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

Form and Function of Connectives in Chinese Conversational Speech

Connectives convey discourse functions that provide textual and pragmatic information in speech communication on top of canonical, sentential use. This paper proposes an applicable scheme with illustrative examples for distinguishing Sentential, Conclusion, Disfluency, Elaboration, and Resumption uses of Mandarin connectives, including conjunctions and adverbs. Quantitative results of our annotation works are presented to gain an overview of connectives in a Mandarin conversational speech corpus. A fine-grained taxonomy is also discussed, but it requires more empirical data to approve the applicability. By conducting a multinomial logistic regression model, we illustrate that connectives exhibit consistent patterns in positional, phonetic, and contextual features oriented to the associated discourse functions. Our results confirm that the position of Conclusion and Resumption connectives orient more to positions in semantically, rather than prosodically, determined units. We also found that connectives used for all four discourse functions tend to have a higher initial F0 value than those of sentential use. Resumption and Disfluency uses are expected to have the largest increase in initial F0 value, followed by Conclusion and Elaboration uses. Durational cues of the preceding context enable distinguishing Sentential use from discourse uses of Conclusion, Elaboration, and Resumption of connectives

Community tracking in a cMOOC and nomadic learner behavior identification on a connectivist rhizomatic learning network

This article contributes to the literature on connectivism, connectivist MOOCs (cMOOCs) and rhizomatic learning by examining participant interactions, community formation and nomadic learner behavior in a particular cMOOC, #rhizo15, facilitated for 6 weeks by Dave Cormier. It further focuses on what we can learn by observing Twitter interactions particularly. As an explanatory mixed research design, Social Network Analysis and content analysis were employed for the purposes of the research. SNA is used at the macro, meso and micro levels, and content analysis of one week of the MOOC was conducted using the Community of Inquiry framework. The macro level analysis demonstrates that communities in a rhizomatic connectivist networks have chaotic relationships with other communities in different dimensions (clarified by use of hashtags of concurrent, past and future events). A key finding at the meso level was that as #rhizo15 progressed and number of active participants decreased, interaction increased in overall network. The micro level analysis further reveals that, though completely online, the nature of open online ecosystems are very convenient to facilitate the formation of community. The content analysis of week 3 tweets demonstrated that cognitive presence was the most frequently observed, while teaching presence (teaching behaviors of both facilitator and participants) was the lowest. This research recognizes the limitations of looking only at Twitter when #rhizo15 conversations occurred over multiple platforms frequented by overlapping but not identical groups of people. However, it provides a valuable partial perspective at the macro meso and micro levels that contribute to our understanding of community-building in cMOOCs