Argument Mining on Twitter: Arguments, Facts and Sources

Abstract

Social media collect and spread on the Web personal opinions, facts, fake news and all kind of information users may be interested in. Applying argument mining methods to such heterogeneous data sources is a challenging open research issue, in particular considering the peculiarities of the language used to write textual messages on social media. In addition, new issues emerge when dealing with arguments posted on such platforms, such as the need to make a distinction between personal opinions and actual facts, and to detect the source disseminating information about such facts to allow for provenance verification. In this paper, we apply supervised classification to identify arguments on Twitter, and we present two new tasks for argument mining, namely facts recognition and source identification. We study the feasibility of the approaches proposed to address these tasks on a set of tweets related to the Grexit and Brexit news topics.

The evolution of argumentation mining: From models to social media and emerging tools

“…Concerning the feature selection and their impact for the classification task, the use of all the possible features performs better in each case. The selection of the classification algorithm does not seem to affect significantly the performance of the task in [63], whereas in [3] the use of SVM surpasses the alternative algorithms. For the task of argument detection, the best results are achieved in [60] with 0.89 F1, whereas [63] and [61] achieve 0.78 F1 and 0.67 F1 respectively.…”

“…The selection of the classification algorithm does not seem to affect significantly the performance of the task in [63], whereas in [3] the use of SVM surpasses the alternative algorithms. For the task of argument detection, the best results are achieved in [60] with 0.89 F1, whereas [63] and [61] achieve 0.78 F1 and 0.67 F1 respectively. The results of the semisupervised approaches are measured in terms of ranking quality either using the Normalized Discounted Cumulative Gain (NDCG) either simply presenting qualitative results.…”

“…The task of argument detection as a necessary preliminary step takes place in the work of Addawood and Bashir [3] and also in Dusmanu et al [63], where the proposed pipelines end up in the recognition of evidence type and in the source identification respectively. In both works the detection of argumentative tweets is necessary, as the non-argumentative tweets consist respectively the 42.3% and the 29.3% of the constructed datasets.…”

“…The DART dataset [79] was used as input of the pipeline, containing 2702 argumentative tweets and 1181 non- [80,81] argumentative tweets. In contrast to [3,63], Bosc et al [61] measured the IAA in terms of Krippendorff's alpha, resulting to the satisfactory a=0.81.…”

“…RNN = Recurrent Neural Network where h i is the person who holds a specific stance s ijkl for a specific debate d l , justified by a rationale r k in a specific time t l . The rationale of the stance for a specific debate can be assessed for its quality [63,3] through a variety of sub-tasks, such as facts identification, evidence recognition, source classification and reasoning evaluation.…”