Fast Few shot Self-attentive Semi-supervised Political Inclination Prediction

• URL: http://arxiv.org/abs/2209.10292v2
• Date: Fri, 23 Sep 2022 01:28:16 GMT
• Title: Fast Few shot Self-attentive Semi-supervised Political Inclination Prediction
• Authors: Souvic Chakraborty, Pawan Goyal, Animesh Mukherjee
• Abstract summary: 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. We introduce a self-attentive semi-supervised framework for political inclination detection to further that objective. We found that the model is highly efficient even in resource-constrained settings.
• Score: 12.472629584751509
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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