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Evolutionary Intelligence

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13.04.2021 | Research Paper

A hybrid bio-inspired computing approach for buzz detection in social media

verfasst von: Rupali Jain, Jai Batra, Arpan Kumar Kar, Himanshu Agrawal, Vinay Anand Tikkiwal

Erschienen in: Evolutionary Intelligence | Ausgabe 1/2022

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Abstract

Social media forums such as Twitter can be used as instruments for understanding the way users behave and engage with other users online. Analysis of data related to material shared by users assists in mining useful information for assessing content for virality. This study proposes a methodology to predict which tweets are likely to become viral and generate a lot of conversations over the Internet, termed as buzz discussions, by considering such discussions as outliers, using bio-inspired algorithms integrated with k-Nearest Neighbors classification. Performances of three bio-inspired optimization algorithms, namely Grey Wolf Optimization, Chicken Swarm Optimization and, Artificial Bee Colony, have also been evaluated based on the efficacy of the proposed hybrid models for mining outliers on a supervised learning data-set containing 11 primary features and 140,707 instances. Among the three algorithms used for this outlier detection problem, Chicken Swarm Optimization shows better performance, overall, in terms of evaluation parameters, including accuracy, precision, recall, specificity, F1-measure and convergence.

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Titel: A hybrid bio-inspired computing approach for buzz detection in social media
verfasst von: Rupali Jain
Jai Batra
Arpan Kumar Kar
Himanshu Agrawal
Vinay Anand Tikkiwal
Publikationsdatum: 13.04.2021
Verlag: Springer Berlin Heidelberg
Erschienen in: Evolutionary Intelligence / Ausgabe 1/2022
Print ISSN: 1864-5909
Elektronische ISSN: 1864-5917
DOI: https://doi.org/10.1007/s12065-020-00512-7

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