Revolutionizing Election Forecasting with AI Technology in NYC

In a game-changing twist to political forecasting, Socialprofiler has unveiled how its advanced AI technology accurately predicted Zohran Mamdani's victory in the New York City mayoral race weeks before the election. On November 20, 2025, Socialprofiler shared the results of its cutting-edge analysis, arguing that traditional polling methods, which often rely on self-reported opinions, are inadequate in capturing the true pulse of voter behavior. Instead, the company focused on behavioral social data, analyzing the interests and interactions of millions of New Yorkers on popular platforms such as Facebook, Instagram, X, and TikTok.

The results were staggering; by mapping what users liked and followed in the weeks leading up to the November 4 election, the AI accurately captured the city's political dynamics. In an era where public opinion polls often miss the mark, this new approach shows promise in creating a more accurate representation of voter sentiment.

Anthony Noskov, the Founder and CEO of Socialprofiler, emphasized this shift in methodology, stating, "For decades, polls have asked people what they think, and those results have consistently missed the mark. By analyzing what people actually do online, we're ushering in a new era of election forecasting. This approach eliminates self-report bias and captures real engagement patterns, giving a far truer picture of voter behavior weeks—or even months—before ballots are cast."

To ensure fairness in their analysis, Socialprofiler examined equally-sized groups of followers for each candidate. This step was crucial to prevent skewed results stemming from the differences in audience sizes. The data revealed a divided electorate—digital echo chambers with very few mixed-preference voters—underscoring the modern reality of elections which hinge more on mobilizing well-defined support clusters than on persuading undecided voters.

In the case of the NYC mayoral race, Mamdani's strong engagement in significant blue clusters reflected his eventual win. Conversely, the limited digital presence of his Republican opponent, Curtis Sliwa, highlighted his constraints from the outset. Notably, the analysis illuminated a surprising trend regarding former Governor Andrew Cuomo's support. Although Cuomo's followers had about half as many political interests as Mamdani's, their engagement levels matched. This indicates that his following consisted of low-interest voters who rarely appear in conventional digital analyses but played a substantial role in his strong second-place performance.

Socialprofiler's findings suggest that the gray zone of voter interest—typically perceived as centrists—is, in fact, a more extensive population of less-engaged users who can be ignited into action through new connections rather than classic persuasion strategies. "Elections are just the beginning," Noskov asserted. "The same behavioral mapping that can predict a mayoral race can also transform how companies understand customers, how brands anticipate trends, and how researchers measure culture itself. We are not just reinventing political analysis; we are reinventing market research."

The implications of this novel approach extend far beyond politics, offering a faster and more accurate alternative to traditional market research. By prioritizing actual online behaviors over self-reported beliefs, Socialprofiler positions itself as a leader in transforming how media outlets, marketers, and policymakers can make informed decisions.

For those interested in a more in-depth look at Socialprofiler's pre-election analysis pertaining to the New York City mayoral race, detailed reports are available upon request. The future of election forecasting—and market research—seems poised for exciting developments in the wake of such groundbreaking technology.