Tweets2Cube: Interactive spatiotemporal knowledge acquisition from massive social media

We present the Tweets2Cube system that uncovers the patterns underlying people's spatiotemporal activities from massive online social media. Tweets2Cube organizes unstructured social media records into a multi-dimensional data cube along three dimensions: (1) what is the user's activity; (2) where does that activity occur; and (3) when does that activity occur. As such, the end users can use simple queries to retrieve task-relevant sub-corpus from the data cube in a flexible way. Moreover, Tweets2Cube consists of a set of spatiotemporal modeling algorithms, which can be readily applied to the retrieved data for extracting knowledge about people's activities in the physical world. Such algorithms jointly model location, time, and text and are capable of discovering a variety of patterns, such as routine spatiotemporal activities, unusual events, and mobility patterns. With Tweets2Cube, the end users can interactively retrieve task-relevant social media and choose appropriate spatiotemporal modeling algorithms for knowledge acquisition, which makes Tweets2Cube highly useful for downstream tasks like disaster relief, targeted advertising, and location-based recommendation.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2018

Author dc:creator

• Li, Lunan

Contributors dc:contributor

• Han, Jiawei

• Social Media, Social Cube

Statement dc:rights

• Copyright 2018 Lunan Li

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/101382

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/101382