Mapreduce Based Framework for Social Media Post Visualization
DOI:
https://doi.org/10.37591/joadms.v2i2.432Keywords:
Mapreduce, social media, media data and timelines, hot topicsAbstract
Handling huge amount of data scalable is a matter of concern for a long time. We all know that social media generates huge amounts of data the explosive growth of social media is one of the reasons that 90% of all the data in the world has been generated in the last two years alone. When we use services like Facebook and Twitter, we agree to let those to store our photos, public observations, and private communications. In exchange, they conveniently structure that information and make it easy for us to access. Reviewing our social media timelines, we can see the stories of our own lives told right in front of us. Visualization of our social media data takes the storytelling to another level, and gives us insights into our own lives that we might never achieve on our own. Social graph visualizations help us make sense of the social dynamics that are playing around us. Therefore, we must be sure about only important things, post or data we can visualizes firstly. I can start to ask, how much overlap is there between my network of friends from university and my professional network? How many of my colleagues are linked with Facebook have Connection to people I’ve met at my previous jobs? And who are the important social connectors in my life who bridge the gaps between all these different groups? At a glance, I can see what ideas matter to the people I care about. Big data, social media and visualization are sure to remain hot topics for the foreseeable future.
Cite this Article
Wakle Dhananjay, Pandit Swapnil, Suryawanshi Sushil et al. Mapreduce Based Framework for Social Media Post Visualization. Journal of Advanced Database Management & Systems. 2015; 2(2): 49–52p.
References
T. Toshimitsu, T. Ryota, and Y. Kenji. Discovering Emerging Topics in Social Streams via Link-Anomaly Detection.
Sugitani et al. Detecting Local Events by Analyzing Spatiotemporal Locality of Tweets.
Jiang et al. Recommendation with Social Contextual Information Fellow, IEEE.
Fu et al. Learning Multimodel Latent Attributes by Student Member, IEEE.
D. Jeffrey, G. Sanjay. MapReduce: Simplified Data Processing on Large Clusters.
Sugitani et al. Detecting Local Events by Analyzing Spatiotemporal Locality of Tweets.
Downloads
Published
Issue
Section
License
Declaration and Copyright Transfer Form
(to be completed by authors)
I/ We, the undersigned author(s) of the submitted manuscript, hereby declare, that the above manuscript which is submitted for publication in the STM Journals(s), is not published already in part or whole (except in the form of abstract) in any journal or magazine for private or public circulation, and, is not under consideration of publication elsewhere.
- I/We will not withdraw the manuscript after 1 week of submission as I have read the Author Guidelines and will adhere to the guidelines.
- I/We Author(s ) have niether given nor will give this manuscript elsewhere for publishing after submitting in STM Journal(s).
- I/ We have read the original version of the manuscript and am/ are responsible for the thought contents embodied in it. The work dealt in the manuscript is my/ our own, and my/ our individual contribution to this work is significant enough to qualify for authorship.
- I/We also agree to the authorship of the article in the following order:
Author’s name
1. ________________
2. ________________
3. ________________
4. ________________
| We Author(s) tick this box and would request you to consider it as our signature as we agree to the terms of this Copyright Notice, which will apply to this submission if and when it is published by this journal. |