Tweets2Cube: Interactive spatiotemporal knowledge acquisition from massive social media

Li, Lunan

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https://hdl.handle.net/2142/101382 Copy

Description

Title

Tweets2Cube: Interactive spatiotemporal knowledge acquisition from massive social media

Author(s)

Li, Lunan

Issue Date

2018-04-26

Director of Research (if dissertation) or Advisor (if thesis)

Han, Jiawei

Department of Study

Computer Science

Discipline

Computer Science

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Keyword(s)

Social Media, Social Cube

Abstract

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.

Graduation Semester

2018-05

Type of Resource

text

Permalink

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

Copyright and License Information

Copyright 2018 Lunan Li

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