Fulmqa: a fuzzy logic-based model for social media data quality assessment

Nowadays, with the advancement of information technology and the growing importance of social media, social media platforms such as Twitter and Facebook have become deeply entrenched in our lives and are growing rapidly around the world. On these platforms, users have the freedom to share and publish whatever they want without control, leading to faster generation and dissemination of information, as a result, rumors and false information can then be spread and seen by a larger number of people. Consequently, assessing the quality of these data proves to be of major importance. In this paper, we aim to present a new and efficient quality assessment model including the quality metrics needed for an accurate assessment. Our work presents an extension of the SMDQM (Social Media Data Quality Model) (Reda and Zellou, in: International conference on innovative research in applied science, engineering and technology, IEEE, 2022), which is used to assess the quality of data provided by social media platforms. We suggest using fuzzy logic to describe data quality metrics in order to overcome imprecision and subjectivity. Next, we perform extensive experiments to evaluate the performance of our model using a real-world implementation by performing an evaluation on two separate Twitter data sets. The findings indicate that the model can successfully evaluate all tweets with high performance.