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

arXiv: https://arxiv.org/abs/2209.10292 · HTML: https://mlsanity.com/paper/2209.10292/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 27 references that are in the map.
- First posted on arXiv 21 Sep 2022.

## Builds on (most cited first, 15 of 15)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Universal Sentence Encoder](https://mlsanity.com/paper/1803.11175/) (2018)
- [BERTweet: A pre-trained language model for English Tweets](https://mlsanity.com/paper/2005.10200/) (2020)
- [Latent Hatred: A Benchmark for Understanding Implicit Hate Speech](https://mlsanity.com/paper/2109.05322/) (2021)
- [Unsupervised User Stance Detection on Twitter](https://mlsanity.com/paper/1904.02000/) (2019)
- [What Was Written vs. Who Read It: News Media Profiling Using Text Analysis and Social Media Context](https://mlsanity.com/paper/2005.04518/) (2020)
- [TIMME: Twitter Ideology-detection via Multi-task Multi-relational Embedding](https://mlsanity.com/paper/2006.01321/) (2020)
- [Learning Implicit Sentiment in Aspect-based Sentiment Analysis with Supervised Contrastive Pre-Training](https://mlsanity.com/paper/2111.02194/) (2021)
- [Predicting Race and Ethnicity From the Sequence of Characters in a Name](https://mlsanity.com/paper/1805.02109/) (2018)
- [Aspect-based Sentiment Analysis of Scientific Reviews](https://mlsanity.com/paper/2006.03257/) (2020)
- [Your Stance is Exposed! Analysing Possible Factors for Stance Detection on Social Media](https://mlsanity.com/paper/1908.03146/) (2019)
- [CRUSH: Contextually Regularized and User anchored Self-supervised Hate speech Detection](https://mlsanity.com/paper/2204.06389/) (2022)
- [(Im)balance in the Representation of News? An Extensive Study on a Decade Long Dataset from India](https://mlsanity.com/paper/2110.14183/) (2021)
- [Decoding Demographic un-fairness from Indian Names](https://mlsanity.com/paper/2209.03089/) (2022)

## Built on by (most cited first, 2 of 2)

- [Personality Detection and Analysis using Twitter Data](https://mlsanity.com/paper/2309.05497/) (2023)
- [Modeling Political Orientation of Social Media Posts: An Extended Analysis](https://mlsanity.com/paper/2311.12323/) (2023)

## Abstract (arXiv)

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.