Online social media like Twitter, Facebook, and Gab is often used as the stage to deliver one's opinion for a particular group of people, a political party, etc. Sometimes, the opinions shared are considered as controversial by some audience, applauded by some, or disagreed by some in the form of comments, sharing, likes, or dislikes. The information about shared opinion and the reaction to it in the form of positive, or negative reaction forms an interaction, and a collection of many such interactions forms a signed network. In addition, the evolution of information on social networks strongly relies on the nature of interactions between the users. The study of interactions is, therefore, crucial to predict the extent and nature of information spread. In this work, we study the relationship between users whether they agree or disagree in the dynamic evolution of interactions (cascades) on a larger network, Gab, to predict the relationship between the users on the social network. We quantitatively use the combination of text information and network information to enhance state of the art deep learning models for contradiction detection. The outcome of this research might contribute to improving link prediction.