Pandemic rumor identification on social networking sites: A case study of COVID-19

Digital Rumors, because of the ease and innovations in social networking technologies, has become an important issue. These rumors become a critical issue in a disaster, epidemic, or pandemic. Considering classification power of conventional and deep learning techniques, we propose a hybrid learning technique that identifies rumors effectively. For this, TF-IDF description has been used to build a stack of multiple conventional learning techniques;logistic regression, Naïve Bayes, and random forest. Whereas, word-embedding features have been used for purpose of deep learning;LSTM and LSTM-RNN. The combination of LSTM and RNN makes this study unique in the field of rumor detection. With LSTM and RNN gated architectures, huge series rumor tweets may be efficiently managed. To aggregate the decisions, the labels of deep learning and the stack of conventional learning have been combined using majority voting based ensemble classification. To evaluate the performance of the proposed technique, we used publically available standard COVID-19 RUMOR dataset. The proposed technique obtains 99.02% accuracy, which shows its effectiveness. The dataset utilized and the ensemble model created for rumor identification distinguish our work from existing methods. © 2021 ACM.

Challenges in accessing maternal and child health services during COVID-19 and the potential role of social networking technologies.

Background: The COVID-19 pandemic causes new challenges to women and their babies who still need to access postnatal care amidst the crisis. The novel application of social network technologies (SNTs) could potentially enhance access to healthcare during this difficult time. Objectives: This study describes the challenges experienced in accessing maternal and child health services by women with limited or no education during this COVID-19 pandemic and discusses the potential of SNTs to support maternal and child health amidst this crisis. Methods: We administered surveys to women who had recently given birth in a rural setting and interviewed a purposively selected subset to ascertain their experiences of accessing maternal and child health services during the COVID-19 pandemic. Our analysis involved descriptive analysis of quantitative data using STATA 13 to describe study participants' characteristics, and content analysis of qualitative data to derive categories describing maternal health challenges. Results: Among 50 women, the median age was 28 years (interquartile range 24-34), 42 (84%) completed upper primary education. Access to the health facility was constrained by transport challenges, fear of contracting COVID-19, and delays at the facility. Due to the COVID-19 crisis, 42 (84%) women missed facility visits, 46 (92%) experienced financial distress, 43 (86%) had food insecurity, and 44 (88%) felt stressed. SNTs can facilitate remote and timely access to health services and information, and enable virtual social connections and support. Conclusion: SNTs have the potential to mitigate the challenges faced in accessing maternal and child health services amidst the ongoing COVID-19 pandemic.