Julio Amador

Julio Amador holds a PhD in Economics from the University of Essex, UK. His area of expertise is applied game theory and machine learning (ML).

Dr. Amador has held different research positions, both in the UK and abroad, and is now an Imperial College Research Fellow affiliated to Imperial College’s Data Science Institute and Business School.

His research includes big-data studies of online political participation and applying ML to categorize public opinion and automatically identifying fake news. Dr. Amador is currently dedicated to the study of misinformation.

As the locus of public discourse has shifted from mass media to online platforms, policymakers are facing the twin challenges of expert backlash and economic incentives for providing stories that people want to hear, even if they are false. The problem arises, of course, when audiences influenced by contentious stories lack the background to evaluate the evidence or have strong antipathies for the parties providing the falsification, as it is currently possible to observe regarding the Covid-19.

Indeed, the spread of COVID has brought with it the spread of misinformation and fake news. False claims such as those stating 5G towers spread COVID or those questioning the effectiveness of face-masks, permeate through online social media feeds. This information may convince people to take actions that are not only harmful to themselves but may compromise the security of others.

To understand why people fall for COVID-related misinformation and why such news are generated and spread, I will use Twitter data to trace arguments related to misinformation.

By doing so I expect to shed some light behind the reasons why people are consuming information that may be harmful to their own health.