PNNL

Abstract

Language in social media is extremely dynamic: new concepts emerge, trend and disappear, while the meaning of existing concepts can fluctuate over time. This work addresses a novel task of measuring and predicting meaning shift – context or semantic representation change from surface level dynamics e.g., word frequencies observed in social media streams. We study the relationship between language dynamics and short-term context change on a corpus of VKontakte posts collected during the Russia-Ukraine crisis in 2014 – 2015. We train a neural network model to predict short-term shifts in meaning from previous meaning as well as word dynamics. We demonstrate that short-term representation shift can be accurately predicted up to several weeks in advance. Moreover, we visualize this short-term context shift for the example words and demonstrate the practical side of our approach to discover and track meaning of newly emerging terms in social media during crises.