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.
Previous work also tests the hypothesis that dialogic exchanges might be more engaging, in the context of expository or car sales dialog @cite_67 @cite_58 @cite_1 @cite_41 . Work comparing monologic vs. dialogic modes of providing information suggest that dialogs: (1) are more memorable and engaging, (2) stimulate the audience to formulate their own questions, and (3) allow audiences to be more successful at following communication @cite_27 @cite_26 @cite_42 @cite_0 @cite_40 @cite_26 @cite_55 .
@cite_67 @cite_26 @cite_41 @cite_55 @cite_42 @cite_1 @cite_0 @cite_27 @cite_40 @cite_58
2019364517 2076139272 2518510348 2135329461
In two experiments, students overheard two computer-controlled virtual agents discussing four computer literacy topics in dialog discourse and four in monologue discourse. In Experiment 1, the virtual tutee asked a series of deep questions in the dialog condition, but only one per topic in the monologue condition in both studies. In the dialog conditions of Experiment 2, the virtual tutee asked either deep questions, shallow questions, or made comments. In a fourth “dialog” condition, the comments were spoken by the virtual tutor. The discourse spoken by the virtual tutor was identical in the dialog and monologue conditions, except in the fourth dialog condition. In both studies, learners wrote significantly more content and significantly more relevant content in the deep question condition than in the monologue condition. No other differences were significant. Results were discussed in terms of advanced organizers, schema theory, and discourse comprehension theory. In written dialog, discourse participants need to justify claims they make, to convince the reader the claim is true and or relevant to the discourse. This paper presents a new task (with an associated corpus), namely detecting such justifications. We investigate the nature of such justifications, and observe that the justifications themselves often contain discourse structure. We therefore develop a method to detect the existence of certain types of discourse relations, which helps us classify whether a segment is a justification or not. Our task is novel, and our work is novel in that it uses a large set of connectives (which we call indicators), and in that it uses a large set of discourse relations, without choosing among them. We propose a new task in the field of computational argumentation in which we investigate qualitative properties of Web arguments, namely their convincingness. We cast the problem as relation classification, where a pair of arguments having the same stance to the same prompt is judged. We annotate a large datasets of 16k pairs of arguments over 32 topics and investigate whether the relation “A is more convincing than B” exhibits properties of total ordering; these findings are used as global constraints for cleaning the crowdsourced data. We propose two tasks: (1) predicting which argument from an argument pair is more convincing and (2) ranking all arguments to the topic based on their convincingness. We experiment with feature-rich SVM and bidirectional LSTM and obtain 0.76-0.78 accuracy and 0.35-0.40 Spearman’s correlation in a cross-topic evaluation. We release the newly created corpus UKPConvArg1 and the experimental software under open licenses. A study compared the communicative effectiveness of spontaneous monologues and dialogues on nonparticipating addressees overhearing talk. Overhearers were more accurate at following instructions in a referential communication task when listening in on dialogues than when listening in on monologues. Several extraneous variables could not account for the result. Two explanations for overhearers’ better performances on the dialogues are that the greater number of discourse markers helps overhearers follow the speech and that the perspectives of multiple interlocutors are more informative than one perspective. Extensions of the collaborative model of language use are discussed.