Fast Few shot Self-attentive Semi-supervised Political Inclination Prediction
This addresses the need for low-cost, controllable political surveys for policymakers and journalists, though it appears incremental as it builds on existing semi-supervised and attention-based methods.
The paper tackles the problem of predicting political inclination from social media data by introducing a self-attentive semi-supervised framework that requires minimal annotated data, achieving 93.7% accuracy with no annotations and competitive performance with few examples per class.
With the rising participation of the common mass in social media, it is increasingly common now for policymakers/journalists to create online polls on social media to understand the political leanings of people in specific locations. The caveat here is that only influential people can make such an online polling and reach out at a mass scale. Further, in such cases, the distribution of voters is not controllable and may be, in fact, biased. On the other hand,if we can interpret the publicly available data over social media to probe the political inclination of users, we will be able to have controllable insights about the survey population, keep the cost of survey low and also collect publicly available data without involving the concerned persons. Hence we introduce a self-attentive semi-supervised framework for political inclination detection to further that objective. The advantage of our model is that it neither needs huge training data nor does it need to store social network parameters. Nevertheless, it achieves an accuracy of 93.7\% with no annotated data; further, with only a few annotated examples per class it achieves competitive performance. We found that the model is highly efficient even in resource-constrained settings, and insights drawn from its predictions match the manual survey outcomes when applied to diverse real-life scenarios.