System and means for generating synthetic social media data

Abstract

System and means generates synthetic forms of social media data such as data from microblogging services (e.g., Twitter) and social networking services (e.g., Facebook). This system and means jointly generate interaction graph structures and text features similar to input social media data. First, an interaction graph is generated by mapping social network interactions in input (real) social media data to graph structures. This interaction graph is fitted to a social network model (or a composite model) by minimizing the distance between the input and the synthetic interaction graphs (of potentially different sizes). The distance is measured statistically or based on the performance of social media analytics. Various patterns (such as anomalies), interaction types and temporal dynamics are generated synthetically. Second, text features are extracted from input social media data with topic modeling and statistical analysis of word tuple distributions. Based on these features, synthetic social media text is generated. Third, synthetic graph structures and text features are combined to generate the synthetic social media data. The system is particularly useful in generating data to be used for developing and testing new social media analytics or for generating or analyzing social bot network behavior and campaigns in social media, and for sharing test data with others without rate and privacy concerns.