Annotating argumentation within questions. Prefaced questions as a genre specific argumentative pattern in earnings conference calls. Proceedings of the 22nd Edition of the Workshop on Computational Models of Natural Argument. CMNA. Cardiff. 2022
- Lucchini C., Rocci A., D'Agostino G. (2022) Annotating argumentation within questions. Prefaced questions as a genre specific argumentative pattern in earnings conference calls. Proceedings of the 22nd Edition of the Workshop on Computational Models of Natural Argument. CMNA. Cardiff. 2022
- D'Agostino G. (2022) Argumentation without Opposition?. Online Handbook of Argumentation for AI. ArXiv
- D'Agostino G. (2022) "(so long, and) thanks for all the color". Requests of Elaboration and Answers They Trigger in Earnings Conference Calls. Proceedings of the 22nd Edition of the Workshop on Computational Models of Natural Argument. CEUR. CMNA22. Cardiff. 2022