Why are some social-media contents more popular than others? Opinion and association rules mining applied to virality patterns discovery

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.This research work has been partially funded by Generalitat Valenciana through project “SIIA: Tecnologias del lenguaje humano para una sociedad inclusiva, igualitaria, y accesible” with grant reference PROMETEU/2018/089. This research work has been also funded by FEDER (EU) / Ministerio de Ciencia e Innovacion - Agencia Estatal de Investigacion (Spanish Government)” through project RTI2018-094653-B-C22: “Modelang: Modeling the behavior of digital entities by Human Language Technologies”, and the LIVING-LANG project (RTI2018-094653-B-C21). Additionally, it has been backed by the work of both COST Actions: CA19134 – “Distributed Knowledge Graphs” and CA19142 – “Leading Platform for European Citizens, Industries, Academia and Policymakers in Media Accessibility”