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Exploring Big Data With Social Media Analytics

905 words - 4 pages

Today, over 5 billion people have mobile phones, 2 billion people are on the Internet, an individual spends more than 3 hours online, and over 400 million tweets are sent per day. 2.5 billion contents are shared on Facebook, 2.7 billion “Likes”; 300 million photos are being uploaded. Facebook is the world´s largest community serving over a billion of users. Social media are storing massive amount of data of terabytes to zettabytes size, never-before-analyzed data that carry crucial information, which cannot be ignored. However, around 90% of the captured data is “Unstructured” meaning it's spontaneously generated and not easily captured and classified. The graph below shows the voluminous increase of unstructured data from 2010 to 2015.

(Ref 1)
Characteristics of Big Data in Social Media:
The characteristics of this unstructured data are high in volume, high velocity, or high variety and complexity. Big data comes from sensors, devices, video/audio, networks, log files, transactional applications, web, and social media - much of it generated in real time and in a very large scale.

Reasons to Explore Big Data with Social Media Analytics
1. Social Media Analytics and Volume. Social Media contribute huge volume of data. There is a high rate of unstructured data streaming and increase of sensor and machine-to-machine data being collected. Correct use of Social Media Analytics could help create significant meaning with the relevant data.
2. Social Media Analytics and Velocity. Data from Social Media is streaming at exceptional speed that must be dealt properly and many organizations face a great challenge with this high velocity data.
3. Social Media Analytics and Variety. Data in Social Media come in all types of formats. Structured numeric data is got from traditional database from different business whereas, unstructured data is different types of format i.e. text documents, email, video, audio, stock ticker data and financial transactions.
4. Social Media Analytics and Variability. Social Media data can be highly unpredictable with periodic peaks. The peak data loads are from latest trends in social media along with unstructured data are even more challenging to manage and explore.
5. Social Media Analytics and Complexity. Data in Social Media comes from numerous sources. It is a great challenge to use different processes like linking, matching, connecting, correlating relationships, hierarchies and multiple data linkages. This is how complex data can be and if not managed properly, could go out of control.( Ref 2)
Data Analytics
In social media every user actively produces data, either by Googling, by sharing tweets, comments, profiles, favorites, likes or follows, or by uploading or downloading blogs, photos, videos or such other content. The amount of information that gets into the Internet is unimaginable large and predicted to double every...

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