Mapping Temporal Horizons: Analysis of Collective Future and Past related Attention in Twitter

Microblogging platforms such as Twitter have recently received much attention as great sources for live web sensing, real-time event detection and opinion analysis. Previous works usually assumed that tweets mainly describe "what's happening now". However, a large portion of tweets contains time expressions that refer to time frames within the past or the future. Such messages often reflect expectations or memories of social media users. In this work we investigate how microblogging users collectively refer to time. In particular, we analyze half a year long portion of Japanese and four months long collection of US tweets and we quantify collective temporal attention of users as well as other related temporal characteristics. This kind of knowledge is helpful in the context of growing interest for detection and prediction of important events within social media. The exploratory analysis we perform is possible thanks to the development of visual analytics framework for robust overview and easy detection of various regularities in the past and future-oriented thinking of Twitter users. We believe that the visualizations we provide and the findings we outline can be also valuable for sociologists and computer scientists to test and refine their models about time in natural language.