Common weaknesss of Big Data analysis:

There are legion illustrations of how Large Datas can be used to calculate the populaces reaction when it comes to package office grosss, sale of consumer goods and the result of certain events such as American Idol. However, even in the instance of the of foretelling something as laughably inane as American Idol there are makings that need to be made about the usage of the informations collected. “As many writers have pointed out, there

are several challenges one must confront when covering with informations of this nature: intrinsic prejudices, uneven trying across location of involvement etc.” American Idol.

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While the experiment in American graven image is mostly viewed as success it concludes that the unfastened beginning informations available on the web can be used to do educated

conjectures on the result of social events. Surely an educated conjecture is nil to acquire excited about.

This subdivision of the paper points efforts to convey to illume the weaknesss in analysis of informations sourced from societal media such as chirrup or from hunt footings used in Google hunts. We focus on three distinct countries which have attempted to utilize these beginnings of information to foretell future results of some event. These countries are:

  • Elections
  • Flu Tendencies
  • Stock Market tendencies


Shortly after the 2010 US general elections showy statements made it to the intelligence media headlines, from those reasoning that Twitter is non a dependable forecaster to those claiming the antonym ( How non to foretell elections ) .

It has been claimed that Twitter can foretell the result of elections with great truth. Given the important differences in the demographics between likely electors and users of societal webs inquiries arise on what is the underlying operating rule enabling these anticipations ( How non to foretell elections ) .

As is reported by “How non to foretell elections” the grade of truth in these claims is recorded in footings of per centum of right guessed electoral races without any farther making at all.

When these anticipations are reported they are frequently non compared against consequences which were arrived at by more traditional agencies. For case in the 2008 US congressional elections the officeholder in won 91.6 % of the clip and in 2010 they won 84 % of the clip. By utilizing this parametric quantity that the incumbent wins about 9 times out of 10 any random member of the populace could walk off the street and predict 90 % of US congressional elections at really small cost.

A Livne, M Simmons, E Adar and L Adamic, “The Party is over here construction and content in the 2010 Election” used tweets sent by electoral campaigners to construct a theoretical account that was claimed would foretell “a campaigner will win with truth of 88 % .” Taken out of context this might look strong but compared with the work stoppage rate for utilizing tenure as the lone parametric quantity it seems a batch of work for small in the manner of touchable consequences, or as “How non to foretell elections set it” “even when anticipations were better than opportunity they were non competent when compared to the fiddling method of foretelling through incumbency” .

Tamasjan et Al who carried similar work out in Germany found that chirrup is used to distribute political sentiment discuss political relations and that sentiment profiles of politicians and parties reflect niceties of the election run and that the mere volume of messages reflects the election consequence and “even comes near to traditional election poles” . It seems as if poll takers have nil to worry about in footings of employment.

A major issue with societal media informations where general elections are concerned is that the people twirping can non be identified as likely electors. To place likely electors a right sample from Twitter would hold to should be able to place the age scope, voting eligibility and anterior vote forms ( How non to foretell elections ) . Obtaining this information is non possible without go againsting the privateness of the users, a peculiarly hot subject of argument for societal media suppliers at the minute. There are surely electors who do non twirp and given the age scope of likely electors in the US ( in 2000 36 % of citizens aged between 18 and 24 voted, 50 % of citizens between 25 and 50 voted and 68 % of those over 35 voted ) while we have no back uping information we’ll put our reputes on the line and state as age additions in todays population the proportion of societal media users likely diminutions while the exact opposite happens to the proportion of likely electors as age additions. This can non be good for the truth of election anticipation by informations gathered from societal media.

It should besides be noted that it is easy to pull strings societal media informations. Far be it from me to propose that politicians are capable of sucas this headline and exerpt from the Technology Review June 2012 demonstrates there are those who will halt at nil to win. “Twitter Mischief Plagues Mexico’s General Election, The top rivals in Mexico’s presidential run are engaged in a Twitter spam war, with ground forcess of “bots” programmed to project slurs on opposing campaigners and interrupt their social-media attempts. This large-scale political spamming could bode on-line jokes that candidates may progressively fall back to in other countries.”

Flu Tendencies:

Google Flu Trends ( GFT ) was launched in November 2008 and is based on the fact that Google users on a regular basis use Google to seek for advice on wellness issues. By analyzing the hunt footings from users Google efforts to foretell flu tendencies.

The Swine Flu pandemic of 2009 provided the first chance to measure the public presentation of GFT theoretical accounts during a non-seasonal grippe eruption. GFT missed it. Equally good as this GFT overestimated the prevalence of grippe in the 2012–2013 season and overshot the existent degree in 2011–2012 by more than 50 % . From 21 August 2011 to 1 September 2013, GFT reported excessively high grippe prevalence 100 out of 108 hebdomads ( The Parable of Google Flu Trends ) .

In February 2013, Google Flu Trends ( GFT ) made headlines but non for a ground that Google Executives or the Godhead of the grippe tracking system ( The fable of the Google Flu Trends ) . Nature reported that GFT was foretelling more than double the proportion of physician visits for influenza-like unwellness than the Centers for Disease Control and Prevention ( CDC ) , which bases its estimations on surveillance studies from research labs across the United States ( D. Butler, Nature 494, 155 ( 2013 ) & A ; D. R. Olson et al. , PLOS Comput. Biol. 9, e1003256 ( 2013 ) ) . This happened despite the fact that GFT was built to foretell CDC studies ( Parable of Google Flu Trends ) .

In the Parable of Google Flu Trends, Lazer et al refer to “Big Data Hubris” as being the inexplicit premise that Big Data are a replacement for instead than a addendum to traditional informations aggregation and travel on to foreground that measure entirely does non intend one can disregard the foundational issues such as measuring, concept cogency and dependences among informations. Like in the old subdivision on elections it seems that informations gathered through societal media does non yet compare to the tested and tested methods.

Lazer et Al took

GFT’s chief job appears to be that it relies on the populace to cognize what the symptoms of the grippe are. If person Googles flu symptoms they may merely hold a cold.

For decision:

While people twirping, showing an sentiment or seeking about a merchandise or a film are more than probably the mark market and a good indicant of a future purchase the same can non be said of elections. Where the client has self selected as a client a elector has non.