In online debates, as in offline ones, individual utterances or arguments support or attack each other, leading to some subset of arguments winning (potentially from different sides of the debate). However, online conversations are much larger in scale than offline ones, with often hundreds of thousands of users weighing in, and so, readers are often forced to sample a subset of the arguments or arguments being put forth. Since such sampling is rarely done in a principled manner, users may not get all the relevant "winning" arguments to get a full picture of the debate from a sample. This paper is interested in answering the question of how users should sample online conversations to selectively favour winning arguments. We apply techniques from argumentation theory and complex networks to build a model that predicts the probabilities of the normatively winning arguments given their location in idealised online discussions. Online discussions are modeled as reply networks where nodes represent the comments exchanged and directed edges represent replies that can either be supporting or attacking. Our model shows that the proportion of replies that are supporting in the network, the network's in-degree distribution and the locations of unrebutted arguments ("the last words") all determine the behaviour of the probability that a comment is a winning argument given its location. This is also verified with data scraped from the online debating platform Kialo. In predicting the locations of the winning arguments in reply networks, we can therefore suggest which arguments a reader should sample and read if he or she would like to grasp the winning opinions in such discussions. Our models have important implications for the design of future online discussion platforms.
Further, @cite_41 @cite_30 have applied techniques of argument mining and evaluation to , http: www.debatepedia.org en index.php Welcome_to_Debatepedia
@cite_41 @cite_30
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The goal of argumentation mining, an evolving research field in computational linguistics, is to design methods capable of analyzing people's argumentation. In this article, we go beyond the state of the art in several ways. i We deal with actual Web data and take up the challenges given by the variety of registers, multiple domains, and unrestricted noisy user-generated Web discourse. ii We bridge the gap between normative argumentation theories and argumentation phenomena encountered in actual data by adapting an argumentation model tested in an extensive annotation study. iii We create a new gold standard corpus 90k tokens in 340 documents and experiment with several machine learning methods to identify argument components. We offer the data, source codes, and annotation guidelines to the community under free licenses. Our findings show that argumentation mining in user-generated Web discourse is a feasible but challenging task. Analyzing arguments in user-generated Web discourse has recently gained attention in argumentation mining, an evolving field of NLP. Current approaches, which employ fully-supervised machine learning, are usually domain dependent and suffer from the lack of large and diverse annotated corpora. However, annotating arguments in discourse is costly, error-prone, and highly context-dependent. We asked whether leveraging unlabeled data in a semi-supervised manner can boost the performance of argument component identification and to which extent is the approach independent of domain and register. We propose novel features that exploit clustering of unlabeled data from debate portals based on a word embeddings representation. Using these features, we significantly outperform several baselines in the cross-validation, cross-domain, and cross-register evaluation scenarios. Argument mining studies in natural language text often use lexical (e.g. n-grams) and syntactic (e.g. grammatical production rules) features with all possible values. In prior work on a corpus of academic essays, we demonstrated that such large and sparse feature spaces can cause difficulty for feature selection and proposed a method to design a more compact feature space. The proposed feature design is based on post-processing a topic model to extract argument and domain words. In this paper we investigate the generality of this approach, by applying our methodology to a new corpus of persuasive essays. Our experiments show that replacing n-grams and syntactic rules with features and constraints using extracted argument and domain words significantly improves argument mining performance for persuasive essays. In this paper, we argue that an annotation scheme for argumentation mining is a function of the task requirements and the corpus properties. There is no one-size-fits-all argumentation theory to be applied to realistic data on the Web. In two annotation studies, we experiment with 80 German newspaper editorials from the Web and about one thousand English documents from forums, comments, and blogs. Our example topics are taken from the educational domain. To formalize the problem of annotating arguments, in the first case, we apply a Claim-Premise scheme, and in the second case, we modify Toulmin's scheme. We find that the choice of the argument components to be annotated strongly depends on the register, the length of the document, and inherently on the literary devices and structures used for expressing argumentation. We hope that these findings will facilitate the creation of reliably annotated argumentation corpora for a wide range of tasks and corpus types and will help to bridge the gap between argumentation theories and actual application needs.