How a Viral Message Spreads?

A couple of years ago – and still today – any unexplained phenomenon that included social media would simply be named “viral”. Virality means that idea/news/meme starts to spread without the help of the original source. This spreading happens by the infectious nature of the idea or by the influence of those who have come to contact to the viral agent. This has been both the dream and the nightmare of the marketing and PR departments. Last week the nightmare scenario occurred to the The Copyright Information and Anti-Piracy Centre in Finland (CIAPC).

In the spring 2012 the CIAPC contacted a Finnish man claiming copyright violations in a P2P network. Long story short: After some threatening emails by the CIAPC and the man’s refusal to pay a compensation the police performed a search and seizure operation. The seizure part included a 9-year old girl’s Winnie the Pooh labeled laptop. Last week, the man described the situation in a Facebook post (original in Finnish) that suddenly started spread through the Facebook’s share-function. Soon the main stream media caught on (first online article in Finnish) and by the end of the next day in even made its way to international media (TorrentFreak and TechCrunch). The result: a PR disaster for the CIAPC.

The original post has at the moment 2 655 Facebook shares. The video below is the dynamic network of the public sharing of the post. The data was gathered by Mikael Rekola using the 99analytics.com  social media analytics platform. Each line represents a share of post, not the post views.

The video visualizes how the original post was shared and re-shared in the first 48 hours of its publication. Largest portion of the shares happened before middle part of the video within the first 24 hours. Actually, 97 % of the public shares happened during the 48 hour period. Those users whose shares got the most re-shares joined the game early.  This means that that the time is of the essence if the PR departments wish to react to these events.

When we look at the time scale of the spreading, we notice a sharp increase in the shared posts between 8 and 11 A.M..  The cascade started from several sources, including politicians  Dan Koivulaakso and Jyrki Kasvi,  who shared the post around 9 A.M. The first news story increased the shares of the original post. Before that, most of the shares were re-shares. The role of main stream media in facilitating these viral phenomenon cannot be forgotten.

The picture below that holds all of the shares. Size of the node represents the amount of shares the user caused and the brightness of the color of the node represents the amount of comments and likes the share received.

Almost all of the shares became directly from the original post (73 %). Less than 4 % of the shares reached some sort of virality i.e. spread beyond the first sharer.The longest chain of shares expanded for 5 steps. ( In Facebook, users are in average 4 steps from one another). We also checked if a friend relationship is present in the shares. Of the public shares, only 14 % of sharing happened between friends. But in the second degree shares, friend relationship was present in 43 % of the shares. The long sharing chains are actually quite rare (pdf) and awareness doesn’t require sharing: for every share there are tens or hundreds of share views.

The power of main stream media to spread a message is unparalleled: the power of social media comes from the power of amplification and raising awareness.

Twitter: jattipaa

99analytics.com

 

EDIT: added some details on what the lines between the nodes represent.

Visualize your Twitter network

Visualizing Twitter networks can make sense in the constant stream of tweets. We can detect different communities or active Tweeters. I have made a quick hands on guide for twitter hashtag visualization using Gephi and NodeXL:

 

twitter: jattipaa

www.verkostoanatomia.fi

facebook: http://www.facebook.com/pages/Verkostoanatomia/189756439160

Tragedy in Norway: 24h of tweets – a dataset

I found out of the horrible Norway attacks an hour after the bombings in twitter. The shootings and extent of the murders were revealed a while later. Usually I gather the tweets for network visualization with NodeXL‘s script, but this time I offer only the dataset for researchers.

The data is 24 hours of tweets gathered every 5 minutes containing the word “oslo” from 4.35 PM (CET) Friday 22nd July. I cannot guarantee that every tweet is in this dataset. The file format is GraphML (you can use text editor to find the term “Tooltip” where the content of the tweet is).

Dataset

Let me know if you can make use of the data.

twitter: jattipaa

 

SomeTime2011 – twitterin kertomaa

SomeTime 2011 -tapahtuma kokoaa 10.6.-11.6.2011 yhteen suomalaisia sosiaalisesta mediasta kiinnostuneita. Itse en paikalle päässyt ja seuraankin tapahtumaa twitterissä tägillä #sometime2011. Saman tägin pohjalta voimme myös koota tweettejä ja visualisoida niistä verkostoja.

Oma panokseni tälle perjantaille onkin keräillä näitä tweettejä ja julkaista lyhyitä raportteja siitä, kuka mainitsee kenet. Tästä seuraa kuitenkin ongelma, jonka Heisenbergikin on todennut: jos seuraavat kartat ovat suosittuja, alan itse olla keskeinen toimija verkostossa. Miten te hanskaisitte asian?

Kuitenkin, itse asiaan. Kerään siis tweetit neljä kertaa tunnissa, ja pyrin julkaisemaan tunnin-puolentoista välein sekä k.o. ajanvälin tweetit sekä kokonaiskertymätweetit (alin kuva muodostaa kuvan siihen saakka kertyneistä tweeteistä). Pallon koko vastaa saatujen mainintojen lukumäärää ja värin tummuus aktiivisuutta mainita muita.

EDIT: mukana myös #someback, #someengine, #somemeeting, #somestage, #somestudio, #someyard haettuina puolen tunnin välein klo 11.45 alkaen.

EDIT: Verkostoanatomia kiittää ja kuittaa. Alla video kaikista tweeteistä ajalla 8.30-18.00. Videon alapuolella tuntikatsaukset ja kuva kaikista tweeteistä. Kaikkiaan jonkun #-tägeistä mainitsi 272 tweettaajaa, 591 kertaa mainittiin joku muu. Päivän tweetatuin henkilö oli @tuija (33 mainintaa) ja aktiivisin tweettaaja @pauliinamakela, joka mainitsi 13 kertaa jonkun muun (myös minut, kiitos!). Enjoy!

8.30-10.00

10.00-11.00

11.00-12.00

12.00-13.00

13.00-14.00

14.00-15.00

15.00-16.00

16.00-17.00

17.00-18.00

Kokonaistilanne (päivittyy jatkuvasti):

twitter: jattipaa

www.verkostoanatomia.fi

facebook: http://www.facebook.com/pages/Verkostoanatomia/189756439160

Tiedot kerätty NodeXL:n kätevällä skriptillä ja visualisoitu Gephillä.

Eurovision Song Contest 2011 – tweets visualized

Eurovision song contest is a great example of networks: people tend to vote their neighboring country. In twitter one could follow and comment the pan-European event using the hashtags #eurovision, #esc, #esc2011 and in Finland #euroviisut. To a network analyst the @-mentions are the most interesting: who mentions whom. A tweet using one of the #-tags and an @name means a connection between two tweeters. Using NodeXL to retrieve the tweets (script here) from 1 hour before the final to the end of the voting and Gephi to visualize the dynamic structure of the network I came up with the following picture:

The size of the node represents received mentions and the darkness of the node represents activity of mentioning other tweeters (detailed picture here, see if you can find yourself with ctrl-f). Tweeters that didn’t mention anyone else are not shown here. @queen_uk (343 mentions received), @malena_ernman (81 mentions) and @bbceurovision (73) were the most popular tweeters. The most active tweeter was @davis3xm. Overall 64187 tweeters used the hashtag during the 4,5 hour period and 6378 mentioned someone else.

If we focus in the biggest connected network (2094 nodes with 3618 mentions) we get a clearer picture. This time the color represents the “community” of tweeters:

Pdf here.

twitter: jattipaa

www.verkostoanatomia.fi

facebook: http://www.facebook.com/pages/Verkostoanatomia/189756439160

EDIT: Finnish Eurovision twitter network

The Finnish tag #euroviisut attracted 840 tweeterers and had 619 mentions. The most mentioned (represented by the size of the node) were @jyrkikasvi (38 mentions), @johannasl (17 mentions), @euroviisut and @eskoseppanen (both 14 mentions). The most active mentioners (represented by the darkness of the node) were @oolatus and @kestinen (both mentioned 10 other. Here’s the picture and pdf.

The Royal Wedding – tweets visualized

The wedding of Prince William and Miss Catherine Middleton is cause for celebration – especially for the conventional and social media. Using twitter’s hashtag #rw2011 the online audience could express their sentiments about the nuptial celebrations. I’m more interested in the conversations that take place in twitter: who mentions whom – the basis of social network analysis. Using NodeXL to retrieve the tweets (script here)  and Gephi to visualize the dynamic structure of the network I came up with the following video:

In the video a connection between two nodes is formed when someone mentions or retweets another tweeter – a cite. The tweets were retrieved every 5 minutes before (4 hour before) and during the wedding ceremony until the flyover at the Buckingham Palace. The size of the node represents received citations and the brightness of the node represents activity of mentioning other tweeters. Tweeters that didn’t mention anyone else are not shown here. @clarencehouse (2625 mentions received), @eonline (546 mentions) and @britishmonarchy (498) were the most popular tweeters. Overall 34780 tweeters used the hashtag during the 6 hour period and 12002 mentioned someone else. If we filter out those who are not connected to the main network and received no cites, a more clear picture emerges:

We can also notice that traditional news sites (@eonline, @enews, @bbcworld etc.) have engaged a large audience. These central nodes connect the whole network but still remain separated. The structure of the network resembles a scale-free network, where the most popular nodes gain new connections just by being popular (“the rich get richer -scenario”).

Why do this? First of all, twitter’s feed doesn’t really relay the dynamics of the discussions.With this information we could map the most active and influential tweeters before an event occurs. We can map the different communities and audiences that have taken interest in the matter. Below is the picture of all of the tweets during the wedding (pdf here, see if you can ctrl+f youself).

Good luck for the married couple, here is .gexf-file (rather large one, 33megs) of the tweets if you want to do some network analysis on your honeymoon.

twitter: jattipaa

www.verkostoanatomia.fi

facebook: http://www.facebook.com/pages/Verkostoanatomia/189756439160

Vaalivalvojaiset twitterissä visualisoituna

Kerran neljässä vuodessa Suomessa muistetaan, että kansanedustajatkin voivat vaihtua. Perinteiset tv:n vaalinvalvojaiset ovat tuttua kauraa, mutta twitterissä sen kuuluisan “kansan” keskustelua pystyi seuraamaan tagillä #vaalit2011, joihin perinteiset mediatkin tarttuivat. Esim. Yle julkaisi tweettejä teksti-tv:ssä ja Nelonen omilla nettisivuillaan. Miltä siis illan tweetit kokonaisuudessaan näyttivät?

Kuvassa pallon ja nimen koko vastaa mainintojen lukumäärää ja värin tummuus aktiivisuutta mainita joku toinen (tarkempi, zoomailtava ja haettava kuva tästä). Eniten mainintoja saivat @yousifabdullah (131 mainintaa), @eskoseppanen (50)  ja @jykin (49). Aktiivisin mainitsija oli @oolatus. Kaiken kaikkiaan twiittaajia oli 1808  ja joku toinen tweettaaja mainittiin 2809 kertaa. Kuva ei kuitenkaan kerro tweettausten dynamiikkaa. Dynaamisen verkoston analyysiin tarvitaan toiset menetelmät ja tässä visualisointiapuna toimii video.

Yllä olevassa videossa on kuvattu tweetit ja niiden sisältämät maininnat klo 19.30-00.05 välisenä aikana viiden minuutin välein. Kuvassa yhteys tarkoittaa mainintaa (kaikki, missä on @merkki ja nimi) ja video muodostaa kertymän koko illan aikana tapahtuneista tweeteistä. Videossa pallon koko ja nimen kirkkaus vastaa saatujen mainintojen lukumäärää. Videossa vain sellaiset tweettaajat, jotka mainitsivat jonkun ja jotka itse mainittiin (379 tweettaajaa, 1201 mainintaa).

Voimme näin verkostoanalyysillä voi nopeasti löytää keskeiset toimijat monimutkaisista vuorovaikutuskuvioista. Voimme myös näin hahmottaa twitterin viestivirtaa luonnollisemmaksi. Visualisoimalla tweetit tilanne ja toimijat hahmottuvat nopeammin ja samalla voidaan selvittää, ketkä oikein keskustelevat. Uskoisin, että seuraavissa vaaleissa meillä on jo käytössä reaaliaikainen tweettien seuranta ja visualisointi, jonka avulla toimittajat ym. voivat poimia puhetta aiheuttavia teemoja jo lähetyksen aikana.

www.verkostoanatomia.fi

facebook: http://www.facebook.com/pages/Verkostoanatomia/189756439160

twitter: jattipaa

Tiedot kerätty NodeXL:n kätevällä skriptillä ja visualisoitu Gephillä