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This study concentrates on assessing the research productivity in the domain of social media security in the past decades. The purpose of this study is to conduct a comprehensive bibliometric review on social media using various bibliometric indicators.

A total of 8,121 scientific publications were retrieved from Scopus database from period 1998 to 2021 using associated keyword search. This study also used VOSviewer^© tool to evaluate the network visualisation.

The result of this study revealed that there is a steady growth of annual publications except for the years 2015 and 2016. A total of 8,121 scientific publications and 80,454 total citations were found with 11.2 average citations per publication. The USA, China and India were top productive countries in terms of publishing research in the field; Chinese Academy of Sciences secured top position with 126 publications in highly productive organisation in the domain. The lecture notes in computer science from Springer Nature received a highly produced title with 553 publications and 4,453 total citations. For co-occurrence of author’s keywords, network visualisation analysis revealed that “Social Media”, “Social Network”, “Security”, “Privacy” and “Trust” found maximum occurrence in the domain of social media security.

This study provides comprehensive research status of social media security by performing a bibliometric analysis. The findings of this study will help relevant researchers to understand the research trend, pursue scientific collaborators and enhance research topic preferences.

The rareness of this study, detailed bibliometric analysis on the domain of social media security, is proven using numerous bibliometric indicators and application of VOSviewer in the domain.

Fake news stories have become a central element of crises that corporate public relations practitioners have confronted. Whether such stories are rumors, outright lies or deliberate attempts to discredit corporations, they have the same impact and require specific strategies for public relations practitioners to effectively respond. The purpose of this study is to uncover strategies to manage crises that arise from fake news and if and how these strategies differ for other corporate crises.

In this multi-method study of 21 in-depth interviews and a 8-person focus group with senior-level corporate public relations practitioners, authors explored decision-making strategies for responding to fake news crises. Transcripts of interviews and the focus group were thematically analyzed.

Results reveal insights regarding how public relations practitioners determine if and when to respond to fake news crises in corporations; what response strategies public relations practitioners have the autonomy to employ for fake news crises in corporations, and how public relations practitioners control media narratives during fake news crises in corporations.

The findings guide public relations practitioners to craft an autonomous decision-making process and effective online listening strategies—establishing a watchful waiting approach—and determine if the fake news issue is a passing moment or movement swirling into a crisis.

Few studies have examined the perspectives of crisis communication experts about minimizing and managing fake news crises. The study identifies opportunities for future research focused on crises originating from fake news and disinformation.

This paper seeks to demonstrate the value of using social media to capture fashion trends, including the popularity of specific features of clothing, in order to improve the speed and accuracy of supply chain response in the era of fast fashion.

This study examines the role that text mining can play to improve trend recognition in the fashion industry. Researchers used n-gram analysis to design a social media trend detection tool referred to here as the Twitter Trend Tool (3Ts). This tool was applied to a Twitter dataset to identify trends whose validity was then checked against Google Trends.

The results suggest that Twitter data are trend representative and can be used to identify the apparel features that are most in demand in near real time.

The 3Ts introduced in this research contributes to the field of fashion analytics by offering a novel method for employing big data from social media to identify consumer preferences in fashion elements and analyzes consumer preferences to improve demand planning.

The 3Ts improves forecasting models and helps inform marketing campaigns in the apparel retail industry, especially in fast fashion.