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 Use of Contestation and Social Norms in Developing Radicalized Discourses Online

This thesis investigated how discourses developed in blog\u27s comment threads work to promote the development of a radicalized public sphere. Using Dahlberg\u27s (2007) concept of radicalized public sphere as an alternative to the mainstream, largely media-controlled, Habermasian public sphere, this study investigated how comments from treehugger.com and techcrunch.com worked to promote or inhibit radicalized public sphere development through the use of discursive radicalism and inter-discursive contestation. Ideological criticism, in combination with the constant comparative method, was employed to code each comment and develop a system of identification for each type of comment based on the ideology it presented, and the rhetorical strategy used to convey its ideology. Coded comments were grouped into categories, and those categories grouped into broader categories, until a dominant form of commenting was identified.;The results from this study showed that discourse-promoting comments, which met the definition of discursive radicalism provided by Dahlberg (2007), dominated the comments on both blogs. Furthermore, it was found that normalizing comments were present on the comment threads, but did not dominate the discourse. These results suggest that radicalized public sphere discourse is functioning in the comment threads of the blogs in this investigation. Further studies are necessary, however, to gain a better understanding of the nature of commenting and the development of a radicalized public sphere on the numerous and diverse blog sites that populate the Internet

Social recognition provision patterns in professional Q&A forums in Healthcare and Construction

© 2015 Elsevier Ltd. All rights reserved. For some decades, professional Q&A forums have been used as a mainstream way of sharing practices between novices and experts. Several forums have had time to develop their own communities and habits, which made them a suitable place to explore patterned epistemic practices. In this paper we look at the social recognition, help seeking and informal learning patterns in communities of practice; our aim is to use the corresponding outputs to scaffold technology supported informal learning. We analyzed professional discussion forums in two countries (UK and Germany) in two different sectors (Healthcare and Construction). We identified a set of interrelated patterns that are used for socially verifying and maturing rules and guidelines, solving problems, introducing new practices and triggering learning. Some particular social recognition and learning trends common in Healthcare and Construction sector Q&A forums are highlighted. We discuss epistemic practice pattern networks for developing scaffolds to enhance the quality of informal learning in workplace environments in an integrated way. We suggest and validate empirically a model of social recognition provision in Q&A forums

Seeking and Providing Social Support in Online Forums for Individuals Experiencing Depression

This thesis project investigates how individuals suffering from depression seek support, provide support, and describe experiences of stigma in an online support platform. A phronetic-iterative and constant comparative approach guided an in-depth analysis of 37 posts on a discussion board designed for individuals with depression. The findings demonstrated that individuals use online discussion forums to request support both implicitly and explicitly. In response to sought support, individuals provided informational, emotional, and network support. Finally, discussion board participants discussed social and self stigma. Despite the limitations of the study, the findings indicate the utility of online social support platforms for individuals suffering from depression and emphasize their importance in facilitating supportive communicative interactions. Keywords: depression, online discussion boards, social support, stigma

Towards Feasible Instructor Intervention in MOOC discussion forums

Massive Open Online Courses allow numerous people from around the world to have access to knowledge that they otherwise have not. However, high student-to-instructor ratio in MOOCs restricts instructors’ ability to facilitate student learning by intervening in discussions forums, as they do in face-to-face classrooms. Instructors need automated guidance on when and how to intervene in discussion forums. Using a typology of pedagogical interventions derived from prior research, we annotate a large corpus of discussion forum contents to enable supervised machine learning to automatically identify interventions that promote student learning. Such machine learning models may allow building of dashboards to automatically prompt instructors on when and how to intervene in discussion forums. In the longer term, it may be possible to automate these interventions relieving instructors of this effort. Such automated approaches are essential for allowing good pedagogical practices to scale in the context of MOOC discussion forums