A Semi-Supervised Framework for Misinformation Detection
This addresses the problem of detecting misinformation in social media for researchers and practitioners, but it is incremental as it builds on existing semi-supervised and data generation techniques.
The paper tackled misinformation detection on social media by proposing a semi-supervised framework to handle extreme class imbalances, achieving significant F1-measure improvements over methods like SMOTE, ADASYN, and GAN-based generation on Covid-related Twitter data.
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.