How Similar Are Elected Politicians and Their Constituents? Quantitative Evidence From Online Social Networks

TL;DR

The paper investigates whether elected politicians resemble their constituents by comparing the online discourse of politicians on Twitter with constituents on Nextdoor across the USA and the UK. It builds a constituency-level dataset from 5.6 million tweets and 21.8 million Nextdoor posts, using mean-pooled embeddings, BERTopic topics, LIWC metrics, and VADER sentiment to quantify content and style. Findings show modest content and style similarity within constituencies, with ideology having limited effect; style similarity increases when the electoral margin is narrow, while content similarity rises with larger margins, and poorer constituencies exhibit higher content and sentiment similarity. These results illuminate democratic representation in the digital age and provide a scalable cross-country framework for analyzing alignment between voters and their representatives through online discourse.

Abstract

How similar are politicians to those who vote for them? This is a critical question at the heart of democratic representation and particularly relevant at times when political dissatisfaction and populism are on the rise. To answer this question we compare the online discourse of elected politicians and their constituents. We collect a two and a half years (September 2020 - February 2023) constituency-level dataset for USA and UK that includes: (i) the Twitter timelines (5.6 Million tweets) of elected political representatives (595 UK Members of Parliament and 433 USA Representatives), (ii) the Nextdoor posts (21.8 Million posts) of the constituency (98.4% USA and 91.5% UK constituencies). We find that elected politicians tend to be equally similar to their constituents in terms of content and style regardless of whether a constituency elects a right or left-wing politician. The size of the electoral victory and the level of income of a constituency shows a nuanced picture. The narrower the electoral victory, the more similar the style and the more dissimilar the content is. The lower the income of a constituency, the more similar the content is. In terms of style, poorer constituencies tend to have a more similar sentiment and more dissimilar psychological text traits (i.e. measured with LIWC categories).

Figures (11)

• Figure 1: Cumulative distribution of Nextdoor and Twitter data across constituencies in USA and UK.
• Figure 2: Distribution of posts/tweets by topics over Nextdoor and Twitter.
• Figure 3: Distribution of cosine similarity of mean-pooled textual embeddings between constituents and elected politicians over deciles of winning vote majority in different constituencies (from higher to lower).
• Figure 4: Distribution of cosine similarity of mean-pooled textual embeddings between Nextdoor and Twitter data over deciles by income levels in different constituencies (from richest to poorest).
• Figure 5: Distribution of differences of top five LIWC categories score of online discourse between constituents and elected politicians (Scale:0-1)