<p>Social media platforms are commonly used to communicate and generate information. In many cases, this information is uncontrolled, which makes it difficult to use and analyze. Although there exist studies focused on data assessment and validation, most of them are limited to specific scenarios. Thus, a more general and flexible model is needed. In previous work, we presented a quality model: FULMQA based on 8 metrics, including credibility, timeliness, popularity, relevancy, accessibility, reliability, presentation and accuracy. In this paper, we propose an extension of FULMQA to provide a robust model to perform various quality analysis of posts on social media based on two levels: content and user, by integrating other different measures and considering various features. The metrics used in 2&#xa0;L-DQM model were selected with care to make sure that all factors are considered when assessing the quality of the data. While many studies focus on particular and limited features, such as follower counts or engagement rates, etc., our approach takes into account the entire set of relevant indicators. We hope to provide a comprehensive view of social media activity by combining quantitative and qualitative data. This inclusive approach enables us to capture the multidimensional nature of social media interactions. As a result, our research aims to improve and encourage a better comprehension of data across various social media platforms. With the usage of this model, users will be able to obtain a thorough understanding of the caliber of the data they come across on social media, assisting them in making defensible selections. Then, we performed extensive experiments to evaluate the effectiveness of our model using a Twitter dataset containing 30000 tweets with all the needed features for user and content level. The findings indicate that the model can successfully evaluate all tweets with high performance.</p>