Identifying Trends in Facebook Usage: A Visual Approach
R. Sethuraman1, Krishna Chaitanya Reddy V2, M. Gautham Veer3, R. Subhashini4
1R. Sethuraman, School of Computing, Sathyabama Institute of Science and Technology, Chennai, (Tamil Nadu), India.
2Krishna Chaitanya Reddy V, School of Computing, Sathyabama Institute of Science and Technology, Chennai, (Tamil Nadu), India.
3M. Gautham Veer, School of Computing, Sathyabama Institute of Science and Technology, Chennai, (Tamil Nadu), India.
4R. Subhashini, School of Computing, Sathyabama Institute of Science and Technology, Chennai, (Tamil Nadu), India.
Manuscript received on 23 March 2019 | Revised Manuscript received on 30 March 2019 | Manuscript published on 30 March 2019 | PP: 823-825 | Volume-7 Issue-6, March 2019 | Retrieval Number: F2439037619/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: A sharp surge in the usage of social media platforms like Facebook, Twitter, Snapchat etc., makes study on social phenomenon worthwhile. This analysis is an effort to understand the usage trends of people belonging to different age groups in Facebook. The main steps involving our analysis are (a) preprocessing the data into appropriate format and shape for easy analysis and (b) visualizing the patterns observed in the dataset. Visual approach is considered so that the representation of complex social data is simplified, also when something is visualized, we tend to identify the patterns just by looking at them. However, this visualization process takes a lot of time, but worth the time spent. Common visualization techniques include Bar graphs, Histograms, Distplots (Distribution Plots). The preprocessing stage which takes place before the visualization phase is a challenge in itself. Once we get the raw data from the source, we then look for the shape of dataset, null values, co-related features etc. For this analysis we used only the cleaning of null values in preprocessing. We normally have a preconception that a certain age group of people will use social media more than that of other age groups. We will explore all such intricacies in this paper. This paper also explores how various features and parameters affect the trends in dataset.
Keywords: Data Preprocessing, Data Visualization, Distplots, Heatmaps.
Scope of the Article: Reflection and Metadata Approaches