We study a platform-agnostic method of using available activity by coordinated influence operations on social media to detect and assess their ongoing activities. Our approach classifies the post-URL pair based on human-interpretable features without relying on user-level behavioral data. We test on data from all publicly avail-able Twitter datasets of Chinese, Russian, and Venezuelan troll activity targeting the United States from late-2015 through 2019, and Reddit dataset of Russian influence effort during 2015 and 2016. Instead of following the conventional approach to train a classifier based on the entire dataset, we train classifiers on a monthly basis across each campaign to capture how changes in trolls activities impact the performance of our classifier over time. Prediction per-formances vary by month, country, platform, and experimental design, ranging from average F1 score of 0.75 to 0.94 , and is robust to 1% false negative and false pos-itive rates. Additional diagnostics test and policy implications and challenges will be discussed. In this project, advised by Professor Jacob Shapiro, Meysam is using publicly available verified data sets of foreign online influence operations to train classifiers that can identify suspicious activities on social media.