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OSoMe: The IUNI observatory on social media

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1 Center for Complex Networks and Systems Research, Indiana University, Bloomington, United States
2 School of Informatics and Computing, Indiana University, Bloomington, United States
3 Network Science Institute, Indiana University, Bloomington, United States
4 Yahoo Inc., London, United Kingdom
5 LinkedIn Inc., Mountain View, CA, United States
6 Information Sciences Institute, University of Southern California, Marina Del Rey, CA, United States
7 Facebook Inc., Boston, MA, United States
8 Center for Data Science, New York University, New York City, New York, United States
9 Max Planck Institute for Software Systems, Saarbrücken, Germany
10 U.S. Open Data, Oakland, CA, United States
11 Google Inc., Mountain View, CA, United States
12 Yahoo Inc., Sunnyvale, CA, United States
13 Affirm Inc., San Francisco, CA, United States
14 Amazon Inc., Seattle, WA, United States
DOI
10.7287/peerj.preprints.2008v1
Published
Accepted
Subject Areas
Data Science, Network Science and Online Social Networks, Social Computing, World Wide Web and Web Science
Keywords
Social media, Observatory, Twitter, Web Science, Network Science, Meme diffusion, Computational Social Science, Big data, API, OSoMe
Copyright
© 2016 Davis et al.
Licence
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
Cite this article
Davis CA, Ciampaglia GL, Aiello LM, Chung K, Conover MD, Ferrara E, Flammini A, Fox GC, Gao X, Gonçalves B, Grabowicz PA, Hong K, Hui P, McCaulay S, McKelvey K, Meiss MR, Patil S, Peli Kankanamalage C, Pentchev V, Qiu J, Ratkiewicz J, Rudnick A, Serrette B, Shiralkar P, Varol O, Weng L, Wu T, Younge AJ, Menczer F. 2016. OSoMe: The IUNI observatory on social media. PeerJ Preprints 4:e2008v1

Abstract

The study of social phenomena is becoming increasingly reliant on big data from online social networks. Broad access to social media data, however, requires software development skills that not all researchers possess. Here we present the IUNI Observatory on Social Media, an open analytics platform designed to facilitate computational social science. The system leverages a historical, ongoing collection of over 70 billion public messages from Twitter. We illustrate a number of interactive open-source tools to retrieve, visualize, and analyze derived data from this collection. The Observatory, now available at osome.iuni.iu.edu, is the result of a large, six-year collaborative effort coordinated by the Indiana University Network Science Institute.

Author Comment

This is a preprint submission to PeerJ Preprints.

Additional Information

Competing Interests

Filippo Menczer is an Academic Editor for PeerJ Computer Science.

Author Contributions

Clayton A Davis wrote the paper, prepared figures and/or tables, performed the computation work, reviewed drafts of the paper.

Giovanni Luca Ciampaglia wrote the paper, prepared figures and/or tables, performed the computation work, reviewed drafts of the paper.

Luca Maria Aiello performed the computation work, reviewed drafts of the paper.

Keychul Chung performed the computation work, reviewed drafts of the paper.

Michael D Conover performed the computation work, reviewed drafts of the paper.

Emilio Ferrara performed the computation work, reviewed drafts of the paper.

Alessandro Flammini reviewed drafts of the paper.

Geoffrey C Fox reviewed drafts of the paper.

Xiaoming Gao performed the computation work, reviewed drafts of the paper.

Bruno Gonçalves performed the computation work, reviewed drafts of the paper.

Przemyslaw A Grabowicz performed the computation work, reviewed drafts of the paper.

Kibeom Hong performed the computation work, reviewed drafts of the paper.

Pik-Mai Hui performed the computation work, reviewed drafts of the paper.

Scott McCaulay performed the computation work, reviewed drafts of the paper.

Karissa McKelvey performed the computation work, reviewed drafts of the paper.

Mark R Meiss performed the computation work, reviewed drafts of the paper.

Snehal Patil performed the computation work, reviewed drafts of the paper.

Chathuri Peli Kankanamalage performed the computation work, reviewed drafts of the paper.

Valentin Pentchev reviewed drafts of the paper.

Judy Qiu reviewed drafts of the paper.

Jacob Ratkiewicz performed the computation work, reviewed drafts of the paper.

Alex Rudnick performed the computation work, reviewed drafts of the paper.

Benjamin Serrette prepared figures and/or tables, performed the computation work, reviewed drafts of the paper.

Prashant Shiralkar performed the computation work, reviewed drafts of the paper.

Onur Varol performed the computation work, reviewed drafts of the paper.

Lilian Weng performed the computation work, reviewed drafts of the paper.

Tak-Lon Wu performed the computation work, reviewed drafts of the paper.

Andrew J Younge performed the computation work, reviewed drafts of the paper.

Filippo Menczer wrote the paper, reviewed drafts of the paper.

Ethics

The following information was supplied relating to ethical approvals (i.e., approving body and any reference numbers):

This study was deemed exempt from review by the Indiana University IRB office under Protocol #1102004860.

Data Deposition

The following information was supplied regarding data availability:

Data from the OSoMe are available through an API and through interactive apps. For more information please visit: http://osome.iuni.iu.edu/

It is important to note that, in compliance with the Twitter terms of service [1], OSoMe does not provide access to the content of tweets. However, researchers can obtain numeric object identifiers in response to their queries. This information can then be used to retrieve tweet content via the official Twitter API.

[1] https://dev.twitter.com/overview/terms/policy

Funding

This work was supported in part by NSF (grants CCF-1101743 and OCI-1149432), the J.S. McDonnell Foundation (grant 220020274), the Swiss National Science Foundation (fellowship PBTIP2_142353), the Lilly Endowment, the Center for Complex Networks and Systems Research (CNetS), the Digital Science Center (DSC), and the Indiana University Network Science Institute (IUNI). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the funding agencies. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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