@article{evans_jones_alkan_sichman_haque_oliveira_mougouei_2023, title={The Emotional Impact of COVID-19 News Reporting: A Longitudinal Study Using Natural Language Processing}, volume={2023}, ISSN={["2578-1863"]}, DOI={10.1155/2023/7283166}, abstractNote={The emotional impact of the COVID-19 pandemic and ensuing social restrictions has been profound, with widespread negative effects on mental health. We made use of the natural language processing and large-scale Twitter data to explore this in depth, identifying emotions in COVID-19 news content and user reactions to it, and how these evolved over the course of the pandemic. We focused on major UK news channels, constructing a dataset of COVID-related news tweets (tweets from news organisations) and user comments made in response to these, covering Jan 2020 to April 2021. Natural language processing was used to analyse topics and levels of anger, joy, optimism, and sadness. Overall, sadness was the most prevalent emotion in the news tweets, but this was seen to decline over the timeframe under study. In contrast, amongst user tweets, anger was the overall most prevalent emotion. Time epochs were defined according to the time course of the UK social restrictions, and some interesting effects emerged regarding these. Further, correlation analysis revealed significant positive correlations between the emotions in the news tweets and the emotions expressed amongst the user tweets made in response, across all channels studied. Results provide unique insight onto how the dominant emotions present in UK news and user tweets evolved as the pandemic unfolded. Correspondence between news and user tweet emotional content highlights the potential emotional effect of online news on users and points to strategies to combat the negative mental health impact of the pandemic.}, journal={HUMAN BEHAVIOR AND EMERGING TECHNOLOGIES}, author={Evans, Simon L. and Jones, Rosalind and Alkan, Erkan and Sichman, Jaime Simao and Haque, Amanul and Oliveira, Francisco Braulio Silva and Mougouei, Davoud}, year={2023}, month={Mar} }
 @article{haque_ajmeri_singh_2023, title={Understanding dynamics of polarization via multiagent social simulation}, volume={1}, ISSN={["1435-5655"]}, DOI={10.1007/s00146-022-01626-5}, abstractNote={It is widely recognized that the Web contributes to user polarization, and such polarization affects not just politics but also peoples' stances about public health, such as vaccination. Understanding polarization in social networks is challenging because it depends not only on user attitudes but also their interactions and exposure to information. We adopt Social Judgment Theory to operationalize attitude shift and model user behavior based on empirical evidence from past studies. We design a social simulation to analyze how content sharing affects user satisfaction and polarization in a social network. We investigate the influence of varying tolerance in users and selectively exposing users to congenial views. We find that (1) higher user tolerance slows down polarization and leads to lower user satisfaction; (2) higher selective exposure leads to higher polarization and lower user reach; and (3) both higher tolerance and higher selective exposure lead to a more homophilic social network.}, journal={AI & SOCIETY}, author={Haque, Amanul and Ajmeri, Nirav and Singh, Munindar P.}, year={2023}, month={Jan} }