Argument Strength is in the Eye of the Beholder: Audience Effects in Persuasion

Americans spend about a third of their time online, with many participating in online conversations on social and political issues. We hypothesize that social media arguments on such issues may be more engaging and persuasive than traditional media summaries, and that particular types of people may be more or less convinced by particular styles of argument, e.g. emotional arguments may resonate with some personalities while factual arguments resonate with others. We report a set of experiments testing at large scale how audience variables interact with argument style to affect the persuasiveness of an argument, an under-researched topic within natural language processing. We show that belief change is affected by personality factors, with conscientious, open and agreeable people being more convinced by emotional arguments.Comment: European Chapter of the Association for Computational Linguistics (EACL 2017

Debbie, the Debate Bot of the Future

Chatbots are a rapidly expanding application of dialogue systems with companies switching to bot services for customer support, and new applications for users interested in casual conversation. One style of casual conversation is argument, many people love nothing more than a good argument. Moreover, there are a number of existing corpora of argumentative dialogues, annotated for agreement and disagreement, stance, sarcasm and argument quality. This paper introduces Debbie, a novel arguing bot, that selects arguments from conversational corpora, and aims to use them appropriately in context. We present an initial working prototype of Debbie, with some preliminary evaluation and describe future work.Comment: IWSDS 201

Deep Memory Networks for Attitude Identification

We consider the task of identifying attitudes towards a given set of entities from text. Conventionally, this task is decomposed into two separate subtasks: target detection that identifies whether each entity is mentioned in the text, either explicitly or implicitly, and polarity classification that classifies the exact sentiment towards an identified entity (the target) into positive, negative, or neutral. Instead, we show that attitude identification can be solved with an end-to-end machine learning architecture, in which the two subtasks are interleaved by a deep memory network. In this way, signals produced in target detection provide clues for polarity classification, and reversely, the predicted polarity provides feedback to the identification of targets. Moreover, the treatments for the set of targets also influence each other -- the learned representations may share the same semantics for some targets but vary for others. The proposed deep memory network, the AttNet, outperforms methods that do not consider the interactions between the subtasks or those among the targets, including conventional machine learning methods and the state-of-the-art deep learning models.Comment: Accepted to WSDM'1

On persuasive strategies: Metadiscourse practices in political speeches

This study attempted to investigate the persuasive meaning of metadiscourse markers in political speeches to see to what extent and how persuasive discourse is constructed in this genre through metadiscourse practices. To this aim, twenty-six political speeches given by Barack Obama, a former president of the United States, were analyzed using a discourse analytic approach and following Hyland’s (2005ab) interpersonal models of metadiscourse to identify the frequency and persuasive function of interactive and interactional devices used. The findings indicated that the persuasive meaning conveyed by metadiscourse was for the most part context-dependent, which sometimes required the speaker to rely on a combination of devices to organize his discourse, persuade audiences, attract their attention and engage them in arguments. Furthermore, interactional devices were more frequently used than interactive ones, reflecting that engaging audiences in arguments and showing one’s attitude and evaluation towards propositions were more likely to contribute to constructing a persuasive political speech. Findings can be discussed in terms of raising the awareness of second language speakers toward the linguistic and pragmatic conventions of political discourse and how persuasive discourse is constructed through metadiscourse markers.