Xiao Huang
For the casual observer, Twitter may be a vehicle for venting frustrations, digging into public figures, or getting into trouble for things you said 10 years before you became famous. But what if all of these tweets across the spectrum of emotions — frustration, anger, fear, hope — could be data-mined to provide a composite of a nation’s overall mental health?
That’s the thinking behind a recent study of nearly 245,000 geotagged COVID-19-related tweets — tweets that talked about the pandemic and embedded geographic information — from across Australia. The aim was to identify changes in mental health as a result of the COVID-19 pandemic and to examine the period from January 2020 to May 2021. The study describes how it “used machine learning and spatial mapping to classify, measure and map changes in the Australian public’s mental health signals and track their change over the different phases of the pandemic in eight Australian capital cities.”
Xiao Huang, an assistant professor at the Department of Geosciences and the Center for Advanced Spatial Technologies, both at U of A, was the study’s second author and corresponding author. Huang explained, “The term ‘social sensing’ has become a hot research direction in many fields due to the proliferation of public-facing platforms. It generally refers to a set of data collection paradigms in which data is collected from people or devices on their behalf. In this sense, the data collection paradigm we designed aims to intelligently retrieve public emotions and sentiments automatically.”
Not surprisingly, the researchers observed a shift from pessimism to heightened optimism in the mid-stage early in the pandemic, but then a further turn to heightened pessimism in the later stage, possibly due to concerns about the launch of the vaccine. While these general feelings could probably be guessed by watching or reading the news, geotagging provides a much more granular picture. Where was anxiety or pessimism most concentrated? In which cities? And in what specific areas of these cities?
Siqin Wang of the University of Queensland, who led the international research team, remarked: “The clear evidence of when and where people show higher levels of pessimistic mental health signals provides important information that can be used to provide finite mental health facility allocation can.”
She added, “We have found that the provision of mental health services and the implementation of mental health policies clearly need to be adjusted at different stages of the pandemic, or indeed in any public health emergency.”
Social perception appears to have important implications for how governments and public health agencies allocate resources to ensure they go where they are needed most, when they are needed most.
The application of social sensing appears to have benefits well beyond the public health arena. “Although only in this study we applied keyword restrictions to fetch tweets that talked about COVID-19,” Huang said, “I think the idea of sentiment analysis across social media platforms can benefit various applications, like.” e.g. B. collecting opinions on products or services, supplementing official surveys or even adapting election campaign strategies and forecasting election results.
This all comes with the caveat that people who tweet may not be a fully accurate reflection of the general population as they are younger and may have more access to digital devices.
The paper, entitled Times are changing: Tracking shifts in mental health signals from the early phase to the later phase of the COVID-19 pandemic in Australia, was published in British Medical Journal Global Health. Wang and Huang’s co-authors included Tao Hu, Mengxi Zhang, Zhenlong Li, Huan Ning, Jonathan Corcoran, Asaduzzaman Khan, Yan Liu, Jiajia Zhang, and Xiaoming Li.
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