From Big Data to Big Theory: Lessons Learned from Archival Internet Research.

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Matthew S. Weber presented research at the 130th Annual Meeting of Big Data, Big Theory & The Thread of Recent History. The presentation discussed analyzing large-scale datasets to study how complete they are over time. It found datasets on political events and natural disasters became less complete as more webpages and URLs were added over multiple crawls. However, the rate of incompleteness followed exponential functions and could be corrected for using established factors for each dataset. While reliability challenges are not unique, understanding degradation rates can help researchers account for gaps in large internet-sourced datasets.

Data & Analytics

Matthew S. Weber
Rutgers University
@docmattweber
Presented At
130th Annual Meeting of
Big Data, Big Theory &
The Thread of Recent History

Pekin Daily Times, Pekin, IL, October 8, 2013
Credit: Pekin Daily Times

What’s in the data?
7
Source | Destination | Date | Frequency | Content Type | Bytes | Content
Link Data:
http://gawker.com/5953665/mitt-romneys-
staff-played-the-media-covering-them-in-a-
friendly-game-of-flag-football
Mitt Romney's Staff Played the Media Covering
Them in a Friendly Game of Flag
http://gawker.com
2012-10-22

13
News Media on the Web
(Weber, Ognyanova, Kosterich & Nguyen, 2015)

17
0
1
2
3
4
5
6
7
0
100
200
300
400
500
600
700
800
900
1000
2007 2008 2009 2010 2011 2012
Avg.MBperWebpage
Avg.NumberofWebpages
NJ.com Domain Analysis
Number of Pages Avg MB

18
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

To what degree are large-scale datasets reliable?

0 5 10 15 20 25 30
050000010000001500000200000025000003000000
Potential vs. Actual URLs
CountofPages
23t
CountofURLs
Potential
Actual
Difference

24
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 periods across a total time T

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
25
ebt

26
Challenges are not unique to these
data
Courtesy of Marc Smith, NodeXL

Research support from:
NSF Award #1244727; Additional support from the NetSCI Lab @ Rutgers

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Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.

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Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is Multiply with different modes (map) 1. Performance of sequential execution based vs OpenMP based vector multiply. 2. Comparing various launch configs for CUDA based vector multiply. Sum with different storage types (reduce) 1. Performance of vector element sum using float vs bfloat16 as the storage type. Sum with different modes (reduce) 1. Performance of sequential execution based vs OpenMP based vector element sum. 2. Performance of memcpy vs in-place based CUDA based vector element sum. 3. Comparing various launch configs for CUDA based vector element sum (memcpy). 4. Comparing various launch configs for CUDA based vector element sum (in-place). Sum with in-place strategies of CUDA mode (reduce) 1. Comparing various launch configs for CUDA based vector element sum (in-place).

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1. Matthew S. Weber Rutgers University @docmattweber Presented At 130th Annual Meeting of Big Data, Big Theory & The Thread of Recent History

2. Credit: Flickr @ilovecology

3. Pekin Daily Times, Pekin, IL, October 8, 2013 Credit: Pekin Daily Times

4. 4

7. What’s in the data? 7 Source | Destination | Date | Frequency | Content Type | Bytes | Content Link Data: http://gawker.com/5953665/mitt-romneys- staff-played-the-media-covering-them-in-a- friendly-game-of-flag-football Mitt Romney's Staff Played the Media Covering Them in a Friendly Game of Flag http://gawker.com 2012-10-22

8. 8

10.

11.

12.

13. 13 News Media on the Web (Weber, Ognyanova, Kosterich & Nguyen, 2015)

14.

15.

16. NJ Local News: 2007 - 2012

17. 17 0 1 2 3 4 5 6 7 0 100 200 300 400 500 600 700 800 900 1000 2007 2008 2009 2010 2011 2012 Avg.MBperWebpage Avg.NumberofWebpages NJ.com Domain Analysis Number of Pages Avg MB

18. 18 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

19. To what degree are large-scale datasets reliable?

20. 20

21. 21

22. 22

23. 0 5 10 15 20 25 30 050000010000001500000200000025000003000000 Potential vs. Actual URLs CountofPages 23t CountofURLs Potential Actual Difference

24. 24 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 periods across a total time T

25. 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 25 ebt

26. 26 Challenges are not unique to these data Courtesy of Marc Smith, NodeXL

27. 27

28. Research support from: NSF Award #1244727; Additional support from the NetSCI Lab @ Rutgers

Editor's Notes

Emporer Penguins… huddling together for survival... Population... Interacting in a large ecosystem with other animals.
Emporer Penguins… huddling together for survival... Population... Interacting in a large ecosystem with other animals.
WhiteHouse.gov press release from May 1, 2003, archived on May 6, 2003
WhiteHouse.gov press release from May 1, 2003, archived on October 1, 2003
July 14, 2006
July 14, 2006
February 25 2011
Correlations between outgoing link vectors to show profile similarities
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…

From Big Data to Big Theory: Lessons Learned from Archival Internet Research.

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