KCORE ANALYTICS – AI TO PREDICT HUMAN BEHAVIOR

Predicting Behavior
with AI

Kcore Analytics is an AI platform to predict elections and behavior

AI Predictions:

US 2016 Election
Argentina 2019 Election
US 2020 Election
Brazil 2022 Election
Brazil 2022 Gubernatorial Election - Cearà
Cyprus 2023 Election
Indian General Elections 2024

News Alter! Kcore has been awarded a SBIR Grant.

The Big Innovation

The big innovation is to use AI and social network structure to infer opinions, track trends and identify influencers from massive real-time online data.

Predict Elections Worldwide

We analyze real-time social media to give a quicker and remarkably accurate method of predicting election trends.

Identify And Target Influencers

We developed an end-to-end framework to find the Influencers in social networks like Twitter.

Forecast Market Trends

We are able to forecast global trends, from the markets to consumer products to social movements and revolutions.

About us

Kcore Approach

We have developed analytic tools combining statistical physics of complex networks, natural language processing and machine learning classification.

Artificial Intelligence
Human Intelligence
Big Data

K-core analytics has been awarded a $275k Small Business Innovation Research grant from the National Science foundation.

This Small Business Innovation Research (SBIR) Phase I project aims to predict global elections in real-time through the integration of artificial intelligence, network theory, and big data science. By harnessing the power of advanced machine learning models and analyzing vast amounts of publicly expressed opinions on social media, the team offers accurate forecasts of election outcomes. This approach has the potential to disrupt the conventional polling industry, which faces growing uncertainties and challenges such as declining response rates and inherent biases in sampling. Ultimately, we are able to poll global opinion on any topic of interest.

The research objectives entail tackling critical research and development challenges, including predicting voter turnout, effectively sampling rural areas with limited online coverage, filtering out bots and fake news sources, inferring the preferences of undecided voters, adjusting sample weights on a state-by-state basis, addressing the opinions of individuals not active on social media, and mitigating social desirability bias (where respondents conceal their intention to vote for controversial candidates). The anticipated technical results involve the development of a transformative machine learning architecture built upon Graph Neural Networks. The framework enables optimized resource allocation and significantly improves the precision of predictions. Ultimately, the results will empower decision-makers with reliable real-time information, facilitating informed choices, and enhancing the resilience of the democratic process.