Sunday, 24 March 2013

Analysing Social Networks to predict the future !

Online Social networks enable us to express ourselves and reach out in an inexpensive and extremely convenient manner. Twitter recently announced a staggering 200 million active users! Facebook claims to have 1 billion. Such widespread use of online social networks provides researchers with a firehose of well organized data. Now, what does a researcher do with data? No points for guessing. He/she analyses it!

Some research which I will talk about, analyse it to make predictions about various things - Movie Box-Office, Stock Prices, Trending Topics, Election Results, etc.

However before we discuss about it, I would like to point out to you that all of this research assumes that buzz on the Web reflects popularity and buzz in the real world. To what extent this premise is valid, is another interesting research topic, but let's not go into it right now.

Lets back to the topic of predicting the future. [1] describes a procedure to identify trends through semantic social network analysis. 

A "Web Buzz Index" is calculated for "concepts" which are input in the form of phrases. "Concepts" may be names of politicians, brands, or general topics of interest. To do this, they extend the concept of betweenness centrality of actors in social networks to semantic networks of concepts.  Trends are measured by tracking a concept’s relative importance in a relevant information sphere - Web, blog, or online forums. Betweenness centrality of the concept is used as a representative of the relative importance of a concept in the information sphere.

To build the semantic social network in an information sphere, "degree of separation search" is used. Degree of separation search works by building a two mode network map displaying the linking structure of a list of Web sites or blog posts returned in response to a search query, or the links among posters responding to an original post in an online forum.

Degree of separation search can be employed to compare the relative importance of various concepts. The figure below shows the comparison of relative importance of the concepts “gun control”, “abortion”, “gay marriage”, and “Iraq war”. So, The idea is that the importance of an individual concept depends on the linking structure of the temporal network and the betweenness of the other concepts in the network.

Further analysis which [1] presents is the social network of blog posts right after the US presidential elections Nov 4, 2008. The blogs talking about McCain form a far more compact cluster, at the very bottom with a tightly interlinked structure. The democratic blogs, linking to Obama, are much wider spread out, and also exhibit fewer interconnecting links, reflecting the wider political interests of the voters supporting Obama.

In [1], the researchers have applied their complete procedure collected data over 213 days (April, 1st 2008 until October, 30th 2008) on 21 stock titles on Yahoo! Finance. The results (for stock prices of Goldman Sachs) as we can see below, show a promising correlation between the web buzz and real world stock prices.

Thus, it is evident from the results of the existing research, that social networks can be used as an indicator of the future. The analysis of sentiments can reveal the results of people driven events like the success of a movie, elections, market fluctuations, and what not!

Further Reading:

1.     Gloor, Peter A., et al. "Web Science 2.0: Identifying Trends through Semantic Social Network AnalysisComputational Science and Engineering, 2009. CSE'09. International Conference on. Vol.4. IEEE, 2009
2.     Asur, Sitaram, and Bernardo A. Huberman. "Predicting the Future With Social Media" arXiv preprint arXiv:1003.5699 (2010)
3.     Yu, Sheng, and Subhash Kak. "A Survey of Prediction Using Social Media" arXiv preprint arXiv:1203.1647 (2012)
4.     Wasserman, Stanley, and Katherine Faust. Social network analysis: Methods and applications. Vol. 8. Cambridge university press, 1994.
5.     Data And Text Mining Of Financial Markets Using News and Social Media (Dissertation by Zhichao Han)

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