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Language in social media is extremely dynamic: new words emerge, trend anddisappear, while the meaning of existing words can fluctuate over time. Suchdynamics are especially notable during a period of crisis. This work addressesseveral important tasks of measuring, visualizing and predicting short termtext representation shift, i.e. the change in a word's contextual semantics,and contrasting such shift with surface level word dynamics, or concept drift,observed in social media streams. Unlike previous approaches on learning wordrepresentations from text, we study the relationship between short-term conceptdrift and representation shift on a large social media corpus - VKontakte postsin Russian collected during the Russia-Ukraine crisis in 2014-2015. Our novelcontributions include quantitative and qualitative approaches to (1) measureshort-term representation shift and contrast it with surface level conceptdrift; (2) build predictive models to forecast short-term shifts in meaningfrom previous meaning as well as from concept drift; and (3) visualizeshort-term representation shift for example keywords to demonstrate thepractical use of our approach to discover and track meaning of newly emergingterms in social media. We show that short-term representation shift can beaccurately predicted up to several weeks in advance. Our unique approach tomodeling and visualizing word representation shifts in social media can be usedto explore and characterize specific aspects of the streaming corpus duringcrisis events and potentially improve other downstream classification tasksincluding real-time event detection.

Measuring, Predicting and Visualizing Short-Term Change in Word Representation and Usage in VKontakte Social Network