Making online polls more accurate: statistical methods explained - Alberto Arletti; Maria Letizia Tanturri; Omar Paccagnella (Frontiers in Political Science, 2025)
Online data has the potential to transform how researchers and companies produce election forecasts. Social media surveys, online panels, and even comments scraped from the internet can offer valuable insights into political preferences. However, such data is often affected by significant selection bias, as online respondents may not be representative of the overall population. At the same time, traditional data collection methods are becoming increasingly cost-prohibitive. In this scenario, scientists need instruments to be able to draw the most accurate estimate possible from samples drawn online. This paper provides an introduction to key statistical methods for mitigating bias and improving inference in such cases, with a focus on electoral polling. Specifically, it presents the main statistical techniques, categorized into weighting, modeling, and other approaches. It also offers practical recommendations for drawing estimates with measures of uncertainty. Designed for both researchers and industry practitioners, this introduction takes a hands-on approach, with code available for implementing the main methods.