How can social media analytics assist authorities in pandemic-related policy decisions? Insights from Australian states and territories

Abstract

Background and objectives: Due to COVID-19, various countries introduced lockdowns and limited citizen movements. These restrictions triggered an increased use of digital technologies and platforms by the public. This provides an opportunity for the authorities to capture public perceptions on COVID-19 from social media channels to make informed decisions. The use of social media analytics during pandemics for decision-making, however, is an understudied area of research. Thus, this study aims to generate insights into how social media analytics can assist authorities in pandemic-related policy decisions. Methods: This study involved a social media analysis approach-i.e., systematic geo-Twitter analysis-that contains descriptive, content, sentiment, and spatial analyses. Australian states and territories are selected as the case study context for the empirical investigation. This study collected 96,666 geotagged tweets (originated from Australia between 1 January and 4 May 2020), and analysed 35,969 of them after data cleaning. Results: The findings disclose that: (a) Social media analytics is an efficient approach to capture the attitudes and perceptions of the public during a pandemic; (b) Crowdsourced social media data can guide interventions and decisions of the authorities during a pandemic, and; (c) Effective use of government social media channels can help the public to follow the introduced measures/restrictions. Conclusion: The findings are invaluable for authorities to understand community perceptions and identify communities in needs and demands in a pandemic situation, where authorities are not in a position to conduct direct and lengthily public consultations.

Changes in Public Response Associated With Various COVID-19 Restrictions in Ontario, Canada: Observational Infoveillance Study Using Social Media Time Series Data

“…While our study was focused on investigating the unique impact of multiple pandemic restrictions on changes in public opinion over time, which has not been examined in prior literature, we found that the association between new COVID-19 case counts and collective attention-one of our ancillary findings-was consistent with prior studies, including the impact of new daily cases on Australian tweets [11] as well as the impact of daily COVID-19 incidence on Reddit posts and comments across the United Kingdom, the United States, Canada, and Italy [12].…”

“…The frequency of tweets about COVID-19-related topics has also been used to measure perceived susceptibility and severity of COVID-19 and was correlated with interventions, public events, and case counts [10]. Another descriptive study of COVID-19-related tweets from Australian states and territories detailed changes in aggregate sentiment trends in relation to COVID-19-related deaths and major COVID-19-related policy events [11] (eg, blocking arrivals from specific countries, expansion of testing criteria, and limits on outdoor gatherings). However, due to the lack of multivariate statistical modeling in the studies listed above, it was not possible to disentangle the independent contribution of these events on tweet frequency or aggregate sentiment and to investigate their relative importance in the shaping of public opinion.…”

What social media told us in the time of COVID-19: a scoping review

“…93 In Australia, Yigitcanlar and colleagues analysed 96 666 tweets and identified that the public's attitude could be captured efficiently through social media analytics. 94 One qualitative content analysis of 30 profiles from Instagram, Twitter, and TikTok in Brunei identified five types of attitudes towards physical distancing: fear, responsibility, annoyance, fun, and resistance. 80 In Turkey, to show the effects of social media on human psychology and behaviour, Arpaci and colleagues 52 used evolutionary clustering analysis on 43 million tweets between March 22 and March 30, 2020.…”

Iktishaf+: A Big Data Tool with Automatic Labeling for Road Traffic Social Sensing and Event Detection Using Distributed Machine Learning

“…Twitter allows users to post short text messages called tweets. A massive amount of real-time data is posted by millions of users on various topics including transportation and real-time road traffic [ 4 , 6 , 7 , 8 ]. In the recent decade, the use of Twitter and other social media by researchers and practitioners to study different issues in many application domains and sectors has steadily increased [ 9 , 10 , 11 , 12 , 13 , 14 ].…”