Scilens news platform

Romanou, Angelika; Smeros, Panayiotis; Castillo, Carlos; Aberer, Karl

doi:10.14778/3415478.3415521

Cited by 5 publications

References 34 publications

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On Representation Learning for Scientific News Articles Using Heterogeneous Knowledge Graphs

Romanou

Smeros

Aberer

2021

Companion Proceedings of the Web Conference 2021

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In the era of misinformation and information inflation, the credibility assessment of the produced news is of the essence. However, fact-checking can be challenging considering the limited references presented in the news. This challenge can be transcended by utilizing the knowledge graph that is related to the news articles. In this work, we present a methodology for creating scientific news article representations by modeling the directed graph between the scientific news articles and the cited scientific publications. The network used for the experiments is comprised of the scientific news articles, their topic, the cited research literature, and their corresponding authors. We implement and present three different approaches: 1) a baseline Relational Graph Convolutional Network (R-GCN), 2) a Heterogeneous Graph Neural Network (HetGNN) and 3) a Heterogeneous Graph Transformer (HGT). We test these models in the downstream task of link prediction on the: a) news article -paper links and b) news article -article topic links. The results show promising applications of graph neural network approaches in the domains of knowledge tracing and scientific news credibility assessment.

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On Representation Learning for Scientific News Articles Using Heterogeneous Knowledge Graphs

Romanou

Smeros

Aberer

2021

Companion Proceedings of the Web Conference 2021

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COVID and social media

2021

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The Coronavirus pandemic is one of the most devastating encounters in modern times. Over 175 million cases have been recorded globally with over 3.5 million deaths. Disseminating information to billions of people during the pandemic has been challenging, and social media has been one of the key resources for the public during these excruciating circumstances. Social media and other online sources have made it easier to access information on a variety of topics. This article presents an exploration of social media trends pertinent to information on the COVID-19 pandemic, the use of several technological advances, as well as methods for evaluating their effectiveness in combating COVID-19. We examine global case studies on the use of data from various sources to tackle COVID-19, address the issue of trust between the government and the public, and shed light on the manner in which it influences the public perception of information. We delve into the role of advances in web technology and data science in curbing COVID-19 while also touching upon the impacts in the field of smart living and healthcare. We examine studies from regions around the world, explore how the pandemic has affected people from different walks of life, and peek into the utilization of advances for disseminating information as well as curbing the spread of the virus. Additionally, we briefly discuss how the works investigated here can open pathways of research to help in further enhancing the situation as we all head towards the light at the end of the tunnel, and strive to restore global normalcy.

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