Towards sentiment and Temporal Aided Stance Detection of climate change tweets

Climate change has become one of the most significant crises of our time. Public opinion on climate change is influenced by social media platforms such as Twitter, often divided into believers and deniers. In this paper, we propose a framework to classify a tweet's stance on climate change (denier/believer). Existing approaches to stance detection and classification of climate change tweets either have paid little attention to the characteristics of deniers' tweets or often lack an appropriate architecture. However, the relevant literature reveals that the sentimental aspects and time perspective of climate change conversations on Twitter have a major impact on public attitudes and environmental orientation. Therefore, in our study, we focus on exploring the role of temporal orientation and sentiment analysis (auxiliary tasks) in detecting the attitude of tweets on climate change (main task). Our proposed framework STASY integrates word- and sentence-based feature encoders with the intra-task and shared-private attention frameworks to better encode the interactions between task-specific and shared features. We conducted our experiments on our novel curated climate change CLiCS dataset (2465 denier and 7235 believer tweets), two publicly available climate change datasets (ClimateICWSM-2022 and ClimateStance-2022), and two benchmark stance detection datasets (SemEval-2016 and COVID-19-Stance). Experiments show that our proposed approach improves stance detection performance (with an average improvement of 12.14% on our climate change dataset, 15.18% on ClimateICWSM-2022, 12.94% on ClimateStance-2022, 19.38% on SemEval-2016, and 35.01% on COVID-19-Stance in terms of average F1 scores) by benefiting from the auxiliary tasks compared to the baseline methods. © 2023 Elsevier Ltd

Factors affecting COVID-19 mortality: an exploratory study

Purpose The purpose of this paper is to study the factors affecting COVID-19 mortality. Design/methodology/approach An empirical model is developed in which the mortality rate per million is the dependent variable, and life expectancy at birth, physician density, education, obesity, proportion of population over the age of 65, urbanization (population density) and per capita income are explanatory variables. Crosscountry data from 184 countries are used to estimate the quantile regression that is employed. Findings The estimated results suggest that obesity, the proportion of the population over the age of 65 and urbanization have a positive and statistically significant effect on COVID-19 mortality. Not surprisingly, per capita income has a negative and statistically significant effect on COVID-19 death rate. Research limitations/implications The study is based on the COVID-19 mortality data from June 2020, which have constantly being changed. What data reveal today may be different after two or three months. Despite this limitation, it is expected that this study will serve as the basis for future research in this area. Practical implications Since the findings suggest that obesity, population over the age of 65 and density are the primary factors affecting COVID-19 death, the policy-makers should pay particular attention to these factors. Originality/value To the authors' knowledge, this is first attempt to estimate the factors affecting the COVID-19 mortality rate. Its novelty also lies in the use of quantile regressions, which is more efficient in estimating empirical models with heterogeneous data.