A Distributed Framework for Early Trending Topics Detection on Big Social Networks Data Threads

TitleA Distributed Framework for Early Trending Topics Detection on Big Social Networks Data Threads
Publication TypeBook Chapter
Year of Publication2016
AuthorsVakali, Athena, Nikolaos Kitmeridis, and Maria Panourgia
EditorAngelov, Plamen, Yannis Manolopoulos, Lazaros Iliadis, Asim Roy, and Marley Vellasco
Book TitleAdvances in Big Data: Proceedings of the 2nd INNS Conference on Big Data, October 23-25, 2016, Thessaloniki, Greece
Pagination186–194
PublisherSpringer International Publishing
CityCham
ISBN Number978-3-319-47898-2
Abstract

Social networks have become big data production engines and their analytics can reveal insightful trending topics, such that hidden knowledge can be utilized in various applications and settings. This paper addresses the problem of popular topics’ and trends’ early prediction out of social networks data streams which demand distributed software architectures. Under an online time series classification model, which is implemented in a flexible and adaptive distributed framework, trending topics are detected. Emphasis is placed on the early detection process and on the performance of the proposed framework. The implemented framework builds on the lambda architecture design and the experimentation carried out highlights the usefulness of the proposed approach in early trends detection with high rates in performance and with a validation aligned with a popular microblogging service.

URLhttp://dx.doi.org/10.1007/978-3-319-47898-2_20
DOI10.1007/978-3-319-47898-2_20
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