How the 2020 US Presidential election impacted sleep and its relationship to public mood and alcohol consumption.

OBJECTIVES: Major sociopolitical events can influence the general public's affective state and other affect-related processes, such as sleep. Here, we investigated the extent that the 2020 US presidential election impacted sleep, public mood, and alcohol consumption. We also explored the relationship between affect and sleep changes during the peak period of election stress. PARTICIPANTS: US-residing (n = 437) and non-US-residing (n = 106) participants were recruited online for participation in the study. METHODS: A non-representative, convenience sample responded to daily assessments of their affect, sleep, and alcohol consumption during a baseline period (October 1-13, 2020) and in the days surrounding the 2020 US Election (October 30-November 12, 2020). RESULTS: Analyses determined changes within and between US and non-US participants. Election Day evoked significantly reduced sleep amount and efficiency, coupled with heightened stress, negative affect, and increased alcohol use. While US participants were significantly more impacted in a number of domains, non-US participants also reported reduced sleep and greater stress compared to baseline. Across participants, disrupted sleep on Election Night correlated with changes in emotional well-being and alcohol consumption on Election Day. CONCLUSION: These results suggest that major sociopolitical events can have global impacts on sleep that may interact with significant fluctuations in public mood and well-being. Further, while the largest impact is on the local population, these results suggest that the effects can extend beyond borders. These findings highlight the potential impact of future sociopolitical events on public well-being.

A Deep Multi-kernel Uniform Capsule Approach for Hate Speech Detection

Hate Speech is an expression that expresses hatred towards people of a specific ethnic group or nationality and incites hatred. Even though many countries have anti-hate speech legislation, hate speech can spread in the native language on social media platforms, resulting in violent riots and protests that spiral out of control and result in anti-social events. Hence, hate speech has caused a crucial social issue. Thus, various intelligent mechanisms have been employed to classify hate speech, depending on the category. A deep learning model has certain limitations for providing n-gram features for text classification of the native language. As a result, in this paper, the Multi-kernel uniform capsule network for multilingual languages is proposed. The proposed method employs a Multi-kernel uniform capsule network to improve feature selection performance by utilizing the capsule network routing algorithm. The experiments were carried out on political, COVID-19 and vaccination, lockdown, and multilingual dataset. The experimental results demonstrate that the proposed methods achieve adequate results when compared with other machine learning models for hate speech detection. © 2022, Springer Nature Switzerland AG.