Detecting changes in context using time series analysis of social network
Researchers are getting great benefits of big data as information gathered from social media like Facebook, Twitter and being used to perceive the lot from family planning to predicting postpartum depression. Detecting behavioral changes in social networks represents an exciting new area of this pr...
| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2015
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/49209/ http://irep.iium.edu.my/49209/2/kajal-paper-2015.pdf |
| Summary: | Researchers are getting great benefits of big data as information gathered from social media like Facebook, Twitter and being used to perceive the lot from family planning to predicting postpartum depression. Detecting behavioral changes in social networks represents an exciting new area of this progression that is used to counter organizational behavior and terrorism. Such analysis in social networks in categorized as dynamic data analysis. To process data dynamically time series considers as an essential component. This research proposes a novel technique that uses time series analysis in cyber space based social networks to detect variances or changes in human context overtime. |
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