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Posts Tagged ‘Social Networks’

q·.:Launcher – Social Network Analysis Part 2

March 19th, 2012 by Martin No Comments

After the initial analysis that included the basic network stats like centrality indices or network density, we took a more detailed look at the social network data. Our first interest was how often our advertisement Tweet was retweeted. We used the Twitter API to get a list of Twitter users that retweeted:

http://api.twitter.com/1/statuses/181034348781383683/retweeted_by.xml?count=100

In this case, the Tweet was retweeted at least 100 times over (Twitter does not provide detailed information on the exact number), which we believe is quite a success for us :-) . We took a first sample of the Twitter users that retweeted the Tweet from meghannz96. Surprisingly, none of the sampled Twitter users could be found in the follower network of meghannz96. We will elaborate on this and check, if we can find a overlay. We also looked for communities and we found six different communities (denoted by different colors) as shown below.

community structure of meghannz96 Twitter ego network

Finally, we checked the impact of the Tweets on our downloads: after the Tweets were published, we noticed an increase of downloads to 35 downloads per day. Previously, we had between 15 and 20 downloads per day. The traffic on our Web page also increased and increased to 903 hits for the product Web page http://www.ikangai.com//apps/qlauncher/ that was included in the advertisement Tweets. This is very interesting, since we had virtually no hits for this particular Web page in the months before and the advertisement Tweets have apparently a considerable impact.

your ikangai science team

q·.:Launcher Marketing Analysis

March 18th, 2012 by Martin No Comments

In an previous experiment, we bought 1000 Twitter followers and analyzed the resulting network. This time, we experimented with marketing on Twitter. During the last few days, we used the services of Twitter users that offer to buy Tweets for advertisement purposes.
After our advertisement Tweets were posted, we decided to take a look at the ego networks of one of our Twitter users that was tweeting for us.
We collected the network data of a Twitter ego network with Node XL. After quickly looking at the data (see below), we can say that the ego network is (not surprisingly) centered around a single ego (meghannz96). Paths in the network are short: basically, we can reach every node in the network with two hops (the average geodesic distance is almost two). The density of the network is rather low with the network having about 1% of all possible links. The distribution of the in and out degree appears to follow a power law distribution which would make the network scale free.

Connected Components: 1
Single-Vertex Connected Components: 0
Maximum Vertices in a Connected Component: 1654
Maximum Edges in a Connected Component: 26307
Maximum Geodesic Distance: 2
Average Geodesic Distance: 1.988184
Graph Density: 0.009621947
Minimum Out-Degree: 0
Maximum Out-Degree: 1483
Average Out-Degree: 15.905
Median Out-Degree: 6.000
Minimum Betweenness Centrality: 0.000
Maximum Betweenness Centrality: 2307580.543
Average Betweenness Centrality: 1635.456
Median Betweenness Centrality: 4.667
Minimum Closeness Centrality: 0.000
Maximum Closeness Centrality: 0.001
Average Closeness Centrality: 0.000
Median Closeness Centrality: 0.000
Minimum Eigenvector Centrality: 0.000
Maximum Eigenvector Centrality: 0.015
Average Eigenvector Centrality: 0.001
Median Eigenvector Centrality: 0.000
Minimum Clustering Coefficient: 0.000
Maximum Clustering Coefficient: 1.000
Average Clustering Coefficient: 0.333
Median Clustering Coefficient: 0.264

The ego network of the twitter user meghannz96 is plotted below.

meghannz96 ego network

For comparison, we selected a random node and plotted the resulting sub-network:

sboysuccess sub network

There is a lot of additional information in our dataset (e.g. number of tweets or the timezone of the twitter account) which we will analyze and post in the next few days.

your ikangai analysis team

Building Bridges for a People’s People Internet

July 21st, 2011 by Martin No Comments

The Times They are a-Changin’. This old Bob Dylan song (also used by Steve Jobs in one of his keynotes in 1984) holds a lot of truth. We can observe rapid changes in technology and in particular in the USE of technology. People are using “the Internet” for things that no one had thought possible a few years ago. For example, people share pictures or videos with their friends all over the planet or just talk with their families over “the Internet”.

Especially with the advent of social network platforms (Facebook, Xing, MySpace or Google+), we observe a seemingly ever-growing use of Internet technologies. In these platforms, people can create links between themselves and their friends – making use of well established Internet technologies – in particular Hyperlinks which connect entities on the Internet.

The use of existing Internet technologies like the aforementioned Hyperlinks for social purposes turns out to be very interesting when viewed from a conceptual perspective. Until the arrival of social network platforms, the predominant use of Hyperlinks was to create relations between artifacts (i.e., Web pages) on the Internet. Indeed, the standard to identify an artifact on the Internet is called URI which stands for Universal RESOURCE Identifier.

Now, in social networks, URIs are used to identify people. We have links between people instead of links between Web pages (artifacts created BY people). One can argue that the link between people is nothing more than a link between their virtual representations, i.e., resources on the Internet that act as avatars on behalf of people. Basically, nothing changes: we link artifacts. But – and I believe this is an very important observation – a lot has changed for the semantics of the link when it connects people on the Internet.

A relation between people is by no means a static structure: former best friends can become enemies, people get married and then divorce, and so on. And the link on the Internet? Well, the link stays the same, because a link on the Internet has no deeper semantics. The reason is that we create a link between resources. The latter do not change the relation with each other – a Web page cannot become an enemy of another Web page. Hypothetically, if a Web page would become the enemy of another Web page, the link between them can be removed and as such this “destroys” their relation.

As a consequence, we need to create “multi dimensional” links that contain additional information about the kind of relation between Internet artifacts. This has been proposed in in the past by various authors, who introduced the use of so called semantic links which provide the ability to store additional meta information about the nature of a link (e.g., is-a-friend or is-a-colleague).

But there is another dimension that is highly dynamic and even more difficult to capture: people implicitly create relations (links) between Internet resources in their heads when they surf the Internet. In other words: they create memetic connections between pages on the Internet. If we start to collect this kind of information, we can begin to create links between Internet pages which originate from a different source: instead of links that were put on an Internet page by the creator, we get links of the people that visit a page. This results in a collection of Web pages which represent our interests. These connections, when evaluated statistically from user data, represent the people’s Internet: ultimately people decide what and how entities on the Internet are connected. This may be difficult to understand for companies, because they have no links to Web pages of competing companies, even if there is a connection between them (e.g. because people know both companies and surf both Web pages).

The combination of semantic information on the nature of links, i.e., if a link represents a relation between people or between Internet resources and the use of user information creates a new – active – layer on existing Web navigation structures which are realized through Hyperlinks. In this layer, we have relations that are built by the peopleusing the Web.

I call them “bridges”.

your ikangai science Team

My Mom knows Everything

March 22nd, 2010 by Martin No Comments

Childhood in the Internet age is difficult. There are a zillions of things to discover on the Web and therefore it’s really easy to waste time online. While this development is nothing new and children have always done things which were generally considered useless by grown-ups (playing with computers, inspecting – i.e. destroying – a model train etc.), modern technologies like cell phones and the Internet itself have opened the door to another dimension. And this dimension is surveillance. These days, kids have their cell phones always on them and worried parents can call at any time to check if “everything is alright”. Which is perfectly fine – every caring parent wants to make sure that their kids are ok. However, if used excessively, this is the first step towards 24/7 parental control.
One might argue that as kids grow older, parents’ calls become fewer and kids become more independent from their parents as they reach adulthood. But, and that’s the point I’d like to make, as kids grow older they spend more time on the Internet; with social platforms like facebook (which recently exceeded google in terms of daily page views) being the most attractive platforms for young people.
A lot has already been written about facebook’s privacy policy and about the data that is being voluntarily made available on these platforms by the users. However, little attention had been paid to the fact that also parents can join such networks. So, what prevents overly caring parents from spying on their kids on facebook? Nothing. And what prevents kids from giving away all information about themselves? Nothing. After all, it has become good practice to be “yourself” in this kind of networks. So today it’s really simple for parents to know what is going on with their children: just become a member of a social network and become a friend of the kids. Which kid can say “no” to a parental friendship invitation? Probably no one.
So, what lesson can be learned from this? A very simple one: before uploading something about one’s personal life (party pictures with lots of half naked girls/boys :-) ), you should ask yourself a simple question: Should my parents know about this? After all, the “should mom know” test is definitively the best privacy filter :-) .

Your ikangai team

[UPDATE] – Yes, the ikangai team members also have facebook accounts. And no, their parents don’t. Maybe they change their mind, after reading this post :-) .[/UPDATE]