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  1. KEEP
  2. Theses and Dissertations
  3. Barrett, The Honors College Thesis/Creative Project Collection
  4. Predicting Trends on Twitter with Time Series Analysis
  5. Full metadata

Predicting Trends on Twitter with Time Series Analysis

Full metadata

Description

Twitter, the microblogging platform, has grown in prominence to the point that the topics that trend on the network are often the subject of the news and other traditional media. By predicting trends on Twitter, it could be possible to predict the next major topic of interest to the public. With this motivation, this paper develops a model for trends leveraging previous work with k-nearest-neighbors and dynamic time warping. The development of this model provides insight into the length and features of trends, and successfully generalizes to identify 74.3% of trends in the time period of interest. The model developed in this work provides understanding into why par- ticular words trend on Twitter.

Date Created
2015-05
Contributors
  • Marshall, Grant A (Author)
  • Liu, Huan (Thesis director)
  • Morstatter, Fred (Committee member)
  • Computer Science and Engineering Program (Contributor)
  • Barrett, The Honors College (Contributor)
  • School of Mathematical and Statistical Sciences (Contributor)
Topical Subject
  • Social networks
  • Time-series Analysis
  • Big Data
  • Machine Learning
Resource Type
Text
Extent
8 pages
Language
eng
Copyright Statement
In Copyright
Primary Member of
Barrett, The Honors College Thesis/Creative Project Collection
Series
Academic Year 2014-2015
Handle
https://hdl.handle.net/2286/R.I.28901
Level of coding
minimal
Cataloging Standards
asu1
System Created
  • 2017-10-30 02:50:57
System Modified
  • 2021-08-11 04:09:57
  •     
  • 1 year 5 months ago
Additional Formats
  • OAI Dublin Core
  • MODS XML

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