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).

Nextdoor is introducing its first generative AI feature, an in-app "assistant" that can help users rewrite "potentially unkind" posts on the neighborhood social network. The new feature is rolling out "over the next several weeks." It's far from the first time the company has experimented with ways to remind users to keep conversations "neighborly." The company, which has at time struggled to fight the perception that its platform can be toxic, began using "kindness reminders" in 2019 and last year introduced pop-ups reminding users to be more "empathetic." The app has also served up more targeted nudges to promote anti-racist language and less heated political discussions.

From health to education, income impacts a huge range of life choices. Earlier research has leveraged data from online social networks to study precisely this impact. In this paper, we ask the opposite question: do different levels of income result in different online behaviors? We demonstrate it does. We present the first large-scale study of Nextdoor, a popular location-based social network. We collect 2.6 Million posts from 64,283 neighborhoods in the United States and 3,325 neighborhoods in the United Kingdom, to examine whether online discourse reflects the income and income inequality of a neighborhood. We show that posts from neighborhoods with different incomes indeed differ, e.g. richer neighborhoods have a more positive sentiment and discuss crimes more, even though their actual crime rates are much lower. We then show that user-generated content can predict both income and inequality. We train multiple machine learning models and predict both income (R-squared=0.841) and inequality (R-squared=0.77).

Nextdoor is where you connect to the neighborhoods that matter to you so you can belong. Our purpose is to cultivate a kinder world where everyone has a neighborhood they can rely on. Neighbors around the world turn to Nextdoor daily to receive trusted information, give and get help, get things done, and build real-world connections with those nearby -- neighbors, businesses, and public services. Today, neighbors rely on Nextdoor in more than 305,000 neighborhoods across 11 countries. At Nextdoor, machine learning is one of the most important teams we are growing.

Nextdoor is where you connect to the neighborhoods that matter to you so you can belong. Our purpose is to cultivate a kinder world where everyone has a neighborhood they can rely on. Neighbors around the world turn to Nextdoor daily to receive trusted information, give and get help, get things done, and build real-world connections with those nearby -- neighbors, businesses, and public services. Today, neighbors rely on Nextdoor in more than 300,000 neighborhoods across 11 countries. As Data Scientists at Nextdoor, we design and oversee product experiments, own complex analyses to drive company and product strategy, and deploy online and offline models to a production environment.

Nextdoor is where you connect to the neighborhoods that matter to you so you can belong. Our purpose is to cultivate a kinder world where everyone has a neighborhood they can rely on. Neighbors around the world turn to Nextdoor daily to receive trusted information, give and get help, get things done, and build real-world connections with those nearby -- neighbors, businesses, and public services. Today, neighbors rely on Nextdoor in more than 300,000 neighborhoods across 11 countries. As a Data Scientist with Nextdoor, Inc. (San Francisco, CA) (100% Telecommuting permitted) you'll: Compensation, benefits, perks, and recognition programs at Nextdoor come together to create one overall rewards package.

Nextdoor is where you connect to the neighborhoods that matter to you so you can belong. Our purpose is to cultivate a kinder world where everyone has a neighborhood they can rely on. Neighbors around the world turn to Nextdoor daily to receive trusted information, give and get help, get things done, and build real-world connections with those nearby -- neighbors, businesses, and public services. Today, neighbors rely on Nextdoor in more than 295,000 neighborhoods across 11 countries. As a Data Scientist at Nextdoor, you will be responsible for design and oversight of product experiments, own complex analysis to drive company and product strategy, and deploy online and offline models to a production environment.

Nextdoor is where you connect to the neighborhoods that matter to you so you can belong. Our purpose is to cultivate a kinder world where everyone has a neighborhood they can rely on. Neighbors around the world turn to Nextdoor daily to receive trusted information, give and get help, get things done, and build real-world connections with those nearby -- neighbors, businesses, and public services. Today, neighbors rely on Nextdoor in more than 295,000 neighborhoods across 11 countries. At Nextdoor, Machine Learning is one of the most important teams we are growing.

Nextdoor is where you connect to the neighborhoods that matter to you so you can belong. Our purpose is to cultivate a kinder world where everyone has a neighborhood they can rely on. Neighbors around the world turn to Nextdoor daily to receive trusted information, give and get help, get things done, and build real-world connections with those nearby -- neighbors, businesses, and public services. Today, neighbors rely on Nextdoor in more than 295,000 neighborhoods across 11 countries. At Nextdoor, we empower our employees to build stronger local communities.

In an interview with NPR, Friar outlined steps the popular neighborhood app is planning to take to address reports of racial profiling and censorship on the platform. In an interview with NPR, Friar outlined steps the popular neighborhood app is planning to take to address reports of racial profiling and censorship on the platform. As protests swept the nation following the police killing of George Floyd, there was a surge of reports that Nextdoor, the hyperlocal social media app, was censoring posts about Black Lives Matter and racial injustice. In an interview with NPR, Nextdoor CEO Sarah Friar said the company should have moved more quickly to protect posts related to Black Lives Matter by providing clearer guidance. It "was really our fault" that moderators on forums across the country were deleting those posts, she said. People of color have long accused Nextdoor, which serves as a community bulletin board in more than 265,000 neighborhoods across the U.S., of doing nothing about users' racist comments and complaints.