[italian version]
We have just gathered a brand new datased of FriendFeed data (you can download it in the Data section, it’s named 2010-a dataset ). Since it is considerably larger than our previous database we decided to test few more hypotesis on information propagation in SNSs. One of the key concepts speaking about the ability to spread online information is that being well connected is a key element in propagation strategies. This point can be roughly summarised as: the more followers you have the more you can inform. We’ve already challenged this assumption before and we wanted to test it deeper.
Therefore we analysed the relationship between the actual number of followers and the average audience of every users. We defined the average audience value as the average number of users been exposed to the messages sent by a specific user during our sample time. Due to the technical structure of Friendfeed users that were able to start the most engaging discussions have a larger opportunity of have an actual audience larger that the simple list of their followers.
As it is shown in the graph – that shows only the top 20 users according to their followers number – there could be a huge difference between the followers and the actual audience that users can engage. It is very interesting to point out how the users with a larger average audience is ranked only 18th according to the followers number.
As we said before, when we’re dealing with social phenomena and users engagement (as it happens in online propagations): followers are not enough.[English version]
Partendo dall’ultimo dataset che abbiamo acquisito con i dati di FriendFeed abbiamo iniziato a testare alcune ipotesi relative alla possibilità di definire la capacità comunicativa degli utenti all’interno di questo tipo di reti. Una delle assunzioni che si sono fatte più spesso (più in passato di quanto non avvenga ora) riguarda il nuero di followers. In pratica si considera spesso questo valore come un indicatore della capacità comunicativa di un utente. Brutalmente si pensa che se una persona è in contatto con molte altre persone questi abbia la capacità di raggiungere una massa importante di utenti.
Per verificare questo assunto abbiamo deciso di osservare la relazione tra il numero di follwers e la audience media degli utenti. Con audience media intendiamo il numero di utenti che sono stati esposti ai messaggi postati da uno specifico utente durante il nostro periodo campione (2 Mesi: Agosto- Settembre 2010).
Data la natura di FriendFeed l’audience tenderà a crescere verso valori più ampi rispetto ai follower diretti tanto più l’utente sarà in grado di far partire discussioni che riescono a propagarsi ed a coinvolgere gli amici degli amici e così via.
Come si può vedere dall’immagine [che mostra il rapporto tra followers e audience media per i 20 utenti con il maggior numero di followers all’interno della rete di FriendFeed italiana (solo account pubblici)] un elevato numero di followers non significa necesariamente un’elevata audience media, anzi l’utente che – in termini assoluti – raggiunge mediamente un’audience maggiore si colloca solo diciottesimo quando andiamo a contare i followers.
Insomma ancora una volta quando parliamo di reti sociali i numeri possono ingannare facilmente.
Tag: SNA
To whom are you speaking? Egonetwork over time
Last version of Gephi introduced some very nice feature. It is now possible to work with dynamic networks that can easily be observed in their evolution. Working with dynamic networks is crucial when you are dealing with social networks, like those existing in microblogging sites, that show a high level of variability: social connections quickly change over time and – even if the connection does not disappear – the use of a specific connection can be very different from time to time. Observing such a phenomenon could be difficult with a static SNA but with a dynamic perspective it becomes quite simple.
The movie shows how the egonetwork of my Friendfeed user changed within the period Aug. – Sept. 2010. The ego-node represents my user and the other nodes are all the users I’ve interacted with (on FriendFeed). Nodes with a higher level of interaction are visualised closer to the ego-node while users with a low level of interaction are pushed away from the ego-node.
The video span over two month of time with the data-resolution set at 10 days (this creates 5 different configuration of the network) and it clearly shows how closer nodes change even in such a short amount of time.
Even if this was intended to be just a demo of then new opportunities offered by gephi it provides some insights about how ego-networks evolve over time. This evolution can be due to endogenous or exogenous aspects but it seems to be quicker than what one could expect.L’ultima versione di Gephi permette finalmente la visualizzazione di reti dinamiche. Quella che trovate qui è una breve visualizzazione di come l’ego-network attiva di uno specifico utente (in questo caso il mio utente FriendFeed) è cambiata nel corso dei mesi di Agosto-Settembre 2010.
I nodi sono posizionati – rispetto al nodo centrale – in modo da rappresentare il livello di interazione: i nodi più prossimi sono quelli con un livello di interazione maggiore. Com’è possibile vedere i nodi più prossimi – ovvero i nodi con i quali in quel periodo ho interagito maggiormente – cambiano con un’elevata frequenza costringendo la rete a riadattarsi di conseguenza.
Oltre alla dimostrazione di alcune delle possibilità offerte dalla nuova versione di gephi questa breve visualizzazione ci permette di capire come i contatti con i quali interagiamo – pur all’interno di un numero sicuramente interiore rispetto all’insieme delle connessioni possibili – sono una realtà dinamica in costante evoluzione. Le ragioni di questa evoluzione possono essere le più diverse, da fattori endogeni alla rete (discussioni interessanti) a fattori esogeni (eventi esterni che desideriamo trattare con alcuni contatti).
Mapping FriendFeed Network: switching the perspective
Recently we’ve posted a visualisation of the Italian Network of FriendFeed. Such a map was an interesting and general perspective showing how a complex network can be visualised. Obviously when we’re dealing with social networks or microblogging sites we’re dealing with a complex network emerging from many different egonetworks. How is the perspective over the same Network (Italian Friendfeed Users) if we observe it from the inside?
Starting from the same dataset we’ve used for the previous visualisation we generated an Egonetwork using as central user one of the minor nodes of the global visualisation. We chose the user lucamondini who, at that time, had a rather small network: 150 following and 200 followers. The idea was to see how the network looked like when the observer was one of the peripheral nodes.
An explanation of nodes sizes and colours can be found here, what’s interesting is that switching the perspective to this user’s point of view the overall scenario change. Even if it is always possible to find major and minor nodes they don’t seem to be necessarily the same nodes of the global map. Of course users that are very popular within the whole network seem to be quite popular also within local egonetwork but their specific size is different. Moving our observation to nodes that are even more peripheral (we chose the user magicabula, 21 followers and 33 following) – since it has so few followers this user wasn’t included into the global visualisation – will show a small network of highly connected users where some of the users are heavily connected even in the global map and some have a large authority only within this local perspective.
When we’re dealing with social network and information propagation we must keep in mind that the scenario might be really different when it is observed from the far periphery of the network.
Visualising Italian Friendfeed Network
Recently we’ve posted on Friendfeed a visualisation of Italian Friendfeed users extracted from the data we’ve collected in 2009. Since the map started an interesting debate (you can read it here – in Italian –) we thought to write a short post explaining how the map has been done and what are its limits and possibilities.
The map is based on a network made of 8024 nodes with 244542 edges (even if the map shows only the nodes with more than 147 followers but statistical values have been counted on the whole network).
We collected the data in September 2009 starting from all the public messages posted on Frienfeed (you can read more about this in out SBP10 paper).
We processed the network with Gephi and the map shows the indegree value as node size and betweenness centrality value as node colour.
The final result is rather interesting since it shows on one side a group of huge nodes with many followers but at the same time it shows how the is no simple correlation between the number of followers and the betweenness centrality value. Since BC value is often used to identify relevant nodes or hidden hubs this can be read as the quality of your connections matters more than their number.
Nevertheless a final remark has to be done. Metrics like betweenness centrality works really well in traditional networks but they fail to grasp the new conversational nature of Friendfeed Network (but the same could be said about Twitter). In Friendfeed conversations exist often out of the network made by the following/follower structure. When I get in touch with a message originally produced out of my network only because a friend mine comments on it, what happens is the creation of an actual network based more on social behaviour than on the underlying set of connections.
We need new social metrics.
July RoundUp
July has been a month fully packed of SIGSNA activities. At the beginning we’ve been in Oxford for the Research Methods festival [reported here] and after that we had just a few days at home and we had to fly to Gothenburg for the International Sociological Association Conference (ISA 2010). Due to the high interdisciplinary approach of the SIGSNA project we have to move through many different conferences so it might be strange to follow the line of all our presentations, but that’s the best part of it: to be able to share our research and our ideas with so many colleagues from a large variety of disciplines. During the ISA conference we presented at the RC51- Sociocybernetics session. Sociocybernetic is an interesting sociological approach rooted in the System theory and in Complexity theory; nowadays it shows a good theoretical background for a Sociological Approach to the internet studies. What’s really cool is that I won the “Walter Buckley Memorial Award for Excellence in Presenting Sociocybernetics”!!
Here you can check the slides I used during my presentation:
Gothenburg
View more presentations from Luca Rossi.
As soon I made my way back to Bologna I attended the International Visual Sociology Association Conference here in Bologna. Visual Sociology is a rather recent and fascinating field of research and I really wanted to show some visual hints we had from our research. So I presented a brief discussion analysing the top100 most commented pictures posted in the Italian Friendfeed durng our sampling time. Well I’m happy to say that we had a great panel there together with some friends also presenting on UGC/SNS pictures (Agnese, Marina, Alessandra, Stefania, and Fatima – and Many thanks to Giovanni and Laura, chairs of the session).
Here you can see the slides I presented during my talk:
Pictures of Friendfeed
View more presentations from Luca Rossi.
So what’s next? July was really full of stuff and we recently received the news that the SIGSNA research has been authorised to use some of the computing resources of the CINECA supercomputer centre. I can clearly see a huge amount of work just ahead.
Visualizing information spread
We are currently working on information spreading in FriendFeed context. One of the best things about SIGSNA data is that by being a comprehensive set of data of two weeks is possible to track how specific information – identifiable by a set of keywords – spreads through the network of users. This is what we are doing tracking down the path of the news about the death of the Italian TV host Mike Bongiorno (died in Sept. 2009). The picture that you can see above shows all the Exposed users (users that directly commented AND users that saw the news but decided not to comment).
Isn’t it beautiful? We can’t say more at the moment, but in few days we are submitting a paper about that and hopefully you’ll be able to read it soon in our Data & Papers Section.