Artificial Intelligence for Social Media Analysis: Detecting Pseudo-Facts in Digital Environments

The rapid proliferation of misinformation and pseudo-facts on social media platforms has raised significant concerns regarding their impact on public opinion, institutional trust, and decision-making processes. This study investigates the effectiveness of artificial intelligence in detecting pseudo-facts by distinguishing between emotional and rational discourse in social media content. Drawing on a dataset of 26,322 Arabic tweets related to a controversial judicial event in Lebanon, the research employs machine learning techniques, including K-Nearest Neighbors, Naïve Bayes, and Logistic Regression, to classify content based on emotional intensity. A manually labeled subset of 600 tweets was used to train and evaluate the models, with Logistic Regression achieving the highest performance across multiple evaluation metrics. The findings reveal that emotional discourse overwhelmingly dominates the dataset, accounting for more than 89% of the analyzed content, consistent with prior evidence of an association between emotional expression and the dissemination of pseudo-facts, while noting that the study classifies discourse as emotional or rational rather than verifying the factual accuracy of individual claims. The study further demonstrates that high classification accuracy can be achieved using relatively small, context-specific datasets, particularly within non-Western linguistic environments. Beyond its methodological contribution, this research offers a proof-of-concept for using artificial intelligence as an analytical aid for organizations, supporting information risk assessment and a clearer understanding of digital communication dynamics. By integrating sentiment analysis with socio-political context, the study advances current knowledge on misinformation detection and highlights the importance of emotion-driven narratives in shaping digital discourse.