The COVID-19 pandemic has not only affected health but has also enabled researchers to gain insights into the spread of ideas through social networks and other interactive platforms. The pandemic, along with vast amounts of data on the spread of disease, has opened the door for analyzing how information and ideas spread among people, often in a manner similar to viruses. This phenomenon, known as "infodemic," is becoming increasingly important in a world where social media has become the primary medium of communication.

Mathematician and biology lecturer Nicholas Landry investigates how the structure of social networks can be used to understand the spread of not just diseases but also information. These two forms of "infection" share common characteristics, but the spread of information can be even more complex. His work indicates that social networks can be utilized to precisely track how viral trends or misinformation spreads, while the spread of infectious diseases like measles or chickenpox is harder to predict.

Landry, along with colleagues from the University of Vermont, employs a hybrid model for analyzing social networks. This model not only tracks social contacts but also studies the rules governing the spread of information. For instance, during the COVID-19 pandemic, social networks were crucial for disseminating information—both accurate and false. Simultaneously, it opened up space for studying "groupthink" effects, where a large number of people adopt ideas or information that spreads through social channels, often without critical scrutiny.

Interestingly, bots on social media further exacerbated the spread of misinformation during the pandemic, highlighting how easily public opinion can be manipulated through algorithmically generated content. Studies show that when a certain number of people within a network start repeating specific information, even if incorrect, the rest of the group tends to accept that idea, which is a mechanism similar to the spread of infectious diseases.

Another important aspect of the research is tracking misinformation that spreads through social networks, which can have serious consequences for public health. The Harvard Gazette highlights how false information about COVID-19 treatments, which spread rapidly through social platforms, can confuse the public and disrupt the implementation of correct health measures. Institutions like Facebook and Twitter have faced challenges in removing this misinformation, but artificial intelligence is not always effective enough in filtering true from false information.

This research points to the need for a greater understanding of how ideas spread through social networks and how this process can be better managed to prevent the spread of misinformation. The dissemination of information, similar to the spread of viruses, requires a thoughtful strategy to ensure accuracy and reliability, especially in crisis situations like a pandemic.

Social networks play a crucial role in the spread of both diseases and information, making it essential to continue research so that we can better understand these processes and improve models that can help mitigate both physical and informational "infections".

Source: University of Virginia