A Survey of Big Data in Social Networking and Media Interaction

A Survey of Big Data in Social Networking and Media Interaction

D. Sridhar

Assistant Professor Dept. of Computer Science,

Dr. SNS Rajalakshmi College of Arts and Science, Coimbatore, India

V. Ganapriya

Assistant Professor Dept. of Computer Science,

Dr. SNS Rajalakshmi College of Arts and Science, Coimbatore, India

M. Srivanitha

Assistant Professor Dept. of Computer Science,

Dr. SNS Rajalakshmi College of Arts and Science, Coimbatore, India

Abstract Data is orgin of the any system. Data continue their scaling at various types. Achieving an automated system in digital media world deals with massive amounts of data that collect over the time in www. It was updated every second around the world using social networks. Social networks provide the opportunity to connect as personnel, professional and entertained. This survey is aims to demonstrate the massive amount of data utilization in social networking and media recommendation.

There are varieties of social networking websites are available in www. Main objective of this survey is to how people are connected with social networks and how TV media are utilizing the social networks to rate the programs and channel.

Keywords: big data, big data analytics, social networks analytics, social networks with TV media, sentimental analyze

1. INTRODUCTION

   Big data is the term for a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications. The challenges include capture, curation, storage, search, sharing, transfer, analysis, and visualization [1]. It can updated every second with tera bytes of data with various information source like social media networks, scientific instruments, mobile devices, sensor technology and networks.

   For example considered a Market analyzers are keeping on viewing the transaction and interactions of the online users to make new trends in business.

2. BIG DATA ANALYTICS

   Big data analytics is the process of examining large amounts of data of a variety of types (big data) to uncover hidden patterns, unknown correlations and other useful information. Such information can provide competitive advantages over rival organizations and result in business benefits, such as more effective marketing and increased revenue. Big data

   analytics can be done with the software tools commonly used as part of advanced analytics disciplines such as predictive analytics and data mining. But the unstructured data sources used for big data analytics may not fit in traditional data warehouses.

   The technologies associated with big data analytics include NoSQL databases, Hadoop and MapReduce. NoSQL database, also called Not Only SQL. A NoSQL database provides a mechanism for storage and retrieval of data that is modeled in means other than the tabular relations used in relational databases.

   MapReduce is a software framework that allows developers to write programs that process massive amounts of unstructured data in parallel across a distributed cluster of processors or stand-alone computers. It was developed at Google for indexing Web pages. Hadoop is a free, Java-based programming framework that supports the processing of large data sets in a distributed computing environment. It is part of the Apache project sponsored by the Apache Software Foundation. Hadoop makes it possible to run applications on systems with thousands of nodes involving thousands of terabytes. Its distributed file system facilitates rapid data transfer rates among nodes and allows the system to continue operating uninterrupted in case of a node failure. This approach lowers the risk of catastrophic system failure, even if a significant number of nodes become inoperative.

3. SOCIAL NETWORKS

   Everything is connected: people, information, events and places, all the more so with the advent of online social media. Social network refers to interaction among people in which exchange and / or create information in virtual online communities and networks. This became a staple part of all business it is important to know unique benefits of each individual. Recently people are engaged in social network to share their ideas, emotions and views with their friends, relative and also with different type of people.

   Many great personalities and renowned people have their updates online which is read by their followers. Comments

   and likes given to those will earn more followers but in the same way, if any negatives are shared about the person it leads to hatred towards the personality.

   Social network otherwise we can say a user friend. It act as discuss board to find whether their thought, ideas or whatever that might is right or wrong at the same time there is a chance to convey messages, get knowledge awareness, finding new things by images, content, videos with sound. It is a not one to one communication it is a many to many communication that can be discuss or chatted with lot member of users who joined in this link There are various social networking sites are available such as Facebook, twitter, Linkedin.

4. SOCIAL NETWORKS WITH TV MEDIA

   Even though TV Medias are connected in social network by creating the separate blog for their channel interact with people. It means of interface to communicate with different variety of people. Social networking is connects the people together online. Ads and massive TV shows are consecutively watched on YouTube and even commented.

   It helps the people by way of getting comments, ideas and making their programs popular. By the recommendations of the viewers, rate the programs if it is appreciated it rates their channel higher otherwise have to rectify the problem of programs. It provides the opportunity to know the competitor channel program rating. It act as discussion boards to know the public or any person opinions easy based on likes and dislikes.

   opinions about political candidates before making a voting decision in a political election. In the past, when an individual needed opinions, he/she asked friends and family. When an organization or a business needed public or consumer opinions, it conducted surveys, opinion polls, and focus groups. Acquiring public and consumer opinions has long been a huge business itself for marketing, public relations, and political campaign companies. With the explosive growth of social media (e.g., reviews, forum discussions, blogs, micro-blogs, Twitter, comments, and postings in social network sites) on the Web, individuals and organizations are increasingly using the content in these media for decision making [2].

   7. SOCIAL NETWORKS ANALYTICS

   The enterprise, with a much smaller number of internal social network users than the public social networks like Facebook, is still generating big data, and enterprises should therefore consider what to collect and how to use the collected data to improve their internal operations. Analyzing internal social networks can provide business intelligence.

   Google Analytics is a great tool to monitor the performance of your blog, but you may not look at it regularly enough. One way to get around this is to set up a weekly report for delivery to you via email.

5. SOCIAL NETWORKS WITH MARKETING

   For marketers this is an opportunity to better understand, target, approach clients, prospects, suspects, to sell them more, for better lead communities, to innovate, differentiate themselves from the competition and develop a competitive advantage. Tweets and products show th popularity of the brand thereby attracting consumers for the same and enhancing sales promotion to manufactures.

   By analyzing the social connections, the enterprise will probably discover that the existing organization structure is partially bypassed so that the individuals can work with others that are not part of their group or department. The enterprise may be able use the connection information to reorganize their internal organization structure to reflect the social network connections and thereby improve enterprise productivity.

   If you share your blog post in email or on Twitter, Facebook and other social channels to Check the Availability of Your Business Name on Social Networks. We can track the content and analyze the blog in variety of channels (Email, Facebook, GooglePlus, Linkedin, and Twitter) for viewing the information and monitor user visitors activity. It also provides the option to find the competitors ranks.

6. SENTIMENTAL ANALYZE OF SOCIAL NETWORKS

Opinions are central to almost all human activities because they are key influencers of our behaviors. Whenever we need to make a decision, we want to know others opinions. In the real world, businesses and organizations always want to find consumer or public opinions about their products and services. Individual consumers also want to know the opinions of existing users of a product before purchasing it, and others

Statistics by socialbaners.com

8. CONCLUSION

Thus the survey paper depicts about a survey of big data in social networking and media interaction with variety of aspects. It can update massively in the form of big data at every second. Finally coming to the conclusion that social networks equally having both merits as well as demerits.

REFERENCES

1. http://en.wikipedia.org

2. Bing Liu, Sentiment Analysis and Opinion Mining" April 22, 2012

3. V. Bharanipriya , V.Kamakshi Prasad Web Content Mining Tools: acomparative study January-June 2011, Volume4, No. 1, pp. 211-215

4. Bamshad Mobasher, Robert Cooley, and Jaideep Srivastava. Creating adaptive web sites through usage-based clustering of urls. In Knowledge and Data Engineering Workshop, 1999.

5. Bernardo Huberman, Peter Pirolli, James Pitkow, andRajan Kukose. Strong regularities in world wide web surfing. Technical report, Xerox PARC, 1998.

6. C. Nuzman, I. Saniee, W. Sweldens, and A. Weiss, A compound model for TCP connection arrivals, with applications to LAN and WAN, Computer Networks, Special Issue on Long-Range Dependent Traffic, vol. 40, no. 3, pp. 319337, Oct. 2002.

7. D. J. Hand, H. Mannila, and P. Smyth, Principles of Data Mining. Cambridge, MA: MIT Press, 2001.