Data stream mining is one of the realms gaining upper hand over traditional data mining methods. Transfinite volumes of data termed as Data Streams are often generated by Internet traffic, Communication networks, On-line bank or ATM transactions etc. The streams are dynamic and ever-shifting and need to be analysed online as they are obtained. Social media is one of the notable sources of such data streams. While social media streaming has received a lot of attention over the past decade, the ever-expanding streams of data presents huge challenges for learning and maintaining control. Dealing with billions of user’s data measured in pet bytes is a demanding task in itself. It is indeed a challenge to mine such dynamic data from social networks in an uninterrupted and competent way. This paper is purposed to introduce social data streams and the mining techniques involved in processing them. We analyse the most recent trends in social media data stream mining to translate to the detailed study of the matter. We also review innovative implementations of social media stream mining that are currently prevalent.
Recent Trends in Communication Networks