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

Audiences’ coping practices with intrusive interfaces : researching audiences in algorithmic, datafied, platform societies

People in their role as audiences are increasingly confronted with intrusive digital media technologies that seek to collect personal data, shape people’s media experiences through algorithms and increasingly work towards establishing what is already being called a platform society. This tendency will become even more pertinent in the near future with the Internet of Things permeating many more aspects of our everyday lives. Software interfaces are thus becoming important objects of scientific inquiry. However, even though in their materiality interfaces promote very specific forms of media practices, people’s sense-making and interpretations still need to be considered as part of future audience research. In this context, we propose the idea of audiences’ coping practices when facing intrusive media interfaces in their exploitive, formative, ubiquitous and excluding character. By juxtaposing coping practices with intrusive media we sketch current and projected trends in audience research that focus on the power behind intrusive media on the one side and on people’s sense-making on the other side