Beyond the cross-lingual effects, drill down analysis revealed that bag-of-words topic classification and event extraction using intra-sentential regular expressions were still letting through a proportion of non-events. We sampled 274 English news articles by hand from the BioCaster portal's implementation of the C2 algorithm using a 7 day baseline window and found that approximately 30% of positively classified news articles fell just below the borderline of our case definition. Commonly misclassified topics included: vaccination campaigns, factual advice on avoidance and treatment of infectious diseases, improvements in surveillance facilities and surveillance exercises. Often the articles mentioned an infectious disease and cited facts about case numbers such as "90% of cholera cases reported annually…" or an analysis of historical events. This points to a need to strengthen discourse analysis, such as inter-sentential causality and inclusion relations between events which the current intra-sentential template driven approach does not handle well. The study also raises several questions about factors in the imbalance of reporting: why did Dengue in Brazil (e12) or FMD in China (e4) receive such massive local coverage but disproportionately less in the English media? Why did cholera in Angola (e9) or influenza in Romania (e8) receive comparatively low coverage overall? We also observed that the USA epidemics (e15 and e16) were widely reported in English but not so greatly in other languages.