Before revision:  Our analysis unveils that males and females interact differently depending on the interaction types, e.g., likes or comments on Instagram. Inspired by the observations of gender disparity, we propose a novel seeding framework, namely Disparity seeding, which aims to maximize information spread while reaching a target user group, e.g., a certain percentage of females -- promoting the influence of under-represented groups. An extensive simulation comparison with target-agnostic algorithms shows that the proposed Disparity can disseminate information according to the disparity requirement while effectively maximizing the information spread.
After revision:  OAn extensive simulation comparison with target-agnostic algorithms shows that the proposed Disparity can disseminate information according to the disparity requirement while effectively maximizing the information spread.