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2020 | OriginalPaper | Chapter

Dynamic Hashtag Interactions and Recommendations: An Implementation Using Apache Spark Streaming and GraphX

Author : Sonam Sharma

Published in: Data Management, Analytics and Innovation

Publisher: Springer Singapore

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Abstract

Hashtag, started with Twitter is a keyword with prefix “#” and now being used mostly for all communication on social media. It has been identified as very powerful and effective in organizing communications according to the topic and trend. Hashtag can further help on various analysis, as it links users with their topic of interests. Hashtag aids in building communities of similar interests. With hashtags, we can follow current trend and interest on twitter which can help us in analyzing multiple factors, e.g., sensitivity of the ongoing trend, its spread, people getting affected, its effect on business and so on. Traditionally available approaches help us in analyzing batch data and finding interests and trends on it. Now with the advancements in the field of technology helps us in analyzing a large amount of online data within seconds. In this paper, we will be exploring dynamic hashtag interactions to find correlations among them and propose a methodology which can successfully find relevant hashtags based on the interest in focus. We will propose our methodology of analyzing and exploring tweets in real time with the extent of converting information; we are getting from twitter to knowledge.

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Metadata
Title
Dynamic Hashtag Interactions and Recommendations: An Implementation Using Apache Spark Streaming and GraphX
Author
Sonam Sharma
Copyright Year
2020
Publisher
Springer Singapore
DOI
https://doi.org/10.1007/978-981-32-9949-8_51