  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.
