Determining Content Classifications Using Feature Frequency

The present technology is directed to systems and methods for determining content of interest by determining the occurrence of fast-rising features (for example, URL, fingerprint, etc.) in posts or comments provided by users within an online community, for example, a social network. The systems and methods obtain user-generated content from an online community and determine signals based on the user-generated content to calculate a frequency of the signals as they post or occur in a pre-defined time bucket or window. A first derivative difference of the frequency and a second derivative difference of the frequency is calculated based on formulating consecutive time buckets or “dynamic windows” to determine if one or more of the frequency, the first derivative difference, and the second derivative difference satisfies one or more pre-defined thresholds. The user-generated content is prioritized based on whether it satisfies the one or more pre-defined thresholds.