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

How did the discussion go: Discourse act classification in social media conversations

We propose a novel attention based hierarchical LSTM model to classify discourse act sequences in social media conversations, aimed at mining data from online discussion using textual meanings beyond sentence level. The very uniqueness of the task is the complete categorization of possible pragmatic roles in informal textual discussions, contrary to extraction of question-answers, stance detection or sarcasm identification which are very much role specific tasks. Early attempt was made on a Reddit discussion dataset. We train our model on the same data, and present test results on two different datasets, one from Reddit and one from Facebook. Our proposed model outperformed the previous one in terms of domain independence; without using platform-dependent structural features, our hierarchical LSTM with word relevance attention mechanism achieved F1-scores of 71\% and 66\% respectively to predict discourse roles of comments in Reddit and Facebook discussions. Efficiency of recurrent and convolutional architectures in order to learn discursive representation on the same task has been presented and analyzed, with different word and comment embedding schemes. Our attention mechanism enables us to inquire into relevance ordering of text segments according to their roles in discourse. We present a human annotator experiment to unveil important observations about modeling and data annotation. Equipped with our text-based discourse identification model, we inquire into how heterogeneous non-textual features like location, time, leaning of information etc. play their roles in charaterizing online discussions on Facebook

The microdynamics of social regulation:Comparing the navigation of disagreements in text-based online and face-to-face discussions

This study explores how people navigate the field of tension between expressing disagreement and maintaining social relationships in text-based online as compared to face-to-face discussions. In face-to-face discussions, differences of opinion are socially regulated by introducing ambiguity in message content coupled with instant responding on a relational level. We hypothesized that online messages are less ambiguous and less responsive, both of which may hinder social regulation. Thirty-six groups of three unacquainted students discussed politically controversial statements via chat, video-chat (nonanonymous), and face-to-face, in a multilevel repeated measures Graeco-Latin square design. Content coding revealed that online discussions were relatively clear and unresponsive. This related to participants experiencing reduced conversational flow, less shared cognition, and less solidarity online. These results suggest that ambiguity and responsiveness enable people to maintain social relationships in the face of disagreement. This emphasizes the key role that subtle microdynamics in interpersonal interaction play in social regulation

Everyday Diplomacy:dealing with controversy online and face-to-face

Discussions about controversial topics, such as immigration, seem to get out of hand more easily when they take place online than when they are conducted face-to-face. It is often assumed that this is because people express themselves less clearly or more ambiguously online due to missing non-verbal signals, or because people become disinhibited online due to feeling anonymous. In this dissertation, we call these assumptions into question by studying what happens within online and face-to-face discussions. We closely examined behavior and social perceptions in conversations: How do interaction partners interact and how does this affect their relationship? We asked groups of unacquainted students to discuss about politically controversial topics via a text-based chat and face-to-face. We found that people express their opinions more clearly or less ambiguously in text-based chats than in face-to-face conversations. We also found that people respond less to each other online. This is due to the way the textual and asynchronous medium limits behavior, but people don't seem to acknowledge this. They do not feel heard because they get the idea that their interlocutors are mainly concerned with venting their own opinion. As a result, people think that they disagree more than they actually do and experience more conflict. This offers a new perspective on online polarization and disinhibition: people can feel polarized and get the idea that their interlocutor is disinhibited without that being the case, purely because of the way the online medium steers behavior

Dealing with disagreement:The depolarizing effects of everyday diplomatic skills face-to-face and online

In online text-based discussions, people behave less diplomatically because they are more outspoken and less responsive. This can feed impressions of polarization. This article uses a new methodology to isolate the influence of outspokenness and responsiveness in shaping perceptions of polarization in online chat and face-to-face discussions. Text-based online and face-to-face discussions were reproduced in a face-to-face format (Study 1) and in a text-based chat format (Study 2). Uninformed observers (N = 102 and N = 103, repeated measures) evaluated these. The results showed that responsiveness was generally considered indicative of agreement and good social relationships but the interpretation of outspokenness (or lack of ambiguity) depended on the medium format. This suggests that what counts as diplomacy is not the same for each medium. Moreover, the experiences of the actors reproducing the chats in a face-to-face format highlighted the differences between media. We conclude that online conversational dynamics may play an important role in societal polarization

(Dis)agreements in Iranians’ internet relay chats

The present study on politeness is an attempt to examine (dis)agreeing strategies utilized by EFL learners while chatting on the internet. Subjects of the study were forty male and thirty-three female Iranian natives whose internet relay chat (IRC) interactions, composed of 400 excerpts, were collected between December 2007 and September 2008. Data analysis was based on the general taxonomy of politeness strategies suggested by Brown and Levinson (1987) which is the baseline of many politeness studies today. The results indicate that IRC is a mode of communication whose characteristics are typically different from face-to-face and real-life conversational settings. Some common face threatening acts (FTAs) like ‘direct disagreements’ are performed widely in chat channels. Furthermore, gender-oriented differences were found not to be statistically significant on the internet

Are We There Yet?: The Development of a Corpus Annotated for Social Acts in Multilingual Online Discourse

We present the AAWD and AACD corpora, a collection of discussions drawn from Wikipedia talk pages and small group IRC discussions in English, Russian and Mandarin. Our datasets are annotated with labels capturing two kinds of social acts: alignment moves and authority claims. We describe these social acts, describe our annotation process, highlight challenges we encountered and strategies we employed during annotation, and present some analyses of resulting data set which illustrate the utility of our corpus and identify interactions among social acts and between participant status and social acts and in online discourse