10 research outputs found
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