I will like to know how Big Data Analytics can be applied in Social Network. What are the parameters to consider in social networks for big data analytics. How can I mine big data in social networks.
This presentation may give you an idea about mining in Big data for social networks.
http://www.slideshare.net/andrybrewok/big-data-analytics-a-social-network-approach
However, the most interesting is security related research in Big data and social Networks.
While I cannot give any insights about mining the Big Data, I can suggest some applications. The main application is making service providers(any kind) understand how likely is to have their business impacted by a great or poor feedback on these social networks. I will provide some examples: Each user of a certain product/service is usually categorized by their ARPU (average revenue per user) which is a monetary unit. Nevertheless, in an interconnected world, this ARPU is no longer valid, or at least should not be for services that intend to grow and here's why: An impact of individuals on social networks can be huge, especially when they are KEY INFLUENCERS- their ARPU may classify them as unimportant but their feedback and influence in a social context can completely change the dynamic of a business. The BIG DATA should find ways to assess the following:
1. Social scoring(based on Leader / follower /bridge/etc. level of influence, type of influence, number of strong connections and social communities)
2.Social ARPU( as sort of churn scoring according to churn and churn potential of user’s social groups based solely on his opinion/feedback).
3.Social graph (per subscriber) - eg. how many influencers/leaders he/she have in their network and how can his networked graph be used to maximize a service/product reach and feedback just by improving this particular user's experience, irrespective of the real ARPU.
It is obvious that these parameters can have different coefficients of importance based on your own judgement(the best is to make small adjustments along the way). I hope this helps
The applications are just many. Some examples are: Google offers customized ads based on search keywords, Facebook offers people based on mutual friends and groups or pages based on previously liked pages. Instagram and Facebook show ads based on locations and etc.
Text mining and multimedia analysis are two parts of big data analytics widely applied in social networks in order to analyze public opinions, to track events and to build recommender systems. Each analysis objective has its own scope and involves certain limitations that one should keep in mind when establishing quality parameters.
I hope the below article to clarify your thoughts.
Article A Survey of Data Mining Techniques for Social Media Analysis
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