COVID-19 and Indonesia.

Social media and vaccine hesitancy

Steven Lloyd Wilson, Charles Shey Wiysonge

BMJ Global Health · BMJ

BACKGROUND: Understanding the threat posed by anti-vaccination efforts on social media is critically important with the forth coming need for world wide COVID-19 vaccination programs. We globally evaluate the effect of social media and online foreign disinformation campaigns on vaccination rates and attitudes towards vaccine safety. METHODS: Weuse a large-n cross-country regression framework to evaluate the effect ofsocial media on vaccine hesitancy globally. To do so, we operationalize social media usage in two dimensions: the use of it by the

Sentiments and emotions evoked by news headlines of coronavirus disease (COVID-19) outbreak

Faheem Aslam, Tahir Mumtaz Awan, Jabir Hussain Syed, Aisha Kashif, Mahwish Parveen

Humanities and Social Sciences Communications · Palgrave Macmillan

Abstract The chronic nature of coronavirus disease (COVID-19) outbreak and lack of success in treatment and cure is creating an environment that is crucial for mental wellbeing. Presently, we extracted and classified sentiments and emotions from 141,208 headlines of global English news sources regarding the coronavirus disease (COVID-19). The headlines considered were those carrying keyword coronavirus between the time frame 15 Janaury, 2020 to 3 June, 2020 from top rated 25 English news sources. The headlines were classified into positive,

Coronavirus herd immunity optimizer (CHIO)

Mohammed Azmi Al‐Betar, Zaid Abdi Alkareem Alyasseri, Mohammed A. Awadallah, Iyad Abu Doush

Neural Computing and Applications · Springer Science+Business Media

Fake news, disinformation and misinformation in social media: a review

Esma Aı̈meur, Sabrine Amri, Gilles Brassard

Social Network Analysis and Mining · Springer Science+Business Media

Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions

Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G. Parker, Munmun De Choudhury

Large language models have abilities in creating high-volume human-like texts and can be used to generate persuasive misinformation. However, the risks remain under-explored. To address the gap, this work first examined characteristics of AI-generated misinformation (AI-misinfo) compared with human creations, and then evaluated the applicability of existing solutions. We compiled human-created COVID-19 misinformation and abstracted it into narrative prompts for a language model to output AI-misinfo. We found significant linguistic differences within human-AI pairs, and patterns of AI-misinfo in enhancing

COVIDLies: Detecting COVID-19 Misinformation on Social Media

Tamanna Hossain, Robert L. Logan, Arjuna Ugarte, Yoshitomo Matsubara, Sean D. Young et al.

The ongoing pandemic has heightened the need for developing tools to flag COVID-19related misinformation on the internet, specifically on social media such as Twitter. However, due to novel language and the rapid change of information, existing misinformation detection datasets are not effective for evaluating systems designed to detect misinformation on this topic. Misinformation detection can be divided into two sub-tasks: (i) retrieval of misconceptions relevant to posts being checked for veracity, and (ii) stance detection to identify whether the posts