Effect of Informational Divergence on the Mental Health of the Population in Crisis Situations: A Study in COVID-19

Informational divergence emerged as a significant phenomenon during the COVID-19 health crisis. This period was characterized by information overload and changes in the communication of public health recommendations and policies by authorities and media outlets. This study examines the impact of such divergence on the population’s mental health, focusing on primary emotions expressed in comments across digital ecosystems. A media EMIC approach was used to analyze digital ecosystems during March and April 2020. Data were collected from Twitter, YouTube, Instagram, official press websites, and internet forums, yielding 3,456,387 communications. These were filtered to extract emotion-expressing content, resulting in 106,261 communications. Communications were categorized into primary emotions (anger, disgust, joy, fear, and sadness) using an exclusionary emotion assignment procedure. Analysis techniques included polarity and term frequency calculation, content analysis using Natural Language Understanding, emotion intensity measurement using IBM Watson Analytics, and data reliability assessment using the ISMA-OWA operator. The findings suggest that exposure to informational divergence from governments, health organizations, and media negatively affected mental health, evidenced by sadness, fear, disgust, and anger, which are associated with elevated levels of stress, anxiety, and information fatigue. In contrast, information perceived as reflecting coordination, support, and solidarity elicited positive emotional responses, particularly joy.