@article{haque_singh_2024, title={NewsSlant: Analyzing Political News and Its Influence Through a Moral Lens}, volume={1}, ISSN={["2329-924X"]}, url={https://doi.org/10.1109/TCSS.2023.3341910}, DOI={10.1109/TCSS.2023.3341910}, abstractNote={Political news is often slanted toward its publisher’s ideology and seeks to influence readers by focusing on selected aspects of contentious social and political issues. We investigate political slants in news and their influence on readers by analyzing election-related news and readers’ reactions to the news on Twitter. To this end, we collected election-related news from six major U.S. news publishers who covered the 2020 U.S. presidential election. We computed each publisher’s political slant based on the favorability of its news toward the two major parties’ presidential candidates. We find that the election-related news coverage shows signs of political slant both in news headlines and on Twitter. The difference in news coverage of the two candidates between the left-leaning ( <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">left</small> ) and right-leaning ( <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">right</small> ) news publishers is statistically significant. The effect size is larger for the news on Twitter than for headlines. And, news on Twitter expresses stronger sentiments than the headlines. We identify moral foundations in readers’ reactions to the news on Twitter based on the moral foundation theory. Moral foundations in readers’ reactions to <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">left</small> and <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">right</small> differ statistically significantly, though the effects are small. Further, these shifts in moral foundations differ across social and political issues. User engagement on Twitter is higher for <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">right</small> than for <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">left</small> . We posit that an improved understanding of slant and influence can enable better ways to combat online political polarization.}, journal={IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS}, author={Haque, Amanul and Singh, Munindar P.}, year={2024}, month={Jan} }
 @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={Abstract}, journal={AI & SOCIETY}, author={Haque, Amanul and Ajmeri, Nirav and Singh, Munindar P.}, year={2023}, month={Jan} }