Analyzing the Semantic Types of Claims and Premises in an Online Persuasive Forum

Argumentative text has been analyzed both theoretically and computationally in terms of argumentative structure that consists of argument components (e.g., claims, premises) and their argumentative relations (e.g., support, attack). Less emphasis has been placed on analyzing the semantic types of argument components. We propose a two-tiered annotation scheme to label claims and premises and their semantic types in an online persuasive forum, Change My View, with the long-term goal of understanding what makes a message persuasive. Premises are annotated with the three types of persuasive modes: ethos, logos, pathos, while claims are labeled as interpretation, evaluation, agreement, or disagreement, the latter two designed to account for the dialogical nature of our corpus. We aim to answer three questions: 1) can humans reliably annotate the semantic types of argument components? 2) are types of premises/claims positioned in recurrent orders? and 3) are certain types of claims and/or premises more likely to appear in persuasive messages than in non-persuasive messages?

[1]  Lei Zhang,et al.  Sentiment Analysis and Opinion Mining , 2017, Encyclopedia of Machine Learning and Data Mining.

[2]  Yan Shao,et al.  Ethos, Logos, Pathos: Strategies of Persuasion in Social/Environmental Reports , 2013 .

[3]  Cristian Danescu-Niculescu-Mizil,et al.  Winning Arguments: Interaction Dynamics and Persuasion Strategies in Good-faith Online Discussions , 2016, WWW.

[4]  Trevor J. M. Bench-Capon,et al.  Argument Schemes for Reasoning About the Actions of Others , 2016, COMMA.

[5]  Iryna Gurevych,et al.  Which argument is more convincing? Analyzing and predicting convincingness of Web arguments using bidirectional LSTM , 2016, ACL.

[6]  Chris Reed,et al.  Argument Mining Using Argumentation Scheme Structures , 2016, COMMA.

[7]  Jacob Andreas,et al.  Detecting Influencers in Written Online Conversations , 2012 .

[8]  Marilyn A. Walker,et al.  Argument Strength is in the Eye of the Beholder: Audience Effects in Persuasion , 2017, EACL.

[9]  Claire Cardie,et al.  Toward machine-assisted participation in eRulemaking: an argumentation model of evaluability , 2015, ICAIL.

[10]  Chris Reed,et al.  Mining Ethos in Political Debate , 2016, COMMA.

[11]  Iryna Gurevych,et al.  Annotating Argument Components and Relations in Persuasive Essays , 2014, COLING.

[12]  Iryna Gurevych,et al.  Argumentation Mining in User-Generated Web Discourse , 2016, CL.

[13]  Yi Li,et al.  Is This Post Persuasive? Ranking Argumentative Comments in Online Forum , 2016, ACL.

[14]  Patrick Saint-Dizier,et al.  A Model for Processing Illocutionary Structures and Argumentation in Debates , 2014, LREC.

[15]  Anette Frank,et al.  Argumentative texts and clause types , 2016, ArgMining@ACL.

[16]  James B. Freeman,et al.  What Types of Statements are There? , 2000 .

[17]  Abhimanyu Das,et al.  Information dissemination in heterogeneous-intent networks , 2016, WebSci.

[18]  J. Anscombre,et al.  L'argumentation dans la langue , 1976 .

[19]  Iryna Gurevych,et al.  Identifying Argumentative Discourse Structures in Persuasive Essays , 2014, EMNLP.

[20]  A. Peldszus An Annotated Corpus of Argumentative Microtexts , 2015 .

[21]  Marilyn A. Walker,et al.  A Corpus for Research on Deliberation and Debate , 2012, LREC.

[22]  Klaus Krippendorff,et al.  Estimating the Reliability, Systematic Error and Random Error of Interval Data , 1970 .

[23]  Debanjan Ghosh,et al.  Coarse-grained Argumentation Features for Scoring Persuasive Essays , 2016, ACL.

[24]  Ch. Perelman,et al.  The New Rhetoric: A Treatise on Argumentation , 1971 .

[25]  Sara Rosenthal,et al.  Detecting Influencers in Multiple Online Genres , 2017, ACM Trans. Internet Techn..

[26]  F. H. van Eemeren,et al.  Examining argumentation in context : fifteen studies on strategic maneuvering , 2009 .

[27]  Jan Nuyts,et al.  Notions of (inter)subjectivity , 2012 .

[28]  Maria Liakata,et al.  Multi-label Annotation in Scientific Articles - The Multi-label Cancer Risk Assessment Corpus , 2016, LREC.

[29]  Chris Reed,et al.  Speech Acts of Argumentation: Inference Anchors and Peripheral Cues in Dialogue , 2011, Computational Models of Natural Argument.