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1) List 3 main takeaways you learned from the readings/videos this week, and how they illuminate issues with using Data & Big data by companiesUse these links and the file uploaded to answer this question.WRITE FROM 300-400 WORDS ONLYhttps://www.youtube.com/watch?v=TiaVGBEFiZshttps://hbr.org/2013/07/keep-up-with-your-quantshttps://hbr.org/2014/03/google-flu-trends-failure-shows-good-data-big-data
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FINAL FINAL
POLICYFORUM
BIG DATA
The Parable of Google Flu:
Traps in Big Data Analysis
Large errors in flu prediction were largely
avoidable, which offers lessons for the use
of big data.
David Lazer, 1, 2* Ryan Kennedy,1, 3, 4 Gary King,3 Alessandro Vespignani 3,5,6
I
CREDIT: ADAPTED FROM AXEL KORES/DESIGN & ART DIRECTION/ISTOCKPHOTO.COM
n February 2013, Google Flu
Trends (GFT) made headlines
but not for a reason that Google
executives or the creators of the flu
tracking system would have hoped.
Nature reported that GFT was predicting more than double the proportion of doctor visits for influenza-like illness (ILI) than the Centers for Disease Control and Prevention (CDC), which bases its estimates on surveillance reports from
laboratories across the United States
(1, 2). This happened despite the fact
that GFT was built to predict CDC
reports. Given that GFT is often held
up as an exemplary use of big data
(3, 4), what lessons can we draw
from this error?
The problems we identify are
not limited to GFT. Research on
whether search or social media can
predict x has become commonplace (5–7) and is often put in sharp contrast
with traditional methods and hypotheses.
Although these studies have shown the
value of these data, we are far from a place
where they can supplant more traditional
methods or theories (8). We explore two
issues that contributed to GFT’s mistakes—
big data hubris and algorithm dynamics—
and offer lessons for moving forward in the
big data age.
Big Data Hubris
“Big data hubris” is the often implicit
assumption that big data are a substitute
for, rather than a supplement to, traditional
data collection and analysis. Elsewhere, we
have asserted that there are enormous scientific possibilities in big data (9–11). However, quantity of data does not mean that
one can ignore foundational issues of mea1
Lazer Laboratory, Northeastern University, Boston, MA
02115, USA. 2Harvard Kennedy School, Harvard University,
Cambridge, MA 02138, USA. 3Institute for Quantitative Social
Science, Harvard University, Cambridge, MA 02138, USA.
4
University of Houston, Houston, TX 77204, USA. 5Laboratory
for the Modeling of Biological and Sociotechnical Systems,
Northeastern University, Boston, MA 02115, USA. 6Institute
for Scientific Interchange Foundation, Turin, Italy.
*Corresponding author. E-mail: d.lazer@neu.edu.
surement and construct validity and reliability and dependencies among data (12).
The core challenge is that most big data that
have received popular attention are not the
output of instruments designed to produce
valid and reliable data amenable for scientific analysis.
The initial version of GFT was a particularly problematic marriage of big and
small data. Essentially, the methodology
was to find the best matches among 50 million search terms to fit 1152 data points
(13). The odds of finding search terms that
match the propensity of the flu but are structurally unrelated, and so do not predict the
future, were quite high. GFT developers,
in fact, report weeding out seasonal search
terms unrelated to the flu but strongly correlated to the CDC data, such as those regarding high school basketball (13). This should
have been a warning that the big data were
overfitting the small number of cases—a
standard concern in data analysis. This ad
hoc method of throwing out peculiar search
terms failed when GFT completely missed
the nonseasonal 2009 influenza A–H1N1
pandemic (2, 14). In short, the initial version of GFT was part flu detector, part
winter detector. GFT engineers updated
the algorithm in 2009, and this
model has run ever since, with a
few changes announced in October
2013 (10, 15).
Although not widely reported
until 2013, the new GFT has been
persistently overestimating flu
prevalence for a much longer time.
GFT also missed by a very large
margin in the 2011–2012 flu season and has missed high for 100 out
of 108 weeks starting with August
2011 (see the graph). These errors
are not randomly distributed. For
example, last week’s errors predict
this week’s errors (temporal autocorrelation), and the direction and
magnitude of error varies with the
time of year (seasonality). These
patterns mean that GFT overlooks
considerable information that
could be extracted by traditional
statistical methods.
Even after GFT was updated in 2009,
the comparative value of the algorithm as a
stand-alone flu monitor is questionable. A
study in 2010 demonstrated that GFT accuracy was not much better than a fairly simple projection forward using already available (typically on a 2-week lag) CDC data
(4). The comparison has become even worse
since that time, with lagged models significantly outperforming GFT (see the graph).
Even 3-week-old CDC data do a better job
of projecting current flu prevalence than
GFT [see supplementary materials (SM)].
Considering the large number of
approaches that provide inference on influenza activity (16–19), does this mean that
the current version of GFT is not useful?
No, greater value can be obtained by combining GFT with other near–real-time
health data (2, 20). For example, by combining GFT and lagged CDC data, as well
as dynamically recalibrating GFT, we can
substantially improve on the performance
of GFT or the CDC alone (see the chart).
This is no substitute for ongoing evaluation
and improvement, but, by incorporating this
information, GFT could have largely healed
itself and would have likely remained out of
the headlines.
www.sciencemag.org SCIENCE VOL 343 14 MARCH 2014
1203
POLICYFORUM
Algorithm Dynamics
1204
10
Google Flu
Google Flu + CDC
% ILI
8
Lagged CDC
CDC
Google estimates more
than double CDC estimates
6
4
2
0
07/01/09
07/01/10
07/01/11
150
Error (% baseline)
All empirical research stands on a foundation of measurement. Is the instrumentation
actually capturing the theoretical construct of
interest? Is measurement stable and comparable across cases and over time? Are measurement errors systematic? At a minimum,
it is quite likely that GFT was an unstable
reflection of the prevalence of the flu because
of algorithm dynamics affecting Google’s
search algorithm. Algorithm dynamics are
the changes made by engineers to improve
the commercial service and by consumers in using that service. Several changes in
Google’s search algorithm and user behavior likely affected GFT’s tracking. The most
common explanation for GFT’s error is a
media-stoked panic last flu season (1, 15).
Although this may have been a factor, it cannot explain why GFT has been missing high
by wide margins for more than 2 years. The
2009 version of GFT has weathered other
media panics related to the flu, including the
2005–2006 influenza A/H5N1 (“bird flu”)
outbreak and the 2009 A/H1N1 (“swine flu”)
pandemic. A more likely culprit is changes
made by Google’s search algorithm itself.
The Google search algorithm is not a
static entity—the company is constantly
testing and improving search. For example,
the official Google search blog reported 86
changes in June and July 2012 alone (SM).
Search patterns are the result of thousands of
decisions made by the company’s programmers in various subunits and by millions of
consumers worldwide.
There are multiple challenges to replicating GFT’s original algorithm. GFT has never
documented the 45 search terms used, and
the examples that have been released appear
misleading (14) (SM). Google does provide
a service, Google Correlate, which allows
the user to identify search data that correlate
with a given time series; however, it is limited to national level data, whereas GFT was
developed using correlations at the regional
level (13). The service also fails to return any
of the sample search terms reported in GFTrelated publications (13, 14).
Nonetheless, using Google Correlate to
compare correlated search terms for the GFT
time series to those returned by the CDC’s
data revealed some interesting differences. In
particular, searches for treatments for the flu
and searches for information on differentiating the cold from the flu track closely with
GFT’s errors (SM). This points to the possibility that the explanation for changes in relative search behavior is “blue team” dynamics—where the algorithm producing the data
(and thus user utilization) has been modi-
Google Flu
Google Flu + CDC
100
07/01/12
07/01/13
Google starts estimating
high 100 out of 108 weeks
Lagged CDC
50
0
–50
07/01/09
07/01/10
07/01/11
07/01/12
07/01/13
Data
GFT overestimation. GFT overestimated the prevalence of flu in the 2012–2013 season and overshot the
actual level in 2011–2012 by more than 50%. From 21 August 2011 to 1 September 2013, GFT reported overly
high flu prevalence 100 out of 108 weeks. (Top) Estimates of doctor visits for ILI. “Lagged CDC” incorporates
52-week seasonality variables with lagged CDC data. “Google Flu + CDC” combines GFT, lagged CDC estimates,
lagged error of GFT estimates, and 52-week seasonality variables. (Bottom) Error [as a percentage {[Non-CDC
estmate)⫺(CDC estimate)]/(CDC) estimate)}. Both alternative models have much less error than GFT alone.
Mean absolute error (MAE) during the out-of-sample period is 0.486 for GFT, 0.311 for lagged CDC, and 0.232
for combined GFT and CDC. All of these differences are statistically significant at P < 0.05. See SM. fied by the service provider in accordance with their business model. Google reported in June 2011 that it had modified its search results to provide suggested additional search terms and reported again in February 2012 that it was now returning potential diagnoses for searches including physical symptoms like “fever” and “cough” (21, 22). The former recommends searching for treatments of the flu in response to general flu inquiries, and the latter may explain the increase in some searches to distinguish the flu from the common cold. We document several other changes that may have affected GFT (SM). In improving its service to customers, Google is also changing the data-generating process. Modifications to the search algorithm are presumably implemented so as to support Google’s business model—for example, in part, by providing users useful information quickly and, in part, to promote more advertising revenue. Recommended searches, usually based on what others have searched, will increase the relative magnitude of certain searches. Because GFT uses the relative prevalence of search terms in its model, improvements in the search algorithm can adversely affect GFT’s estimates. Oddly, GFT bakes in an assumption that relative search volume for certain terms is statically related to external events, but search behavior is not just exogenously determined, it is also endogenously cultivated by the service provider. Blue team issues are not limited to Google. Platforms such as Twitter and Facebook are always being re-engineered, and whether studies conducted even a year ago on data collected from these platforms can be replicated in later or earlier periods is an open question. Although it does not appear to be an issue in GFT, scholars should also be aware of the potential for “red team” attacks on the systems we monitor. Red team dynamics occur when research subjects (in this case Web searchers) attempt to manipulate the datagenerating process to meet their own goals, such as economic or political gain. Twitter polling is a clear example of these tactics. Campaigns and companies, aware that news media are monitoring Twitter, have used numerous tactics to make sure their candidate or product is trending (23, 24). Similar use has been made of Twitter and Facebook to spread rumors about stock prices and markets. Ironically, the more successful we become at monitoring the behavior of people using these open sources of information, the more tempting it will be to manipulate those signals. 14 MARCH 2014 VOL 343 SCIENCE www.sciencemag.org POLICYFORUM Transparency, Granularity, and All-Data The GFT parable is important as a case study where we can learn critical lessons as we move forward in the age of big data analysis. Transparency and Replicability. Replication is a growing concern across the academy. The supporting materials for the GFTrelated papers did not meet emerging community standards. Neither were core search terms identified nor larger search corpus provided. It is impossible for Google to make its full arsenal of data available to outsiders, nor would it be ethically acceptable, given privacy issues. However, there is no such constraint regarding the derivative, aggregated data. Even if one had access to all of Google’s data, it would be impossible to replicate the analyses of the original paper from the information provided regarding the analysis. Although it is laudable that Google developed Google Correlate ostensibly from the concept used for GFT, the public technology cannot be utilized to replicate their findings. Clicking the link titled “match the pattern of actual flu activity (this is how we built Google Flu Trends!)” will not, ironically, produce a replication of the GFT search terms (14). Oddly, the few search terms offered in the papers (14) do not seem to be strongly related with either GFT or the CDC data (SM)—we surmise that the authors felt an unarticulated need to cloak the actual search terms identified. What is at stake is twofold. First, science is a cumulative endeavor, and to stand on the shoulders of giants requires that scientists be able to continually assess work on which they are building (25). Second, accumulation of knowledge requires fuel in the form of data. There is a network of researchers waiting to improve the value of big data projects and to squeeze more actionable information out of these types of data. The initial vision regarding GFT—that producing a more accurate picture of the current prevalence of contagious diseases might allow for life-saving interventions—is fundamentally correct, and all analyses suggest that there is indeed valuable signal to be extracted. Google is a business, but it also holds in trust data on the desires, thoughts, and the connections of humanity. Making money “without doing evil” (paraphrasing Google’s motto) is not enough when it is feasible to do so much good. It is also incumbent upon academia to build institutional models to facilitate collaborations with such big data projects—something that is too often missing now in universities (26). Use Big Data to Understand the Unknown. Because a simple lagged model for flu prevalence will perform so well, there is little room for improvement on the CDC data for model projections [this does not apply to other methods to directly measure flu prevalence, e.g., (20, 27, 28)]. If you are 90% of the way there, at most, you can gain that last 10%. What is more valuable is to understand the prevalence of flu at very local levels, which is not practical for the CDC to widely produce, but which, in principle, more finely granular measures of GFT could provide. Such a finely granular view, in turn, would provide powerful input into generative models of flu propagation and more accurate prediction of the flu months ahead of time (29–33). Study the Algorithm. Twitter, Facebook, Google, and the Internet more generally are constantly changing because of the actions of millions of engineers and consumers. Researchers need a better understanding of how these changes occur over time. Scientists need to replicate findings using these data sources across time and using other data sources to ensure that they are observing robust patterns and not evanescent trends. For example, it is eminently feasible to do controlled experiments with Google, e.g., looking at how Google search results will differ based on location and past searches (34). More generally, studying the evolution of socio-technical systems embedded in our societies is intrinsically important and worthy of study. The algorithms underlying Google, Twitter, and Facebook help determine what we find out about our health, politics, and friends. It’s Not Just About Size of the Data. There is a tendency for big data research and more traditional applied statistics to live in two different realms—aware of each other’s existence but generally not very trusting of each other. Big data offer enormous possibilities for understanding human interactions at a societal scale, with rich spatial and temporal dynamics, and for detecting complex interactions and nonlinearities among variables. We contend that these are the most exciting frontiers in studying human behavior. However, traditional “small data” often offer information that is not contained (or containable) in big data, and the very factors that have enabled big data are enabling more traditional data collection. The Internet has opened the way for improving standard surveys, experiments, and health reporting (35). Instead of focusing on a “big data revolution,” perhaps it is time we were focused on an “all data revolution,” where we recognize that the critical change in the world has been innovative analytics, using data from all traditional and new sources, and providing a deeper, clearer understanding of our world. References and Notes 1. D. Butler, Nature 494, 155 (2013). 2. D. R. Olson et al., PLOS Comput. Biol. 9, e1003256 (2013). 3. A. McAfee, E. Brynjolfsson, Harv. Bus. Rev. 90, 60 (2012). 4. S. Goel et al., Proc. Natl. Acad. Sci. U.S.A. 107, 17486 (2010). 5. A. Tumasjan et al., in Proceedings of the 4th International AAAI Conference on Weblogs and Social Media, Atlanta, Georgia, 11 to 15 July 2010 (Association for Advancement of Artificial Intelligence, 2010), pp. 178. 6. J. Bollen et al., J. Comput. Sci. 2, 1 (2011). 7. F. Ciulla et al., EPJ Data Sci. 1, 8 (2012). 8. P. T. Metaxas et al., in Proceedings of PASSAT—IEEE Third International Conference on Social Computing, Boston, MA, 9 to 11 October 2011 (IEEE, 2011), pp. 165; doi:10.1109/PASSAT/SocialCom.2011.98. 9. D. Lazer et al., Science 323, 721 (2009). 10. A. Vespignani, Science 325, 425 (2009). 11. G. King, Science 331, 719 (2011). 12. D. Boyd, K. Crawford Inform. Commun. Soc. 15, 662 (2012). 13. J. Ginsberg et al., Nature 457, 1012 (2009). 14. S. Cook et al., PLOS ONE 6, e23610 (2011). 15. P. Copeland et al., Int. Soc. Negl. Trop. Dis. 2013, 3 (2013). 16. C. Viboud et al., Am. J. Epidemiol. 158, 996 (2003). 17. W. W. Thompson et al., J. Infect. Dis. 194 (Suppl. 2), S82–S91 (2006). 18. I. M. Hall et al., Epidemiol. Infect. 135, 372 (2007). 19. J. B. S. Ong et al., PLOS ONE 5, e10036 (2010). 20. J. R. Ortiz et al., PLOS ONE 6, e18687 (2011). 21. Organizing lists of related searches, Google; http:// insidesearch.blogspot.com/2011/06/organizing-lists-ofrelated-searches_16.html. 22. Improving health searches, because your health matters, Google; http://insidesearch.blogspot.com/2012/02/ improving-health-searches-because-your.html. 23. E. Mustafaraj, P. Metaxas, in Proceedings of the WebSci10, Raleigh, NC, 26 and 27 April 2010 (Web Science Trust, 2010); http://journal.webscience.org/317/. 24. J. Ratkiewicz et al., in Proceedings of 5th International AAAI Conference on Weblogs and Social Media, San Francisco, CA, 7 to 11 August 2011 (AAAI, 2011), p. 297–304. 25. G. King, PS Polit. Sci. Polit. 28, 443 (1995). 26. P. Voosen, Chronicle of Higher Education, 13 September 2013; http://chronicle.com/article/Researchers-Struggleto-Secure/141591/. 27. R. Lazarus et al., BMC Public Health 1, 9 (2001). 28. R. Chunara et al., Online J. Public Health Inform. 5, e133 (2013). 29. D. Balcan et al., Proc. Natl. Acad. Sci. U.S.A. 106, 21484 (2009). 30. D. L. Chao et al., PLOS Comput. Biol. 6, e1000656 (2010). 31. J. Shaman, A. Karspeck, Proc. Natl. Acad. Sci. U.S.A. 109, 20425 (2012). 32. J. Shaman et al., Nat. Commun. 4, 2837 (2013). 33. E. O. Nsoesi ... Purchase answer to see full attachment

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