La Trobe

File(s) stored somewhere else

Please note: Linked content is NOT stored on La Trobe and we can't guarantee its availability, quality, security or accept any liability.

A framework for identifying influential people by analyzing social media data

journal contribution
posted on 11.01.2021, 00:12 by Md Sabbir Al Ahsan, Mohammad Shamsul Arefin, A S M Kayes, Mohammad Hammoudeh, Omar Aldabbas
© 2020 by the authors. Licensee MDPI, Basel, Switzerland. In this paper, we introduce a new framework for identifying the most influential people from social sensor networks. Selecting influential people from social networks is a complicated task as it depends on many metrics like the network of friends, followers, reactions, comments, shares, etc. (e.g., friends-of-a-friend, friends-of-a-friend-of-a-friend). Data on social media are increasing day-by-day at an enormous rate. It is also a challenge to store and process these data. Towards this goal, we use Hadoop to store data and Apache Spark for the fast computation of the data. To select influential people, we apply the mechanisms of skyline query and top-k query. To the best of our knowledge, this is the first work to apply the Apache Spark framework to identify influential people on social sensor network, such as online social media. Our proposed mechanism can find influential people very quickly and efficiently on the data pattern of Facebook.

History

Publication Date

08/12/2020

Journal

Applied Sciences

Volume

10

Issue

24

Article Number

8773

Pagination

16p. (p. 1-16)

Publisher

MDPI AG

ISSN

2076-3417

Rights Statement

The Author reserves all moral rights over the deposited text and must be credited if any re-use occurs. Documents deposited in OPAL are the Open Access versions of outputs published elsewhere. Changes resulting from the publishing process may therefore not be reflected in this document. The final published version may be obtained via the publisher’s DOI. Please note that additional copyright and access restrictions may apply to the published version.

Usage metrics

Journal Articles

Categories

Licence

Exports