Emotional experiences and psychological well-being in 51 countries during the COVID-19 pandemic.

The COVID-19 pandemic presents challenges to psychological well-being, but how can we predict when people suffer or cope during sustained stress? Here, we test the prediction that specific types of momentary emotional experiences are differently linked to psychological well-being during the pandemic. Study 1 used survey data collected from 24,221 participants in 51 countries during the COVID-19 outbreak. We show that, across countries, well-being is linked to individuals' recent emotional experiences, including calm, hope, anxiety, loneliness, and sadness. Consistent results are found in two age, sex, and ethnicity-representative samples in the United Kingdom (n = 971) and the United States (n = 961) with preregistered analyses (Study 2). A prospective 30-day daily diary study conducted in the United Kingdom (n = 110) confirms the key role of these five emotions and demonstrates that emotional experiences precede changes in well-being (Study 3). Our findings highlight differential relationships between specific types of momentary emotional experiences and well-being and point to the cultivation of calm and hope as candidate routes for well-being interventions during periods of sustained stress. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Towards automated check-worthy sentence detection using Gated Recurrent Unit.

People are exposed to a lot of information daily, which is a mix of facts, opinions, and false claims. The rate at which information is created and spread has necessitated an automated fact-checking mechanism. In this work, we focus on the first step of the fact-checking system, which is to identify whether a given sentence is factual. We propose a glove embedding-based gated recurrent unit pipeline for check-worthy sentence detection, referred to as G2CW framework. It detects whether a given sentence has check-worthy content in it or not; furthermore, if it has check-worthy content, whether it is important or not, from a fact-checking perspective. We evaluate our proposed framework on two datasets: a standard ClaimBuster dataset commonly used by the research community for this problem and a self-curated IndianClaim dataset. Our G2CW framework outperforms prior work with 0.92 as F1-score. Furthermore, our G2CW framework, when trained on the ClaimBuster dataset, performs the best on the IndianClaims dataset.

Public reactions towards Covid-19 vaccination through twitter before and after second wave in India.

Social media have a significant impact on opinion building in public. Vaccination in India started in January 2021. We have seen many opinions towards vaccination of the people, as vaccination is one of the most crucial steps toward the fight against COVID-19. In this paper, we have compared the public's sentiments towards COVID vaccination in India before the second wave and after the second wave. We worked by extracting tweets regarding vaccination in India, building our datasets. We extracted 5977 tweets before the second wave and 42,936 tweets after the second wave. We annotated the collected tweets into four categories, namely Provaccine, Antivaccine, Hesitant and Cognizant. We built a baseline model for sentiment analysis and have used multiple classification techniques among which Random Forest using the TF-IDF vectorization technique gave the best accuracy of 69% using max-features and n-estimators as parameters.