Climate change opinions in online debate sites

Abstract

Debate sites in social media provide a unified platform for citizens to discuss controversial questions and to put forward their ideas and arguments on the issues of common interest. Opinions of citizens may provide useful knowledge to stakeholders but manual analysis of arguments in debate sites is tedious, while computational support to this end has been rather scarce. We focus here on developing a technical instrumentation for making sense of a set of online arguments and aggregating them into usable results for policy making and climate science communication. Our objectives are: (i) to aggregate arguments posted for a certain debate topic, (ii) to consolidate opinions posted under several but related topics either in the same or different debate site, and (iii) to identify possible linguistic characteristics of the argumentative texts. For the first objective, we propose a voting method based on subjective logic [13]. For the second objective, we assess the semantic similarity between two debate topics based on textual entailment [28]. For the third objective, we employ various existing methods for lexical analysis such as frequency analysis or readability indexes. Although we focused here on the climate change, the method can be applied to any domain.

Development of a Virtual Reality Simulator for an Intelligent Robotic System Used in Ankle Rehabilitation

“…There are several classification techniques such as: K-nearest neighbors [ 28 ], decision trees [ 29 ], random forest [ 30 ] and feed-forward neural network [ 31 ]. This project will use the K-nearest neighbors (KNN) algorithm which is based on a similarity of characteristics, which means that the level of similarity of the instances characteristics with those of the training set determines how a new observation will be classified.…”