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Big Data? Big Issues: Degradation in Longitudinal Data and Implications for Social Sciences
- 1. Matthew S. Weber Hai Nguyen Rutgers University WebSci 2015 Oxford, UK BIG DATA, BIG ISSUES
- 3. 3 Dataset Research Potential Dates Captures Unique URLs Hurricane Katrina Online networks and organizational resilience (Chewning, Lai and Doerfel, 2012; Perry, Taylor and Doerfel, 2003) in the wake of disasters; information dissemination 2003 – 2012 1,694,236 663,740 Superstorm Sandy 2003 – 2012 41,703,112 20,013,455 US Senate Study the growth of political activity in online environments (Adamic & Glance, 2005; Bruns, 2007; Chang & Park, 2012); polarization & media discourse 109th – 112th Congresses 26,965,770 8,674,397 US House 51,840,777 12,410,014 Occupy Wall Street Previous research on NGOs in the online environment (Bach & Stark, 2004; Shumate, 2003, 2012; Shumate, Fulk, & Monge, 2005); use of hyperlink data to study the formation and role of alliances between SMOs 2010 – 2012 247,928,272 11,3259,655 US Media Previous studies of news media organizations (Greer & Mensing, 2006; Weber, 2012; Weber & Monge, In Press); focus on evolutionary patterns 2008 – 2012 1,315,132,555 539,184,823
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- 6. 6 News Media on the Web (Weber, Ognyanova, Kosterich & Nguyen, 2015)
- 7. To what degree are large-scale datasets reliable?
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- 17. • Scale out across multiple datasets: – US House – 2005:2013: – US Senate – 2005:2013 – Hurrican Katrina – 2003:2012: – Occupy Wall Street – 2010:2012 17
- 18. 0 5 10 15 20 25 30 050000010000001500000200000025000003000000 Potential vs. Actual URLs CountofPages 18t CountofURLs Potential Actual Difference
- 19. 19 0e+002e+064e+066e+06 Changes in Crawl Completeness CountofPages t CountofURLs OWS House Senate Katrina existing potential b = set a unit of time for analysis, c choosing n perios across a total time T
- 20. In the ideal case, it would be possible to create a factor that corrects for data degrade: bt How does this help? Each of the illustrated cases fits against an exponential function ~ b • Senate: 0.13 • House: 0.13 • Katrina: 0.02 • OWS: 0.10 20 ebt
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- 23. 23 Challenges are not unique to these data Courtesy of Marc Smith, NodeXL
- 24. Lessons Learned • Degradation is a factor in working with available large-scale data – In part, degradation is related to the provenance of the data – In turn, there is a need to record the origins of datasets (provenance) • Patterns of degradation prove problematic for statistical analyses – Ex: network analysis with snowball samples vs. whole network • Continued work needed to develop research guidelines as more scholars engage with this data 24
- 25. Get in contact with us: – matthew.weber@rutgers.edu – @mediareinvented The Team – Kris Carpenter, Vinay Goel, Internet Archive – David Lazer, Katherine Ognyanova, Northeastern University – Allie Kosterich, Hai Nguyen, Rutgers University Research supported by NSF Award #1244727 and the NetSCI Lab @ Rutgers
Editor's Notes
- There are many types of large-scale data… only talking about Internet based data… focusing on datasets that are re-used. - Markus - “social scientists are used to fine-grain, well-controlled data, and that doesn’t exist on the web”
- 20th Century Collection = 9TB of metadata Media Seed List = 4,891 For instance, researchers have proposed focusing archival efforts on capturing data that changes the most frequently, in order to capture the majority of new content [36]. Elsewhere, researchers have suggested that crawling strategies should prioritize archival efforts based on the size and relative position of websites within their larger ecosystems [37].
- Driscoll and Walker (2014) For instance, a comparison of Twitter data collected via a public API and data collected from a “fire hose” provided by GNIP PowerTrack, found significant differences between the two datasets. In most cases the PowerTrack data proved to be more powerful,
- 3 month windows of time…
- Also looked at the size of the webpages, and estimating out size… wasn’t as reliable.