  The spread of misinformation in social media outlets has become a prevalent
societal problem and is the cause of many kinds of social unrest. Curtailing
its prevalence is of great importance and machine learning has shown
significant promise. However, there are two main challenges when applying
machine learning to this problem. First, while much too prevalent in one
respect, misinformation, actually, represents only a minor proportion of all
the postings seen on social media. Second, labeling the massive amount of data
necessary to train a useful classifier becomes impractical. Considering these
challenges, we propose a simple semi-supervised learning framework in order to
deal with extreme class imbalances that has the advantage, over other
approaches, of using actual rather than simulated data to inflate the minority
class. We tested our framework on two sets of Covid-related Twitter data and
obtained significant improvement in F1-measure on extremely imbalanced
scenarios, as compared to simple classical and deep-learning data generation
methods such as SMOTE, ADASYN, or GAN-based data generation.
