Stance Prediction from Multimodal Social Media Data

Abstract

Stance prediction -the computational task of inferring attitudes towards a given target topic of interest -relies heavily on text data provided by social media or similar sources, but it may also benefit from non-text information such as demographics (e.g., users' gender, age, etc.), network structure (e.g., friends, followers, etc.), interactions (e.g., mentions, replies, etc.) and other non-text properties (e.g., time information, etc.). However, so-called hybrid (or in some cases multimodal) approaches to stance prediction have only been developed for a small set of target languages, and often making use of count-based text models (e.g., bag-of-words) and time-honoured classification methods (e.g., support vector machines). As a means to further research in the field, in this work we introduce a number of text-and non-text models for stance prediction in the Portuguese language, which make use of more recent methods based on BERT and an ensemble architecture, and ask whether a BERT stance classifier may be enhanced with different kinds of network-related information.