# Validation of Twitter opinion trends with national polling aggregates: Hillary Clinton vs Donald Trump

arXiv: https://arxiv.org/abs/1610.01587 · HTML: https://mlsanity.com/paper/1610.01587/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Oct 2016.

## Built on by (most cited first, 7 of 7)

- [DoubleH: Twitter User Stance Detection via Bipartite Graph Neural Networks](https://mlsanity.com/paper/2301.08774/) (2023)
- [Cognitive networks identify the content of English and Italian popular posts about COVID-19 vaccines: Anticipation, logistics, conspiracy and loss of trust](https://mlsanity.com/paper/2103.15909/) (2021)
- [On the efficacy of old features for the detection of new bots](https://mlsanity.com/paper/2506.19635/) (2025)
- [Correcting public opinion trends through Bayesian data assimilation](https://mlsanity.com/paper/2105.14276/) (2021)
- [Twitter Data Analysis: Izmir Earthquake Case](https://mlsanity.com/paper/2212.01453/) (2022)
- [Design and analysis of tweet-based election models for the 2021 Mexican legislative election](https://mlsanity.com/paper/2301.00626/) (2023)
- [Detecting Political Opinions in Tweets through Bipartite Graph Analysis: A Skip Aggregation Graph Convolution Approach](https://mlsanity.com/paper/2304.11367/) (2023)

## Abstract (arXiv)

Measuring and forecasting opinion trends from real-time social media is a long-standing goal of big-data analytics. Despite its importance, there has been no conclusive scientific evidence so far that social media activity can capture the opinion of the general population. Here we develop a method to infer the opinion of Twitter users regarding the candidates of the 2016 US Presidential Election by using a combination of statistical physics of complex networks and machine learning based on hashtags co-occurrence to develop an in-domain training set approaching 1 million tweets. We investigate the social networks formed by the interactions among millions of Twitter users and infer the support of each user to the presidential candidates. The resulting Twitter trends follow the New York Times National Polling Average, which represents an aggregate of hundreds of independent traditional polls, with remarkable accuracy. Moreover, the Twitter opinion trend precedes the aggregated NYT polls by 10 days, showing that Twitter can be an early signal of global opinion trends. Our analytics unleash the power of Twitter to uncover social trends from elections, brands to political movements, and at a fraction of the cost of national polls.