📑Technical Summary

Uncovering the True Impact of KOL Posts on Crypto Prices with Causal Inference At Cryptify AI, we utilize causal inference algorithms to determine the true impact of Key Opinion Leader (KOL) posts on social media on cryptocurrency token prices. Traditional correlation-based methods often struggle to discern whether KOL posts genuinely drive price changes or merely coincide with them. To solve this challenge, Cryptify AI applies advanced causal inference techniques that isolate the effect of these posts from confounding factors like general market trends or major news events.

Our approach pinpoints key moments when KOLs make posts about a specific token and tracks price movements immediately before and after these posts. The central challenge is to estimate what would have happened to the token's price had the post not occurred—essentially constructing a counterfactual scenario. Since we cannot control the timing of these posts or create a formal control group, we use a combination of time-series modeling and counterfactual prediction techniques to assess this effect.

Analyzing KOL Impact with Time Windows and EWMA to Track Token Price Movements Each KOL post is treated as an event, analyzed using varying time windows (e.g., 10, 30, or 60 minutes before and after the post). This helps us assess the impact over different time frames and understand how the effect evolves. We calculate the exponentially weighted moving average (EWMA) of the token's price before and after each event, using the ratio between these two to quantify price movement. More weight is assigned to data points closer to the event, capturing the immediate response while minimizing the influence of older data.

Estimating KOL Post Impact with Counterfactual Analysis and Bayesian Modeling We estimate what the price movement would have been if the post had not been made by forecasting a counterfactual price. The difference between the actual price change and the predicted counterfactual price represents the estimated causal impact of the KOL post. Additionally, we conduct similar analyses on other features like trading volume and volatility, offering a more comprehensive understanding of the impact. Bayesian models are employed to estimate uncertainty and provide credible intervals for the effect size.

Based on this analysis, we created a normalized score—the CRAI score—to quantify the impact of each post. We aggregate this score for each KOL to evaluate their average impact per post, and assess it over different periods, such as monthly, to understand consistency and overall influence. Furthermore, we aggregate impact scores per token to gauge how much of the token's value movements (e.g., price changes) can be attributed to social media posts.

These findings differentiate between mere hype and genuine market impact, providing insights into how influential KOLs are in affecting token prices. We evaluate effects on trading volume, holder count, relative price changes, and market cap, giving traders, investors, and analysts an evidence-based perspective on how social media narratives shape crypto markets—beyond mere intuition or superficial correlations.