Abstract
Abstract Real-time social media data hold great conceptual promise for research and policymaking, but also face substantial limitations and shortcomings inherent to processing re-purposed data in near-real-time. This paper aims to fill two research gaps important for understanding utility of real-time social media data for policymaking: What policy-relevant information is contained in this data and whether this information changes in periods of abrupt social, economic, and policy change. To do so, this paper focuses on two salient policy areas heavily affected by the lockdown policies responding to the 2020 COVID-19 crisis – early childhood education and care policies, and labor market policies focused on (un)employment. We utilize Twitter data for a four-month period during the first wave of COVID-19 and data for the same four-month period the preceding year. We analyze this data using a novel method combining structural topic models and latent semantic scaling, which allows us to summarize the data in detail and to test for change of content between the period of ‘normalcy’ and period of ‘crisis’. With regards to the first research gap, we show that there is policy-relevant information in Twitter data, but that the majority of our data is of limited relevance, and that the data that is relevant present some challenges and limitations. With regards to the second research gap, we successfully quantify the change in relevant information between periods of ‘normalcy’ and ‘crisis’. We also comment on the practicality and advantages of our approach for leveraging micro-blogging data in near real-time.
Highlights
The velocity of data is a concept receiving a substantial amount of traction in academia, in no small part due to it being one of the characteristics distinguishing ‘big data’ from more conventional data sources (Emmanuel and Stanier 2016; Ward and Barker 2013; Ylijoki and Porras 2016)
This paper aims to fill two research gaps important for understanding utility of real-time social media data for policymaking: What policy-relevant information is contained in this data and whether this information changes in periods of abrupt social, economic, and policy change
Applying the proposed method we focus on a 20-topic model for the early childhood education and care (ECEC) sub-section and a 30-topic model for the labour market (LM) sub-section of our corpus
Summary
The velocity of data is a concept receiving a substantial amount of traction in academia, in no small part due to it being one of the characteristics distinguishing ‘big data’ from more conventional data sources (Emmanuel and Stanier 2016; Ward and Barker 2013; Ylijoki and Porras 2016). In the case of Twitter, the social media platform this paper focuses on, the two primary motivations for using the platform are to connect with others and to seek or share information and advice (Chen 2011; Johnson and Yang 2009), which results in users posting updates about their life (Java et al 2007) or sharing their beliefs and concerns with regards to current (crisis) events (Gilardi et al Forthcoming; McNeill, Harris, and Briggs 2016; Signorini, Segre, and Polgreen 2011) Such tweets provide very detailed information at micro-level and in near real-time, lending them well to being utilized in the policymaking process, especially in situations requiring a rapid policy response. This constitutes the first research gap this paper explores – the lack of knowledge with regards to what relevant information for social and economic policymaking do real-time social media data streams contain
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