1 / 10

Exploring Urban Characteristics Through Twitter Data: An Analytical Framework

This study utilizes the Twitter Open API to investigate urban characteristics via user-generated content. By requesting posts within specified geographic parameters, it collects up to 1,500 tweets from designated centers with a 1km radius. The analysis focuses on the content of messages for socio-psychological insights and examines location and time to reveal socio-migration patterns. Additionally, we propose methods to overcome API limitations, employing grid and quad-tree splitting techniques to efficiently gather and analyze regional tweet trends, enhancing our understanding of urban dynamics.

taya
Télécharger la présentation

Exploring Urban Characteristics Through Twitter Data: An Analytical Framework

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. www.cs.odu.edu/~rlewis/urban_characteristics Urban Characteristics As discovered from the Twitter open API

  2. Open Data Gathering • Ability to request posts from a region • Provide center point and radius (≤ 1km) • Returns ≤ 1,500 posts up to one week old • Contains • User ID • Time • Location • Posted message

  3. Socio-Psychological Discoveries • Focus is on the content of the message • Places emphasis on link structure of followers (subscribers) of the post • Gather trends within posted messages to cull tastes/preferences

  4. Socio-Migration Discoveries • Focus is on the time and location of the tweet • Cull regional characteristic patterns from movement histories • Monitor updates from micro-blogging site in relation to geography • Clustering posts as a step to measure movement patterns • Extract characteristic patterns

  5. Circumventing Twitter Open API Limitation • The naïve way • Method • Uniformly split a region into 1km grid cells • Call API for each cell • Application • Circular area of 100km radius (124 miles in diameter) • Need ┌1002π┐ cells • Results in 31,416 separate calls to the API (current limitation imposed is 150 calls per hour)

  6. Circumventing Twitter Open API Limitation • Quad-tree splitting • Method • Recursively split the region into four rectangular cells of equal size • Stop splitting when • Cell is larger than the minimum radius permitted by the API (1km) • Number of posts in a cell is < 1,500

  7. Circumventing Twitter Open API Limitation • Quad-tree splitting (continued) • Application

  8. Quad-Tree Splitting

  9. Aggregation Model

  10. Dispersion Model

More Related