That’s your evidence?: Classifying stance in online political debate

{{Summary
 * title=That’s your evidence?: Classifying stance in online political debate
 * authors=Marilyn A. Walker, Pranav Anand, Rob Abbott, Jean E. Fox Tree, Craig Martelly, and Joseph King,
 * url=http://users.soe.ucsc.edu/~maw/papers/wassa_article.pdf
 * tags=online argumentation, NLP, stance, disagreement, Mechanical Turk, natural language processing, discourse analysis, cue words
 * summary=This is a journal submission version of the Cats rule and dogs drool!: Classifying stance in online debate. It covers 14, rather than 12 topics; has more extensive figures; and further examples. It uses a third machine learning algorithm (SVM using libLinear) and experiments with MinCut. Examples of posts that are difficult to side are particularly helpful.

The authors further examined 386 incorrectly classified posts and indicate that context was needed nearly 30% of the time (6% were pure disagreement without content, 12% needed more context, 12% contained quotations as context but the algorithm took these as verbatim statements of the post). 33% were "qualitatively labeled as 'hard'").

Selected references
}}
 * Abbott, R., Walker, M., Anand, P., Tree, J., Bowmani, R., King, J., 2011. How can you say such things?!?: Recognizing disagreement in informal political argument. ACL HLT 2011, 2.
 * Marcu, D., 2000. Perlocutions: The achilles’ heel of speech act theory. Journal of pragmatics 32, 1719-1741.
 * relevance=Significant review of related work, embedded in the paper.
 * journal=Decision Support Sciences
 * doi=10.1016/j.dss.2012.05.032
 * subject=Computer Science