In　recent　academic　discourse,　the　escalation　of　counterfeit　news　dissemination　via　social　media　platforms　has　been　identified　as　a　burgeoning　concern.　Conventional,　labor-intensive　screening　practices　are　proving　inadequate　in　curtailing　the　rapid　proliferation　of　such　false　information,　underscoring　the　necessity　for　the　deployment　of　automated,　machine　learning-based　classification　systems.　This　study　focuses　on　the　application　of　the　Multinomial　Naïve　Bayes　(NB)　algorithm,　grounded　in　Bayes　Theorem,　notable　for　its　minimal　computational　demand,　reduced　energy　requirements,　and　user-friendliness.　Utilizing　data　primarily　from　the　years　2016　and　2017,　the　research　employs　this　algorithm　to　develop　a　model,　subsequently　achieving　a　classification　precision　of　95%.　Prior　to　model　training,　data　preprocessing　was　conducted　to　distill　the　essential　elements　of　each　dataset.　The　model＇s　efficacy　was　assessed　using　conventional　metrics,　including　precision,　recall,　and　the　F1-score.　In　addition,　the　Support　Vector　Classifier　(SVC)　was　employed　as　a　comparative　benchmark.　Despite　its　relative　simplicity　compared　to　the　SVC,　the　NB　algorithm　demonstrated　commensurate　performance.　Further,　the　research　delved　into　the　distinct　attributes　of　counterfeit　news,　observing　a　higher　prevalence　of　imagery　usage　in　false　reports,　while　genuine　news　more　frequently　cited　sources　for　information.　In　summary,　this　paper　elucidates　the　capabilities　and　constraints　of　the　Multinomial　Naïve　Bayes　algorithm　in　the　context　of　fake　news　classification,　underscoring　its　potential　utility　in　this　domain.　©　2024　IEEE.