NDLI: Should We Use the Sample? Analyzing Datasets Sampled from Twitter’s Stream API

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ACM Transactions on the Web (TWEB) : Volume 10

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A UI-Centric Approach for the End-User Development of Multidevice Mashups

Sampling Content from Online Social Networks: Comparing Random vs. Expert Sampling of the Twitter Stream

Should We Use the Sample? Analyzing Datasets Sampled from Twitter’s Stream API

Reliable and Resilient Trust Management in Distributed Service Provision Networks

A Supervised Learning Approach to Protect Client Authentication on the Web

Detection of Political Manipulation in Online Communities through Measures of Effort and Collaboration

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Should We Use the Sample? Analyzing Datasets Sampled from Twitter’s Stream API

Content Provider	ACM Digital Library
Author	Wang, Yazhe Callan, Jamie Zheng, Baihua
Copyright Year	2015
Abstract	Researchers have begun studying content obtained from microblogging services such as Twitter to address a variety of technological, social, and commercial research questions. The large number of Twitter users and even larger volume of tweets often make it impractical to collect and maintain a complete record of activity; therefore, most research and some commercial software applications rely on samples, often relatively small samples, of Twitter data. For the most part, sample sizes have been based on availability and practical considerations. Relatively little attention has been paid to how well these samples represent the underlying stream of Twitter data. To fill this gap, this article performs a comparative analysis on samples obtained from two of Twitter’s streaming APIs with a more complete Twitter dataset to gain an in-depth understanding of the nature of Twitter data samples and their potential for use in various data mining tasks.
Starting Page	1
Ending Page	23
Page Count	23
File Format	PDF
ISSN	15591131
e-ISSN	1559114X
DOI	10.1145/2746366
Volume Number	9
Issue Number	3
Journal	ACM Transactions on the Web (TWEB)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-06-02
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Twitter API Data mining Sample
Content Type	Text
Resource Type	Article
Subject	Computer Networks and Communications

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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Should We Use the Sample? Analyzing Datasets Sampled from Twitter’s Stream API

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Is the sample good enough? comparing data from twitter’s streaming api with twitter’s firehose.

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