Thesis Proposal Using Argument Analysis to Define a Structure for User Generated Content

Conversation is how people compare ideas, solve problems and identify disagreements as part of daily life. The internet contains several good information sources which can provide facts and general information, but it is less obvious where to find meaningful discussions and arguments that are grounded with evidence. User generated content can be found in social networking sites, add comments sections of news articles, blogs or micro blogs and information collation sites such as Wikipedia. Any evaluations that occur in these discussions must be conducted by humans because any argument structure is not presented explicitly and therefore cannot be automatically analysed. If a user wishes to hear all sides of an argument it can be difficult to find all the right information. This work aims to automatically identify and extract the structure of arguments from user generated content. Thereby, allowing the users of this content to see the structured arguments contained within, with the claims, counter claims, and grounds. Presenting this structure can allow users to more easily navigate the content thereby enabling the user to become part of the conversation in a way that is arguably more useful to them. The steps in the process, and therefore the problems that this work aims to tackle, are the identification of discussions on different topics, the identification of spans of text that illustrate the discussion, classification of text into an argument structure so as to define the relationships between spans of text and to present this information to users.