Social media analytics: analysis and visualisation of news diffusion using NodeXL

Author:

Ahmed WasimORCID,Lugovic Sergej

Abstract

Purpose The purpose of this paper is to provide an overview of NodeXL in the context of news diffusion. Journalists often include a social media dimension in their stories but lack the tools to get digital photos of the virtual crowds about which they write. NodeXL is an easy to use tool for collecting, analysing, visualising and reporting on the patterns found in collections of connections in streams of social media. With a network map patterns emerge that highlight key people, groups, divisions and bridges, themes and related resources. Design/methodology/approach This study conducts a literature review of previous empirical work which has utilised NodeXL and highlights the potential of NodeXL to provide network insights of virtual crowds during emerging news events. It then develops a number of guidelines which can be utilised by news media teams to measure and map information diffusion during emerging news events. Findings One emergent software application known as NodeXL has allowed journalists to take “group photos” of the connections among a group of users on social media. It was found that a diverse range of disciplines utilise NodeXL in academic research. Furthermore, based on the features of NodeXL, a number of guidelines were developed which provide insight into how to measure and map emerging news events on Twitter. Social implications With a set of social media network images a journalist can cover a set of social media content streams and quickly grasp “situational awareness” of the shape of the crowd. Since social media popular support is often cited but not documented, NodeXL social media network maps can help journalists quickly document the social landscape utilising an innovative approach. Originality/value This is the first empirical study to review literature on NodeXL, and to provide insight into the value of network visualisations and analytics for the news media domain. Moreover, it is the first empirical study to develop guidelines that will act as a valuable resource for newsrooms looking to acquire insight into emerging news events from the stream of social media posts. In the era of fake news and automated accounts, i.e., bots the ability to highlight opinion leaders and ascertain their allegiances will be of importance in today’s news climate.

Publisher

Emerald

Subject

Library and Information Sciences,Computer Science Applications,Information Systems

Reference56 articles.

1. About Twitter (n.d.), “Twitter Q1 2017 Company Metrics”, available at: https://about.twitter.com/company (accessed 19 June 2017).

2. Using Twitter as a data source: an overview of current social media research tools,2015

3. Using Twitter as a data source: an overview of social media research tools (updated for 2017),2017

4. Public health implications of# ShoutYourAbortion;Public Health,2018

5. Ahmed, W. and Downing, J. (2017), “Campaign leaks and the far-right: who influenced #Macronleaks on Twitter?”, LSE European Politics and Policy EUROPP Blog, available at: http://blogs.lse.ac.uk/europpblog/ (accessed 16 June 2018).

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