"Double vaccinated, 5G boosted!": Learning Attitudes towards COVID-19 Vaccination from Social Media (preprint)

To address the vaccine hesitancy which impairs the efforts of the COVID-19 vaccination campaign, it is imperative to understand public vaccination attitudes and timely grasp their changes. In spite of reliability and trustworthiness, conventional attitude collection based on surveys is time-consuming and expensive, and cannot follow the fast evolution of vaccination attitudes. We leverage the textual posts on social media to extract and track users' vaccination stances in near real time by proposing a deep learning framework. To address the impact of linguistic features such as sarcasm and irony commonly used in vaccine-related discourses, we integrate into the framework the recent posts of a user's social network neighbours to help detect the user's genuine attitude. Based on our annotated dataset from Twitter, the models instantiated from our framework can increase the performance of attitude extraction by up to 23% compared to state-of-the-art text-only models. Using this framework, we successfully validate the feasibility of using social media to track the evolution of vaccination attitudes in real life. We further show one practical use of our framework by validating the possibility to forecast a user's vaccine hesitancy changes with information perceived from social media.

A Multilingual Dataset of COVID-19 Vaccination Attitudes on Twitter (preprint)

Vaccine hesitancy is considered as one main cause of the stagnant uptake ratio of COVID-19 vaccines in Europe and the US where vaccines are sufficiently supplied. Fast and accurate grasp of public attitudes toward vaccination is critical to address vaccine hesitancy, and social media platforms have proved to be an effective source of public opinions. In this paper, we describe the collection and release of a dataset of tweets related to COVID-19 vaccines. This dataset consists of the IDs of 2,198,090 tweets collected from Western Europe, 17,934 of which are annotated with the originators' vaccination stances. Our annotation will facilitate using and developing data-driven models to extract vaccination attitudes from social media posts and thus further confirm the power of social media in public health surveillance. To lay the groundwork for future research, we not only perform statistical analysis and visualisation of our dataset, but also evaluate and compare the performance of established text-based benchmarks in vaccination stance extraction. We demonstrate one potential use of our data in practice in tracking the temporal changes of public COVID-19 vaccination attitudes.

The Burden of Being a Bridge: Analysing Subjective Well-Being of Twitter Users during the COVID-19 Pandemic (preprint)

The outbreak of the COVID-19 pandemic triggers infodemic over online social media, which significantly impacts public health around the world, both physically and psychologically. In this paper, we study the impact of the pandemic on the mental health of influential social media users, whose sharing behaviours significantly promote the diffusion of COVID-19 related information. Specifically, we focus on subjective well-being (SWB), and analyse whether SWB changes have a relationship with their bridging performance in information diffusion, which measures the speed and wideness gain of information transmission due to their sharing. We accurately capture users' bridging performance by proposing a new measurement. Benefiting from deep-learning natural language processing models, we quantify social media users' SWB from their textual posts. With the data collected from Twitter for almost two years, we reveal the greater mental suffering of influential users during the COVID-19 pandemic. Through comprehensive hierarchical multiple regression analysis, we are the first to discover the strong {relationship} between social users' SWB and their bridging performance.

From #Jobsearch to #Mask: Improving COVID-19 Cascade Prediction with Spillover Effects (preprint)

An information outbreak occurs on social media along with the COVID-19 pandemic and leads to infodemic. Predicting the popularity of online content, known as cascade prediction, allows for not only catching in advance hot information that deserves attention, but also identifying false information that will widely spread and require quick response to mitigate its impact. Among the various information diffusion patterns leveraged in previous works, the spillover effect of the information exposed to users on their decision to participate in diffusing certain information is still not studied. In this paper, we focus on the diffusion of information related to COVID-19 preventive measures. Through our collected Twitter dataset, we validated the existence of this spillover effect. Building on the finding, we proposed extensions to three cascade prediction methods based on Graph Neural Networks (GNNs). Experiments conducted on our dataset demonstrated that the use of the identified spillover effect significantly improves the state-of-the-art GNNs methods in predicting the popularity of not only preventive measure messages, but also other COVID-19 related messages.

An Exploratory Study of COVID-19 Information on Twitter in the Greater Region (preprint)

The outbreak of the COVID-19 leads to a burst of information in major online social networks (OSNs). Facing this constantly changing situation, OSNs have become an essential platform for people expressing opinions and seeking up-to-the-minute information. Thus, discussions on OSNs may become a reflection of reality. This paper aims to figure out the distinctive characteristics of the Greater Region (GR) through conducting a data-driven exploratory study of Twitter COVID-19 information in the GR and related countries using machine learning and representation learning methods. We find that tweets volume and COVID-19 cases in GR and related countries are correlated, but this correlation only exists in a particular period of the pandemic. Moreover, we plot the changing of topics in each country and region from 2020-01-22 to 2020-06-05, figuring out the main differences between GR and related countries.

Effectiveness of a group functional power training program for frail older adults implemented through neighbourhood senior centres – a randomised controlled study (preprint)

Background Several trials have demonstrated the efficacy of resistance training to reduce frailty and improve function of older adults. To narrow the research-practice gap, we designed and evaluated the implementation of a community-delivered group-based functional power training (FPT) program for frail older adults within their neighbourhoods.Methods Two-arm, multicentre assessor-blind stratified randomised controlled trial at four local senior activity centres. Older adults (n = 61) with low handgrip strength (HGS) were randomised to intervention (IG) or control (CG) group. The IG underwent the FPT program (power and balance exercises using simple equipments) delivered by a community provider. The 12-week program comprised 2 × 60 mins sessions/wk. CG continued usual activities at the centres. Functional performance (SPPB and TUG), HGS, knee extensor strength (KES), and frailty status were assessed at baseline and 3-month. Program implementation was evaluated using RE-AIM framework.Results The program was halted due to Coronavirus Disease 2019 related suspension of senior centre activities. Results are reported from four centres (n = 61), which completed the program. IG showed significant improvement of moderate effect sizes in frailty status (0.36 points, 95CI [0.09, 0.64], p = 0.011) and SPPB (0.51 points, 95CI [0.13, 0.89], p = 0.010). IG improvement in TUG (0.57 s, 95CI [-0.07, 1.20], p = 0.080) did not achieve significance and there were no effects for HGS and KES. Only SPPB showed greater improvement in IG than CG (p = 0.047). The community program exhibited good reach, effectiveness, adoption, and implementation.Conclusions FPT is superior to regular activities at local senior centres in improving physical function and can be successfully implemented for frail older adults in their neighbourhoods.Trial registration: ClinicalTrials.gov, NCT04438876. Registered 19 June 2020 – Retrospectively registered, https://clinicaltrials.gov/ct2/show/NCT04438876?term=NCT04438876