50 Citations (Scopus)

Abstract

Twitter shares a free 1% sample of its tweets through the Streaming API". Recently, research has pointed to evidence of bias in this source. The methodologies proposed in previous work rely on the restrictive and expensive Firehose to find the bias in the Streaming API data. We tackle the problem of finding sample bias without costly and restrictive Firehose data. We propose a solution that focuses on using an open data source to find bias in the Streaming API.

Original languageEnglish (US)
Title of host publicationWWW 2014 Companion - Proceedings of the 23rd International Conference on World Wide Web
PublisherAssociation for Computing Machinery, Inc
Pages555-556
Number of pages2
ISBN (Electronic)9781450327459
DOIs
StatePublished - Apr 7 2014
Event23rd International Conference on World Wide Web, WWW 2014 - Seoul, Korea, Republic of
Duration: Apr 7 2014Apr 11 2014

Other

Other23rd International Conference on World Wide Web, WWW 2014
CountryKorea, Republic of
CitySeoul
Period4/7/144/11/14

Fingerprint

Application programming interfaces (API)

Keywords

  • Big data
  • Data sampling
  • Sampling bias
  • Twitter analysis

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Software

Cite this

Morstatter, F., Pfeffer, J., & Liu, H. (2014). When is it biased? Assessing the representativeness of twitter's streaming API. In WWW 2014 Companion - Proceedings of the 23rd International Conference on World Wide Web (pp. 555-556). Association for Computing Machinery, Inc. https://doi.org/10.1145/2567948.2576952

When is it biased? Assessing the representativeness of twitter's streaming API. / Morstatter, Fred; Pfeffer, Jürgen; Liu, Huan.

WWW 2014 Companion - Proceedings of the 23rd International Conference on World Wide Web. Association for Computing Machinery, Inc, 2014. p. 555-556.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Morstatter, F, Pfeffer, J & Liu, H 2014, When is it biased? Assessing the representativeness of twitter's streaming API. in WWW 2014 Companion - Proceedings of the 23rd International Conference on World Wide Web. Association for Computing Machinery, Inc, pp. 555-556, 23rd International Conference on World Wide Web, WWW 2014, Seoul, Korea, Republic of, 4/7/14. https://doi.org/10.1145/2567948.2576952
Morstatter F, Pfeffer J, Liu H. When is it biased? Assessing the representativeness of twitter's streaming API. In WWW 2014 Companion - Proceedings of the 23rd International Conference on World Wide Web. Association for Computing Machinery, Inc. 2014. p. 555-556 https://doi.org/10.1145/2567948.2576952
Morstatter, Fred ; Pfeffer, Jürgen ; Liu, Huan. / When is it biased? Assessing the representativeness of twitter's streaming API. WWW 2014 Companion - Proceedings of the 23rd International Conference on World Wide Web. Association for Computing Machinery, Inc, 2014. pp. 555-556
@inproceedings{b757d13d792e4e4f985531ff23633649,
title = "When is it biased? Assessing the representativeness of twitter's streaming API",
abstract = "Twitter shares a free 1{\%} sample of its tweets through the Streaming API{"}. Recently, research has pointed to evidence of bias in this source. The methodologies proposed in previous work rely on the restrictive and expensive Firehose to find the bias in the Streaming API data. We tackle the problem of finding sample bias without costly and restrictive Firehose data. We propose a solution that focuses on using an open data source to find bias in the Streaming API.",
keywords = "Big data, Data sampling, Sampling bias, Twitter analysis",
author = "Fred Morstatter and J{\"u}rgen Pfeffer and Huan Liu",
year = "2014",
month = "4",
day = "7",
doi = "10.1145/2567948.2576952",
language = "English (US)",
pages = "555--556",
booktitle = "WWW 2014 Companion - Proceedings of the 23rd International Conference on World Wide Web",
publisher = "Association for Computing Machinery, Inc",

}

TY - GEN

T1 - When is it biased? Assessing the representativeness of twitter's streaming API

AU - Morstatter, Fred

AU - Pfeffer, Jürgen

AU - Liu, Huan

PY - 2014/4/7

Y1 - 2014/4/7

N2 - Twitter shares a free 1% sample of its tweets through the Streaming API". Recently, research has pointed to evidence of bias in this source. The methodologies proposed in previous work rely on the restrictive and expensive Firehose to find the bias in the Streaming API data. We tackle the problem of finding sample bias without costly and restrictive Firehose data. We propose a solution that focuses on using an open data source to find bias in the Streaming API.

AB - Twitter shares a free 1% sample of its tweets through the Streaming API". Recently, research has pointed to evidence of bias in this source. The methodologies proposed in previous work rely on the restrictive and expensive Firehose to find the bias in the Streaming API data. We tackle the problem of finding sample bias without costly and restrictive Firehose data. We propose a solution that focuses on using an open data source to find bias in the Streaming API.

KW - Big data

KW - Data sampling

KW - Sampling bias

KW - Twitter analysis

UR - http://www.scopus.com/inward/record.url?scp=84990955096&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84990955096&partnerID=8YFLogxK

U2 - 10.1145/2567948.2576952

DO - 10.1145/2567948.2576952

M3 - Conference contribution

AN - SCOPUS:84990955096

SP - 555

EP - 556

BT - WWW 2014 Companion - Proceedings of the 23rd International Conference on World Wide Web

PB - Association for Computing Machinery, Inc

ER -