Argumentation Mining in User-Generated Web Discourse

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.Comment: Cite as: Habernal, I. & Gurevych, I. (2017). Argumentation Mining in User-Generated Web Discourse. Computational Linguistics 43(1), pp. 125-17

Conventions and mutual expectations — understanding sources for web genres

Genres can be understood in many different ways. They are often perceived as a primarily sociological construction, or, alternatively, as a stylostatistically observable objective characteristic of texts. The latter view is more common in the research field of information and language technology. These two views can be quite compatible and can inform each other; this present investigation discusses knowledge sources for studying genre variation and change by observing reader and author behaviour rather than performing analyses on the information objects themselves

A New application of Social Impact in Social Media for overcoming fake news in health

One of the challenges today is to face fake news (false information) in health due to its potential impact on people's lives. This article contributes to a new application of social impact in social media (SISM) methodology. This study focuses on the social impact of the research to identify what type of health information is false and what type of information is evidence of the social impact shared in social media. The analysis of social media includes Reddit, Facebook, and Twitter. This analysis contributes to identifying how interactions in these forms of social media depend on the type of information shared. The results indicate that messages focused on fake health information are mostly aggressive, those based on evidence of social impact are respectful and transformative, and finally, deliberation contexts promoted in social media overcome false information about health. These results contribute to advancing knowledge in overcoming fake health-related news shared in social media

Redefining media agendas: topic problematization in online reader comments

Media audiences representing a significant portion of the public in any given country may hold opinions on media-generated definitions of social problems which differ from those of media professionals. The proliferation of online reader comments not only makes such opinions available but also alters the process of agenda formation and problem definition in the public space. Based on a dataset of 33,877 news items and 258,121 comments from a sample of regional Russian newspapers we investigate readers' perceptions of social problems. We find that the volume of attention paid to issues or topics by the media and the importance of those issues for audiences, as judged by the number of their comments, diverge. Further, while the prevalence of general negative sentiment in comments accompanies such topics as disasters and accidents that are not perceived as social problems, a high level of sentiment polarization in comments does suggest issue problematization. It is also positively related to topic importance for the audience. Thus, instead of finding fixed social problem definitions in the reader comments, we observe the process of problem formation, where different points of view clash. These perceptions are not necessarily those expressed in media texts since the latter are predominantly “hard” news covering separate events, rather than trends or issues. As our research suggests, problematization emerges from readers’ background knowledge, external experience, or values