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A big data approach to examining social bots on Twitter

A big data approach to examining social bots on Twitter Social bots are prevalent on social media. Malicious bots can severely distort the true voices of customers. This paper aims to examine social bots in the context of big data of user-generated content. In particular, the author investigates the scope of information distortion for 24 brands across seven industries. Furthermore, the author studies the mechanisms that make social bots viral. Last, approaches to detecting and preventing malicious bots are recommended.Design/methodology/approachA Twitter data set of 29 million tweets was collected. Latent Dirichlet allocation and word cloud were used to visualize unstructured big data of textual content. Sentiment analysis was used to automatically classify 29 million tweets. A fixed-effects model was run on the final panel data.FindingsThe findings demonstrate that social bots significantly distort brand-related information across all industries and among all brands under study. Moreover, Twitter social bots are significantly more effective at spreading word of mouth. In addition, social bots use volumes and emotions as major effective mechanisms to influence and manipulate the spread of information about brands. Finally, the bot detection approaches are effective at identifying bots.Research limitations/implicationsAs brand companies use social networks to monitor brand reputation and engage customers, it is critical for them to distinguish true consumer opinions from fake ones which are artificially created by social bots.Originality/valueThis is the first big data examination of social bots in the context of brand-related user-generated content. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Services Marketing Emerald Publishing

A big data approach to examining social bots on Twitter

Liu, Xia
Journal of Services Marketing , Volume 33 (4): 11 – Sep 18, 2019
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References (66)

Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
0887-6045
eISSN
0887-6045
DOI
10.1108/jsm-02-2018-0049
Publisher site
See Article on Publisher Site

Abstract

Social bots are prevalent on social media. Malicious bots can severely distort the true voices of customers. This paper aims to examine social bots in the context of big data of user-generated content. In particular, the author investigates the scope of information distortion for 24 brands across seven industries. Furthermore, the author studies the mechanisms that make social bots viral. Last, approaches to detecting and preventing malicious bots are recommended.Design/methodology/approachA Twitter data set of 29 million tweets was collected. Latent Dirichlet allocation and word cloud were used to visualize unstructured big data of textual content. Sentiment analysis was used to automatically classify 29 million tweets. A fixed-effects model was run on the final panel data.FindingsThe findings demonstrate that social bots significantly distort brand-related information across all industries and among all brands under study. Moreover, Twitter social bots are significantly more effective at spreading word of mouth. In addition, social bots use volumes and emotions as major effective mechanisms to influence and manipulate the spread of information about brands. Finally, the bot detection approaches are effective at identifying bots.Research limitations/implicationsAs brand companies use social networks to monitor brand reputation and engage customers, it is critical for them to distinguish true consumer opinions from fake ones which are artificially created by social bots.Originality/valueThis is the first big data examination of social bots in the context of brand-related user-generated content.

Journal

Journal of Services Marketing – Emerald Publishing

Published: Sep 18, 2019

Keywords: Big data; User-generated content; Sentiment analysis; Consumer sentiments; Social bots

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