Towards Argument Mining for Social Good: A Survey

Abstract

This survey builds an interdisciplinary picture of Argument Mining (AM), with a strong focus on its potential to address issues related to Social and Political Science. More specifically, we focus on AM challenges related to its applications to social media and in the multilingual domain, and then proceed to the widely debated notion of argument quality. We propose a novel definition of argument quality which is integrated with that of deliberative quality from the Social Science literature. Under our definition, the quality of a contribution needs to be assessed at multiple levels: the contribution itself, its preceding context, and the consequential effect on the development of the upcoming discourse. The latter has not received the deserved attention within the community. We finally define an application of AM for Social Good: (semi-)automatic moderation, a highly integrative application which (a) represents a challenging testbed for the integrated notion of quality we advocate, (b) allows the empirical quantification of argument/deliberative quality to benefit from the developments in other NLP fields (i.e. hate speech detection, fact checking, debiasing), and (c) has a clearly beneficial potential at the level of its societal thanks to its real-world application (even if extremely ambitious).

Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: Tutorial Abstracts

“…Tutorial Type: Introductory, 6 hours Tutorial Materials: Tutorial materials and related information will be made available online. 9 Recommended Reading List Survey Papers (Cabrio and Villata, 2018;Lawrence and Reed, 2019;Vecchi et al, 2021) Mining Arguments (Habernal and Gurevych, 2017;Daxenberger et al, 2017;Dusmanu et al, 2017b) Assessing Argument Quality Lauscher et al, 2020;Marro et al, 2022) Assessing Deliberative Quality (Steenbergen et al, 2003;Gerber et al, 2016) Improving Arguments (Hua and Wang, 2018;Alshomary et al, 2020;Gurcke et al, 2021) Challenges (Durmus et al, 2019;Toledo-Ronen et al, 2020;Spliethöver and Wachsmuth, 2020)…”

HyEnA: A Hybrid Method for Extracting Arguments from Opinions

“…Further, some preliminary work adjusts visualization for minority opinions (Baumer et al, 2022). However, in terms of data sources, most work is still centered on Englishspeaking content, with few multi-lingual or multicultural resources available (Vecchi et al, 2021). to key points.…”

Opening up Minds with Argumentative Dialogues

“…the aim of reaching agreement (Vecchi et al, 2021;De Kock and Vlachos, 2021). Nonetheless, none of the previous works has studied dialogues in terms of their ability to stimulate open-minded thinking and help participants learn about views that are unfamiliar or in opposition to their own and become more tolerant towards people who hold these views.…”