English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

While the world has been combating COVID-19 for over three years, an ongoing"Infodemic" due to the spread of fake news regarding the pandemic has also beena global issue. The existence of the fake news impact different aspect of ourdaily lives, including politics, public health, economic activities, etc.Readers could mistake fake news for real news, and consequently have lessaccess to authentic information. This phenomenon will likely cause confusion ofcitizens and conflicts in society. Currently, there are major challenges infake news research. It is challenging to accurately identify fake news data insocial media posts. In-time human identification is infeasible as the amount ofthe fake news data is overwhelming. Besides, topics discussed in fake news arehard to identify due to their similarity to real news. The goal of this paperis to identify fake news on social media to help stop the spread. We presentDeep Learning approaches and an ensemble approach for fake news detection. Ourdetection models achieved higher accuracy than previous studies. The ensembleapproach further improved the detection performance. We discovered featuredifferences between fake news and real news items. When we added them into thesentence embeddings, we found that they affected the model performance. Weapplied a hybrid method and built models for recognizing topics from posts. Wefound half of the identified topics were overlapping in fake news and realnews, which could increase confusion in the population.

Fake News Detection and Behavioral Analysis: Case of COVID-19