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More than three-quarters of new Democrats in Congress pledged to cut costs in their first messages after the election.

Affordability will be Democrats’ top concern, according to a Washington Post analysis of social media posts and victory speeches from 274 Democrats who won their elections last week.

“Life is way too expensive there,” Rep. Kathy Castor (D-Fla.) said in a victory speech posted online.

Even other topics, like alleged corruption within the Trump administration, were often tied to affordability.

“This administration’s policies have increased costs for millions of Americans, deprived our people of health care, food assistance and other essential services, and enriched the president and his family at the expense of taxpayers. » tweeted Rep. Deborah Ross (D-North Carolina), who was re-elected to the House.

But two-thirds of Democrats’ messages promising affordability don’t specify how they plan to reduce costs. Of the 33 percent who did, most mentioned the end of the war in Iran and tariffs.

“Our agenda: Stop this war. End trade wars. Reverse health care cuts,” Rep. Jason Crow (D-Colorado) wrote on TikTok, saying, “There are actually three very specific things that this administration has done, that Donald Trump has done, that have blown a hole in people’s budgets.”

Immigration controls and data centers fueled voter anger, but relatively few Democrats mentioned the need to rein them in. And only a tiny fraction mentioned guns, the climate or the war in Gaza – all potentially divisive issues in a larger-than-expected Democratic coalition.

Methodology: The Washington Post analyzed 2,381 social media posts and podcasts produced between the evening of Tuesday, November 3 and Monday, November 9, from all congressional Democrats who won their elections.

The Post identified the agenda statements as explicit promises, plans and lists of issues. Twenty-five members of Congress have made no public statements about the agenda. We have categorized these promises into 14 thematic areas. An AI agenda statement classifier had a 94% accuracy rate; the topic classifier had an accuracy rate of 85%; both used GPT-6 Luna.