Issues in Mining Techniques in Social Media

Abstract

Social network has acquired substantial attention in the last few decades. Access to social network sites such as Twitter, Facebook, LinkedIn and Google+ through the internet has become more affordable. Social media are the online platforms that provide a way for people to connect with each other and participate actively in the group conversations. For individuals, it is a mean of communication that helps sharing contents with friends and like-minded people. For businesses, it provides an access to various issues like what people say about their brand, their product and/or service, to know who are the dominating individuals or possible influencers for their new ideas and then use these findings to make better business strategies. The social media also can be exploited for viral marketing to grow the business sprectrum. It is found that there is a growing interest in social networks and people are depending on social networks for information, news and opinion of other users in various fields. The growing trust on social network sites causes people to generate massive data, also called bigdata. It is typically characterised by three computational issues namely; volume, velocity and varosity. These issues in turn make social network data very complex to analyse manually, resulting in the need to use computational means of analysing them. Data mining provides a wide range of techniques for detecting useful knowledge from massive datasets like trends, patterns and rules. In this paper we discuss the different issues with social networks, different approaches, issues, current challenges and trends.