ML Anthology

TwitterCracy: Exploratory Monitoring of Twitter Streams for the 2016 U.S. Presidential Election Cycle

Muhammad Atif Qureshi, Arjumand Younus, Derek Greene
ECML-PKDD 2016 pp. 71-75
doi:10.1007/978-3-319-46131-1_16 /ecmlpkdd/2016/qureshi2016ecmlpkdd-twittercracy/

Abstract

We present TwitterCracy , an exploratory search system that allows users to search and monitor across the Twitter streams of political entities. Its exploratory capabilities stem from the application of lightweight time-series based clustering together with biased PageRank to extract facets from tweets and presenting them in a manner that facilitates exploration.

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Cite

Text

Qureshi et al. "TwitterCracy: Exploratory Monitoring of Twitter Streams for the 2016 U.S. Presidential Election Cycle." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2016. doi:10.1007/978-3-319-46131-1_16

Markdown

[Qureshi et al. "TwitterCracy: Exploratory Monitoring of Twitter Streams for the 2016 U.S. Presidential Election Cycle." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2016.](https://mlanthology.org/ecmlpkdd/2016/qureshi2016ecmlpkdd-twittercracy/) doi:10.1007/978-3-319-46131-1_16

BibTeX

@inproceedings{qureshi2016ecmlpkdd-twittercracy,
  title     = {{TwitterCracy: Exploratory Monitoring of Twitter Streams for the 2016 U.S. Presidential Election Cycle}},
  author    = {Qureshi, Muhammad Atif and Younus, Arjumand and Greene, Derek},
  booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
  year      = {2016},
  pages     = {71-75},
  doi       = {10.1007/978-3-319-46131-1_16},
  url       = {https://mlanthology.org/ecmlpkdd/2016/qureshi2016ecmlpkdd-twittercracy/}
}