Why are some social-media contents more popular than others? Opinion and association rules to

Discovering the main features of virality patterns in Twitter is the focus of this research. Five trending topics related to the COVID-19 pandemic were selected for the study, with Spanish as the target language. To carry out the discovery of virality patterns, we applied opinion mining techniques that enable us to structure the information based on the polarity of the messages and the emotions they contain. After transforming the information from an unstructured textual representation to a structured one, data mining techniques were applied, specifically association rules mining. Message patterns with the highest virality (high shares and high likes), and at the same time the most relevant characteristics of the patterns with less impact were extracted. After an exhaustive analysis of the most relevant non-redundant rules, it can be concluded that messages with a high-negative polarity and a very high emotional charge, especially emotions that have intensified with the COVID-19 pandemic, such as fear, sadness, anger and surprise are more likely to go viral in social media. By contrast, messages with little news coverage in the media, few authors, and the absence of surprise are relevant features when it comes to seeing messages with very low dissemination in social media.

Automated summary

The focus of this research is to discover the main features of virality patterns in Twitter. Five trending topics related to the COVID-19 pandemic in Spanish language were selected. Opinion mining techniques were used to structure the information based on message polarity and emotions. Data mining techniques, specifically association rules mining, were then applied to identify the highest viral message patterns and the relevant characteristics of patterns with low impact. The analysis revealed that messages with high-negative polarity and intense emotions, particularly fear, sadness, anger, and surprise intensified by the COVID-19 pandemic, are more likely to go viral on social media. On the other hand, messages with little news coverage, few authors, and the absence of surprise are relevant features for messages with very low dissemination on social media.