Predicting Online Buzz and Audience                Predicting Online Buzz and Audience                             The Nex...
Predicting Online Buzz and AudienceIntroduction	With the rise of the Internet, news coverage and accessibility have greatl...
Predicting Online Buzz and AudienceTragic events provoke rapid spikes in online comments beforefading into the distanceThe...
Predicting Online Buzz and AudienceGoogle set up a “Person Finder” web app3 to allow families and victims to reunite, and ...
Predicting Online Buzz and AudienceOnline audiences and buzz for planned political events are eas-ily predictableAudience ...
Predicting Online Buzz and Audience                                  Transport Strike    200    180    160    140    120  ...
Predicting Online Buzz and AudienceSport and television events hold audience captiveAthletic events hold audiences captive...
Predicting Online Buzz and Audience                          “Koh Lanta” Reality TV Show400350300250200150100 50  0      0...
Predicting Online Buzz and AudienceConclusionAn analysis of online buzz and its related predictions of online audiences is...
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Predictive Analytics Using Social Media

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Predictive Analytics Using Social Media

  1. 1. Predicting Online Buzz and Audience Predicting Online Buzz and Audience The Next Step in New Market Research Summary Introduction.... 2 Tragic events provoke rapid spikes in online comments before fading into the distance.... 3 Online audiences for planned political events are easily predictable.... 5 Sport and television events hold audiences captive.... 7 Conclusion.... 8Synthesio - Predicting Online Buzz and Audience - April 2011 1
  2. 2. Predicting Online Buzz and AudienceIntroduction With the rise of the Internet, news coverage and accessibility have greatly increased. With the number of siteshousing printed publications, news agencies, pure players, and blogs, etc., the number of articles covering currentevents continues to grow and to fuel the popular Internet phenomenon known as “buzz”. Social media has acrucial role in this phenomenon; nearly one third of news publications comes from blogs.Synthesio teamed with Bouygues e-lab1 to study and model current event buzz levels on the web and the way itspreads across media sites, blogs, and video-sharing platforms. The study also looked at how the buzz can bebroken down among the various sites, and at what the publishing cycles look like for certain events.Is it possible to analyze the impact of global events on the publication of online news articles, and thus indirectlyon the audience of websites? Media channels are rapidly changing, but we can measure media as well as the wayconsumers are using media to adapt. Number of publications May 2010 1 000 000 900 000 800 000 700 000 600 000 500 000 400 000 300 000 200 000 100 000 0 11 01 03 05 07 09 13 15 17 19 21 23 25 27 29 31 02 04 06 5- 5- 5- 5- 5- 5- 5- 5- 5- 5- 5- 5- 5- 5- 5- 5- 6- 6- 6- -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20Media publications typically have regular patterns. The graph below shows the number of media publicationsduring May of 2010, the period we based our study upon. Typical news outlets follow this pattern, posting far lesson the weekend than during the week. 100% 90% 80% 70% 60% 50% News articles 40% Videos 30% Blog posts 20% 10% 0% 1 2 1 2 3 4 4 5 6 7 8 9 0 -1 -1 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -1 09 09 10 10 10 10 09 09 09 09 09 09 09 20 20 20 20 20 20 20 20 20 20 20 20 20Synthesio and Bouygues e-lab based this study upon sources that relay news first-hand: media news stories, vid-eos, and blog posts, broken down along the percentages above. The sources are all French language sources,meaning France, Belgium, Canada, etc.Synthesio - Predicting Online Buzz and Audience - April 2011 2
  3. 3. Predicting Online Buzz and AudienceTragic events provoke rapid spikes in online comments beforefading into the distanceThe traces of a celebrity death are visible for up to one monthCelebrity deaths nowadays grab people’s attention for one reason or another, but are then quickly forgotten aspeople move onto new news. Michael Jackson’s death was one of the most marked in recent history, shutting downboth Facebook and Twitter as millions of comments poured onto the Internet, and one that attracted an inordinateamount of media attention. Even Jackson’s Wikipedia page was updated before some major media were able toreport on the event. A typical curve is represented to the right, with an example given from the death of Mano Soloin January of 2010. With all of this information pouring out, it must be asked – who benefits from this informationoutpour?Media outlets race to be the first to report a celebrity death, yet there can be large gaps of lag time like there wasin the case of Michael Jackson’s death, between an announcement from TMZ and Twitter and those from theHuffington Post and the New York Times1. This phenomenon highlights the struggle faced by “traditional” mediatoday to maintain an exclusive hold on current events. The Death of Mano Solo 400 350 300 250 200 150 100 50 0 10 10 10 10 10 0 0 0 0 0 /2 /2 /2 /2 /2 1 2 3 4 5 /0 /0 /0 /0 /001 01 01 01 01Even the most destructive natural disasters leave their traces online for only 1 or2 monthsThe 2010 earthquake in Haïti was one of the first in recent years to bring attention to the impact on social media.There was an outpour of online comments similar to other natural disasters tested including the BP oil spill crisisand the volcanic explosion of Eyjafjöll in Iceland in 2010 (see graph at right). When one of the largest recordedtsunamis in history hit Japan’s coast on March 11, 2011 following an 8.9 magnitude earthquake, the number oftweets alone exceeded 1,200 per minute2. Just on blogs, forums, and news sites alone, there were over 63,000mentions of Japan online on March 11th. Over the past month March-April 2011, there has been a total of 1,655,561comments from these sites. On Twitter, hashtags (key words tagged with a pound sign that can then be used tosearch for other tweets including the same word) #japan, #tsunami, #prayforjapan and others almost immediatelybecame trending topics.1 Michael Jackson’s death and its lessons for online journalists covering breaking news2 Twitter Reacts To Massive Quake, Tsunami In JapanSynthesio - Predicting Online Buzz and Audience - April 2011 3
  4. 4. Predicting Online Buzz and AudienceGoogle set up a “Person Finder” web app3 to allow families and victims to reunite, and as expected, Yahooannounced more than 1 million clicks per hour to its media coverage of the tsunami, reporting traffic five timeshigher than usual throughout the day.The conversations around Japan are still within the 1-2 month time range during which comments online (and thusonline audiences looking for this information) stagnate and then fall off. Haiti Earthquake 2500 2000 1500 1000 500 0 10 10 10 10 10 10 20 20 20 20 20 20 1/ 2/ 3/ 4/ 5/ 6/ /0 /0 /0 /0 /0 /0 01 01 01 01 01 014 P. Cattin, R. Festa, A. Le Diberder, A Model for Forecasting the Audience of TV Programs. Worldwide Electronic and Broadcasting Audience Re-search SymposiumSynthesio - Predicting Online Buzz and Audience - April 2011 4
  5. 5. Predicting Online Buzz and AudienceOnline audiences and buzz for planned political events are eas-ily predictableAudience levels for an election take off 3 months priorMeasuring captive audiences is nothing new for elections, but now that audiences are increasingly watching TVonline or surfing the web while watching TV, it is necessary to know when and where these audiences arecaptive. The number of studies predicting audience levels (online or on TV) for an election is limited, as moststudies focus on panel studies4, and predictions are often based on past numbers. While the accompanying graphsare based on French presidential elections and thus a French electoral time frame, it has become evident thatonline audiences are active at a given time before a political event, rise at predictable moments, and quickly falloff afterwards. In the case of the events studied, Internet buzz took off 3 months prior to elections, peaked duringelections, and rather quickly died off afterwards. It should be stressed that these audience levels are for plannedpolitical events, with an emphasis on elections. In the case of recent uprisings in the Middle East, events are lessstable and therefore more difficult to predict. Regional Elections 2010 2000 1800 1600 1400 1200 1000 800 600 400 200 0 10 10 10 10 10 10 20 20 20 20 20 20 1/ 2/ 3/ 4/ 5/ 6/ /0 /0 /0 /0 /0 /001 01 01 01 01 01Daily articles during strikes draw a regular audience throughoutOnline content and its audience during strikes are interesting events to analyze as there is a regular publicationof articles throughout the strike with spikes occurring at predictable moments. Although this example does notapply to all countries, it is interesting to note moments at which certain country audiences are captive on aregular basis. We can look at the example of a strike in France during the spring of 2010. Although there were twodays that saw extremely high levels of online buzz, publications were consistent throughout the entire period.This type of event is key for not only media outlets to understand but also companies wishing to protect theirreputations, online or elsewhere.Airline and other travel companies know the weight that strikes can have on their online and offline reputations.Negative media on a regular basis, along with the comments that accompany them, are common fare.Just last fall, London tube strikes in October and November trended on Twitter, and online comments followed thepattern observed by Synthesio and Bouygues e-lab. Two phenomena are particular to this type of event: the racebetween media sites and “citizen journalists” to be the first to deliver the news, and crowdsourcing in journalism.“Citizen journalists” can be anyone that has a smartphone and an Internet connection at his/her disposal,contributing to online buzz and alerting fellow travelers of the strike situation. There are over 100,000 blogmentions alone for “London tube strike” and 321 videos on YouTube.Synthesio - Predicting Online Buzz and Audience - April 2011 5
  6. 6. Predicting Online Buzz and Audience Transport Strike 200 180 160 140 120 100 80 60 40 20 0 10 10 10 10 10 10 20 20 20 20 20 20 1/ 2/ 3/ 4/ 5/ 6/ /0 /0 /0 /0 /0 /001 01 01 01 01 01Synthesio - Predicting Online Buzz and Audience - April 2011 6
  7. 7. Predicting Online Buzz and AudienceSport and television events hold audience captiveAthletic events hold audiences captive throughout the eventAn analysis of several athletic events, including the Giro d’Italia (Tour of Italy), the 2010 World Cup, Roland Garros,and the Vancouver Olympic Games, showed that online buzz on blogs, forums, and news sites rises just before theevent, is steady throughout, and drops off sharply after the event. In terms of sponsorship and advertising, this isan interesting phenomenon to understand. Content surrounding these events is only present directly before theevent, therefore narrowing while defining the time period during which audiences are reading about it.Certain brands certainly took advantage of specific matches during the World Cup for launching carefully craftedads, such as Adidas’s Star Wars-themed ad featuring David Beckham, Franz Beckenbauer, Snoop Dogg, and others,launching the campaign online as well. Media outlets also took advantage of a TV-Internet mix by launchingproprietary viewers to draw fans: BBC’s iPlayer, and ITV Player’s online services, for example6. The Giro d’Italia (Tour of Italy) 250 200 150 100 50 0 10 10 10 10 10 10 20 20 20 20 20 20 1/ 2/ 3/ 4/ 5/ 6/ /0 /0 /0 /0 /0 /001 01 01 01 01 01Buzz around reality TV shows is similar, with maximum communication at the beginningand endFinally, reality TV shows are another spectrum that is possible to predict, as shown by the graph to the right.Studies carried out by Synthesio and Bouygues e-lab show that online buzz peaks at the beginning and end of ashow, with regular online publications throughout. This sample graph was taken from the evaluation of severalreality TV shows including a show called “Koh Lanta”, pictured below.Not only are reality shows less expensive to produce than sitcoms, their audiences are easier to predict as theyrun during a set, predictable time period. Brands are cashing in on these captive audiences not only via directedadvertising but also via product placement and partnership programs. For the next episode of “Big Brother”, forexample, none other than Coca Cola will be “amplifying” the show, i.e. sponsoring7.5 World  Cup  2010:  millions  of  women  will  watch  - but  the  ads  will  aim  at  men  |  Media  |  guardian.co.uk  6 Coca Cola to sponsor Big Brother amplifiedSynthesio - Predicting Online Buzz and Audience - April 2011 7
  8. 8. Predicting Online Buzz and Audience “Koh Lanta” Reality TV Show400350300250200150100 50 0 07/03/2010 07/04/2010 07/05/2010Synthesio - Predicting Online Buzz and Audience - April 2011 8
  9. 9. Predicting Online Buzz and AudienceConclusionAn analysis of online buzz and its related predictions of online audiences is a study that is highly related to thegeographical region/country that is being studied. French data was used in the initial study carried out bySynthesio and Bouygues e-lab, but other countries’ information could surely be analyzed in a similar manner.There are opportunities for media outlets and brands in various sectors that can predict the amplification ofcaptive audiences around a certain topic and predictable dates. As more marketing dollars move to the Internet,predicting where audiences will be can also help advertisers determine where to best spend their budgets. About Synthesio Synthesio is a global, multi-lingual Social Media Monitoring and research company, utilizing a powerful hybrid of tech and human monitoring services to help Brands and Agencies collect and analyze consumer conversations online. The result is actionable analytics and insights that provide an accurate snapshot of a brand and help answer the ultimate questions – how are we really doing right now, and how can we make it better. Founded in 2006, the company has grown to include analysts who provide native-language monitoring and analytic services in over 30 lanuages worldwide. Brands such as Toyota, Microsoft, Sanofi, Accor, Orange and many other well-known companies turn to Synthesio for the data they need to engage with their markets, an- ticipate and prepare for emerging crisis situations, and prepare for new product or new campaign launches.Synthesio - Predicting Online Buzz and Audience - April 2011 9

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