3 research outputs found
Stance Detection in Web and Social Media: A Comparative Study
Online forums and social media platforms are increasingly being used to
discuss topics of varying polarities where different people take different
stances. Several methodologies for automatic stance detection from text have
been proposed in literature. To our knowledge, there has not been any
systematic investigation towards their reproducibility, and their comparative
performances. In this work, we explore the reproducibility of several existing
stance detection models, including both neural models and classical
classifier-based models. Through experiments on two datasets -- (i)~the popular
SemEval microblog dataset, and (ii)~a set of health-related online news
articles -- we also perform a detailed comparative analysis of various methods
and explore their shortcomings. Implementations of all algorithms discussed in
this paper are available at
https://github.com/prajwal1210/Stance-Detection-in-Web-and-Social-Media
Analyzing Granger causality in climate data with time series classification methods
Attribution studies in climate science aim for scientifically ascertaining the influence of climatic variations on natural or anthropogenic factors. Many of those studies adopt the concept of Granger causality to infer statistical cause-effect relationships, while utilizing traditional autoregressive models. In this article, we investigate the potential of state-of-the-art time series classification techniques to enhance causal inference in climate science. We conduct a comparative experimental study of different types of algorithms on a large test suite that comprises a unique collection of datasets from the area of climate-vegetation dynamics. The results indicate that specialized time series classification methods are able to improve existing inference procedures. Substantial differences are observed among the methods that were tested