Abstract: Social network apps, such as Twitter, Sina Micro-blog and etc. have provided mass context-information associated with user IDs (who), check-in time (when), GPS coordinates (where), topic (what) and incentive (why) of tweets (5W for short) for location based services. The availability of such data received from users offers a good opportunity to study the user's behavior and preference. In this paper, we propose a W5 probabilistic model to exploit such data with context by jointly probability to discover users' dynamic behaviors from temporal, spatial and activity aspects. Our work is applied to prediction for user and location. Experimental results on two real-world datasets show that W5 model is effective in discovering users' spatial-temporal prediction, and outperforms state-of-the art baselines, such as W4, on accuracy UP: (GT: 3.75%, ST: 6.54%) and LP: (GT: 8.7%, ST: 20.6%) at aspects of user prediction and location prediction based on Geo-Text (GT) and Sina-Tweets (ST).