Towards Argumentative Ranking

Organizers:

Chiraz Latiri, Julio Gonzalo, Malek Hajjem

Task 2 participation deadline April 30, 2018

Argumentative Ranking of Microblogs

Argumentation mining is a new problem in corpus-based text analysis that addresses the challenging task of automatically identifying the justifications provided by opinion holders for their judgment. Several approaches of argumentation mining have been proposed so far in areas such as legal documents, on-line debates, product reviews, newspaper articles and court cases, as well as in dialogical domains.
With the popularization of social networks, argumentation mining is considered as an extension of the opinion mining issue from social network content. The aim is to automatically identify reason-conclusion structures that can lead to model social web user’s positions about a service or an event expressed through social networks platforms like Twitter. Indeed, when we need to form an opinion on a new topic or make a decision, arguments will be all what we are looking for.
To make argumentation structures available, in case of Twitter, a robust automatic recognition of it is required, based on resources that should be created in a reproducible fashion to be reliable. However, the ambiguity of natural language text produced in social media, with different writing styles, implicit context and heterogeneous content make argumentation, on Twitter, very challenging.

Another possible way to pick up the argumentation structures, from a generic tweet corpus, is to use approaches based on information extraction. The idea is to perform a search process that focus on claims about a given topic out in a massive collection. This approach relates to the field of focused retrieval, that aims to provide users with direct access to relevant information in retrieved documents. In this task, relevant information is expressed in the form of arguments. [1]

Success of such argumentation ranking will require interdisciplinary approaches based on the combination of different research issues. In fact, to better understand a short text and be able to detect the argumentative structures within a microblog, we could restore a « text contextualization » as a way to provide more information on the corresponding text [2]. Providing such information in order to detect argumentative tweets, would highlight relevant ones, in other words, tweets expressed in the form of arguments. Thus, argumentation mining in this situation will tend to act in the same way of an Information Retrieval (IR) system where potential argumentative tweets had to come first. Similar approach that addresses such purpose is presented in [3], where the output of the priority task will be a ranking of tweets according to their probability of being a potential threat to the reputation of some entity.

[1] Argumentative Ranking
Marco Lippi and Paolo Sarti and Paolo Torroni DISI - Universita degli Studi di Bologna Proceedings of Natural Language Processing meets Journalism - IJCAI-16 Workshop (NLPMJ 2016), New York, (July 2016)
[2] INEX Tweet Contextualization task : Evaluation, results and lesson learned
Patrice Bellot, Véronique Moriceau, Josiane Mothe, Eric SanJuan, Xavier Tannier:- Inf. Process. Manage. 52(5): 801-819 (2016)
[3] Overview of RepLab 2013: Evaluating Online Reputation Monitoring Systems
Enrique Amigo, Jorge Carrillo de Albornoz, Irina Chugur, Adolfo Corujo Julio Gonzalo, Tamara Martin, Edgar Meij Maarten de Rijke and Damiano Spina