engleski

Exploring the COVID-19 infodemic in social media: a multilayer framework approach

Before the presence of social media, the distribution of information relied on traditional media, such as newspapers and television. The new era of social media arises new problems in terms of infodemics and misinformation spreading. As stated by the WHO, the COVID-19 outbreak and response culminated with massive infodemic that is dangerous because it makes it difficult for people to find trustworthy sources and reliable guidance when they need it. Automatic recognition of information spreading patterns may be helpful in the tasks of devising efficient and effective ways to disseminate true and relevant information. In the field of natural language processing (NLP) various approaches have already been defined that can differentiate between various kinds of information, for example, information with a positive or negative attitude ; or misinformation vs. mainstream information. However, the COVID 19 crisis brings a completely new realm of challenges in terms of large communication volumes that result in massive datasets, new terminology, new aspects, and new specific topics that have come into focus. The aim of our research is to perform a quantitative and qualitative study of the communication in social media during the COVID- 19 crisis. More precisely, the main objective of the project “Multilayer Framework for the Information Spreading Characterization in Social Media during the COVID-19 Crisis”, financed by the Croatian Science Foundation is to propose a multilayer framework that defines a set of approaches, methods and network based models that capture three aspects of information spreading analysis: (i) content, (ii) context and (iii) dynamic. The content- based analysis of textual information will rely on the various natural language processing methods and approaches for tasks such as keywords/keyphrases extraction and text classification. Additionally, this segment of analysis will include descriptive statistics of the textual information related to COVID-19 crisis communication characteristics. The context-based analysis refers to the analysis of various multilayer network properties on the global, middle and local scale. The analysis of the dynamics involves the analysis of cascade dynamics and other properties such as information trends changing over time. The first step toward better understanding of the infodemic is analysis of the terminology related to COVID-19, different topic and categories covered in online news portals and how trends and communication discourse are changing during the time. This step requires a detailed language analysis and deep knowledge of linguistics.

infodemic ; social networks analysis ; natural language processing ; COVID-19

nije evidentirano