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Aussie Trump-Tweeters more positive than negative

A fascinating Twitter analysis conducted at Victoria University indicates slightly more Australians are positive than negative about Donald Trump’s election as the new American president.

The revelation is a result of an analysis of more than 32,000 tweets posted the day of Trump’s election on 10 November 2016 by VU Information Systems academics, Associate Professor Paul Hawking and Scott Bingley.

They found that 48.6% of Australian Twitter users posting about the election displayed positive sentiments, using words such as ‘sincerely’, ‘thanks’, ‘remarkable’, ‘triumph’ and ‘inspiring.’ This compared to the 44.6% who used negative words in their tweets like ‘scares’, ‘homophobe’, ‘hate’, and ‘arrogance.’

Interestingly, Twitter users in Sydney, Brisbane, Canberra and Hobart were more positive about Trump than those in Darwin, Adelaide, Melbourne and Perth.

The study was made possible due to recent advances in sophisticated software tools, with VU’s Business Analytics and Big Data Lab working in partnership with SAP, a system used worldwide to manage business operations.

Associate Professor Hawking acknowledged that the analytics system – while very advanced – had difficulty identifying sarcasm and humour.

“Business analytic tools traditionally focus on structured data in databases and spreadsheets, but much social media contain largely unstructured data,” he said.    

He said the study is an example of how business analysis tools can be applied to social media to gain insights about how a specific population views a particular event, as well as highlight their potential limitations.

A more comprehensive article about the VU Twitter analysis of Trump’s election was published today on The Conversation.  


Associate Professor Paul Hawking is one of Australia’s best-selling IT authors, and was the first academic in the world to be awarded the status of ‘SAP Mentor’ in 2010 for his expertise in SAP business software. He is available for interview on 0419 301 628.


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