State transition in communication under social network: An analysis using fuzzy logic and Density Based Clustering towards big data paradigm

Social networks like Facebook, Twitter, LinkedIn, are the social clouds that provide a platform for a diversified range of conversations on any theme at a stipulated time. The flow rate of a conversation can be measured from the perspective of different amalgamations formed by the theme and the level of the participation. Multiple users share the cloud resources and reallocation of the resources is also possible within the social network as and when required. The communication and conversation can be influential extensively due to the presence of big data orientation. It has been observed that depending on certain random parameters of big data driven social network few blocks of conversation may become explicit and influence on the arena of social network. The element of randomness and diversity creates ambiguity in the content of conversation. The conversation may become appealing or may lose the sympathy of the participants. This paper proposes a novel algorithm deploying Fuzzy methodology to investigate the embedded uncertainty and ambiguity involved within the conversation blocks. Fuzzy logic is capable of taking care of non-crisp states of conversation that might be the source of branding and putting a particular content to the maximum elevation across the social media and services. The experimental results and the graphs give the justification towards the effect of participants act on the topic discussion session, especially the periphery of big data framework. The pre-condition and the post-condition of the topic have been observed after implementing the proposed algorithm and finally further scope of the research has been discussed.