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The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.

Caveat emptor, computational social science: Large-scale missing data in a widely-published Reddit corpus

Gaffney, Devin, Matias, J. Nathan·2018·PLOS ONE
DOI 10.1371/journal.pone.0200162PMC PMC6034852
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Crossref · 10.1371/journal.pone.0200162

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5 of 5 references verified0 citation errors · 0 not checkable

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Figures

Fig 1 Log-histograms of sampled user submission and comment counts.
Fig 1 Log-histograms of sampled user submission and comment counts.CC BY · PubMed Central
Fig 2 Burstiness of missing submissions and comments per month, 2005-June 2017.
Fig 2 Burstiness of missing submissions and comments per month, 2005-June 2017.CC BY · PubMed Central
Fig 3 Varied measures of missing submissions per month. Medium blue circles denote the percent of submissions missing for each month of data, bright blue squares denote the average percent of missing submissions to date, and dark blue stars denote the cumulative total percent of missing submissions to date.
Fig 3 Varied measures of missing submissions per month. Medium blue circles denote the percent of submissions missing for each month of data, bright blue squares denote the average percent of missing submissions to date, and dark blue stars denote the cumulative total percent of missing submissions to date.CC BY · PubMed Central
Fig 4 Varied measures of missing comments per month. Medium blue circles denote the percent of comments missing for each month of data, bright blue squares denote the average percent of missing comments to date, and dark blue stars denote the cumulative total percent of missing comments to date.
Fig 4 Varied measures of missing comments per month. Medium blue circles denote the percent of comments missing for each month of data, bright blue squares denote the average percent of missing comments to date, and dark blue stars denote the cumulative total percent of missing comments to date.CC BY · PubMed Central
Fig 5 Gaps are not evenly distributed across communities. The total historical counts of comments per community comments are mildly correlated with the number of dangling references, while submissions are not very correlated with the number of dangling references.
Fig 5 Gaps are not evenly distributed across communities. The total historical counts of comments per community comments are mildly correlated with the number of dangling references, while submissions are not very correlated with the number of dangling references.CC BY · PubMed Central

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Citations

Cites

Research works printed but not linked: 22

  1. Pak A, Paroubek P. Twitter as a Corpus for Sentiment Analysis and Opinion Mining. In: LREc. vol. 10; 2010. p. 1320–1326.
  2. Abdullah S, Wu X. An epidemic model for news spreading on twitter. In: 2011 IEEE 23rd International Conference on Tools with Artificial Intelligence. IEEE; 2011. p. 163–169. · DOI
  3. Matias JN. Going Dark: Social Factors in Collective Action Against Platform Operators in the Reddit Blackout. In: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. ACM; 2016. p. 1138–1151. · DOI
  4. Newell E, Jurgens D, Saleem HM, Vala H, Sassine J, Armstrong C, et al. User Migration in Online Social Networks: A Case Study on Reddit During a Period of Community Unrest. In: Tenth International AAAI Conference on Web and Social Media; 2016.
  5. Fire M, Guestrin C. Analyzing Complex Network User Arrival Patterns and Their Effect on Network Topologies. arXiv preprint arXiv:160307445. 2016;.
  6. Tan C, Lee L. All who wander: On the prevalence and characteristics of multi-community engagement. In: Proceedings of the 24th International Conference on World Wide Web. ACM; 2015. p. 1056–1066. · DOI
  7. Saleem HM, Dillon K, Benesch S, Ruths D. A Web of Hate: Tackling Hateful Speech in Online Social Spaces. In: First Workshop on Text Analytics for Cybersecurity and Online Safety (TA-COS 2016); 2016.
  8. Barbosa S, Cosley D, Sharma A, Cesar Jr RM. Averaging Gone Wrong: Using Time-Aware Analyses to Better Understand Behavior. In: Proceedings of the 25th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee; 2016. p. 829–841. · DOI
  9. Leavitt A, Clark JA. Upvoting hurricane Sandy: event-based news production processes on a social news site. In: Proceedings of the SIGCHI conference on human factors in computing systems. ACM; 2014. p. 1495–1504. <comment>Available from: http://dl.acm.org/citation.cfm?id=2557140</comment>
  10. A Massanari, 2015, Participatory Culture, Community, and Play: Learning from Reddit, 10.3726/978-1-4539-1501-1 · DOI
  11. Massanari A. # Gamergate and The Fappening: How Reddit’s algorithm, governance, and culture support toxic technocultures. New Media & Society. 2015; p. 1461444815608807. · DOI · Paper page
  12. Leavitt A, Robinson JJ. The Role of Information Visibility in Network Gatekeeping: Information Aggregation on Reddit during Crisis Events. In: CSCW; 2017. p. 1246–1261. · DOI
  13. G Lotan, 2011, The Arab Spring—the revolutions were tweeted: Information flows during the 2011 Tunisian and Egyptian revolutions, International journal of communication, 5, 31
  14. Y Benkler, 2015, Social mobilization and the networked public sphere: Mapping the SOPA-PIPA debate, Political Communication, 32, 594, 10.1080/10584609.2014.986349 · DOI
  15. HH Jo, 2012, Circadian pattern and burstiness in mobile phone communication, New Journal of Physics, 14, 013055, 10.1088/1367-2630/14/1/013055 · DOI
  16. Hessel J, Tan C, Lee L. Science, AskScience, and BadScience: On the Coexistence of Highly Related Communities. In: Tenth International AAAI Conference on Web and Social Media; 2016.
  17. Hessel J, Schofield A, Lee L, Mimno D. What do Democrats do in their Spare Time? Latent Interest Detection in Multi-Community Networks. arXiv preprint arXiv:151103371. 2015;.
  18. Hessel J, Lee L, Mimno D. Cats and captions vs. creators and the clock: Comparing multimodal content to context in predicting relative popularity. In: Proceedings of the 26th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee; 2017. p. 927–936. · DOI
  19. Fire M, Guestrin C. The Rise and Fall of Network Stars. arXiv preprint arXiv:170606690. 2017;.
  20. Barbaresi A. Collection, Description, and Visualization of the German Reddit Corpus. In: 2nd Workshop on Natural Language Processing for Computer-Mediated Communication; 2015. p. 7–11.
  21. Horne BD, Adali S. The impact of crowds on news engagement: A reddit case study. arXiv preprint arXiv:170310570. 2017;. · DOI
  22. Dosono B, Semaan B, Hemsley J. Exploring AAPI identity online: Political ideology as a factor affecting identity work on Reddit. In: Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems. ACM; 2017. p. 2528–2535. · DOI

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Metadata

Authors
Gaffney, D., & Matias, J. N.
ORCID
Gaffney, Devin
Venue
PLOS ONE
Publisher
Public Library of Science (PLoS)
Published
2018-07-06
Volume
13
Issue
7
Pages
e0200162
ISSN
1932-6203
DOI
10.1371/journal.pone.0200162
PMC
PMC6034852
Cited by
63
References
5
Language
en
Licence
creativecommons.org/licenses/by/4.0/ (vor)

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