COIN Course 2009 00 (2009) 000–000

Emerging social networks of unsigned country music artists on MySpace: using demographic and network related information of an artist friend network Jonas Kuhlemanna, Matthias Rottländerb, Ville Heikkiläc a

[email protected] [email protected] c [email protected]

b

Abstract Click here and insert your abstract text.

Keywords: Type your keywords here, separated by semicolons ;

Author name / 00 (2010) 000–000

1. Introduction -

How social Media changes the emerging of new artists o …

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The importance of social networks and the Location: An unsigned less popular artist is mainly first known at one location, e.g. her or his hometown. Without any promotion or advertising her or his growth is strong correlated to the word of mouth effect at the beginning. The social network around the artists is an important factor for successful growth in the early stage. Early adopter spreading the word of mouth more powerful than the artist itself, in a phase where no label or investor normally recognize the artist. Usually it may be expect that the word of mouth effect is much stronger in a network, where actors are more homogeneous. If a friend of an actor is fan of an emerging artist it is more likely that she or he becomes a fan too, than a complete random actor around the country. However, the location based growth in one region will reach its maximum, when the homogeneous network stops growing and structural holes to other actors appear. At this point promotion and advertising are important for further growth. Successful advertising can provides the network with more random ties outside the homogeneous clique and helps the network keep growing. This location based growth effect is what may be expected in an offline world, where transaction costs are high, and random ties are less likely. However, in the ages of the Internet and social Media the transaction costs are only a fraction of past values. -

Research Hypothesis and Agenda: So, the question is: Is the adopting process in internet driven social networks the same, as in the offline world, or is it different? Is it possible to measure an emergent social network by analyzing demographic attributes and network related information of an artist friend network? However, in this paper we discuss these questions, by using a data driven approach using friend network information of unsigned country music artists on MySpace. We describe a location based approach after that, by measuring the divergence of the actors region within an artist friend network. Then we are measure the network around several artists by looking at the network structure. 2. Methodology 2.1. The data approach As the decision was to analyze web based social Networks we decided to take MySpace for several reasons: First, we needed a social network, which was big enough and where user data was accessible from public. On MySpace, user data and network information is more likely to be open to the public than, e.g. Facebook. Second, MySpace is highly focusing on music related topics and provides us with charts and rankings for even unsigned artists. Also MySpace has over … unique user (missing source). We decided to drill down our observation to a specific music genre to avoid influences such as different trends in popularity and audience of a genre, to assure that every artist can be measured by the same attributes. The country music genre was chosen, because we were expecting an more homogeneous audience than e.g. from artists of the pop music genre. While MySpace has no official data-api support, we were forced to write a crawler, which supports to parse the DOM structure of a user profile page, the friend list page and the quick info (which is popping up for each user on the friend list and is used by MySpace to support an internal Ajax function). Because the HTML code of a typical user profile is about 100kb in average, we decided in terms of speed to focus on parsing the quick info page. The key technologies were PHP as a scripting language including the web query library curl and MySQL for data storage. Friendships between users were seen as links between actors and were accessible through the friend list page of each user resp. artist. We started our observation on December 18th 2009, where we collected the first degree friend data (117.000 unique users) of the top 100 American unsigned country music artists from the MySpace chart list. For each user the

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following demographic attributes were collected: friendID, username, region, city, age, gender, number of friends, whether the user is a music band or not Attribute

Description

friendID Username Region City Age Gender Number_of_friends Table 1: User Attributes To detecting network growth, we updated the network data for each artist on January 3rd 2010 and on 11. January 2010 and complemented the user data of new friendships (2000 and 2000) were made in this period. We also collected the second degree for chosen artists later on January 14th to get information about the network structure around an artist. 2.1.1. Filtering and cleaning -

Why is it necessary to filter Filter rules How this will affect the results

2.2. Divergence measurement using location based attributes -

∑ P(i)*log P(i)/Q(i) P(i) = number of friends of an artist in region i/ total friends of an artist Q(i) = total friends from region i/ total friends

2.3. Small world theory -

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3. Results 3.1. Demographic analysis of emerging artists 3.1.1. Location -

Divergence Analysis results Is Divergence of the location correlating with growth ?

3.1.2. None location attributes -

gender, age, Number of friends average, median, distribution

3.2. Network analysis -

For a selection of Artists: look at links between fans of the same artist

3.2.1. Interconnectedness and growth -

Are there any correlations with growth ? (finding cliques)

3.2.2. Homophily -

Are actors more likely to have friends from the same location, gender, age, Number of friends ?

4. Conclusion 4.1. Lessons learned -

Recap interesting results

4.2. Limitation of the research -

Observation period to small rough filtering of data

4.3. Future research

5. Literature

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