The understanding of people’s communication structure is crucial to answering the ongoing debates about whether social media is a “filter bubble”. Focusing on this topic, our work investigated people’s preference for homogeneous or cross-cutting communications in an online activism setting, also the difference in language use and hyperlink use in different communications types. We used the tweets with #Silent Sam to classify communication types of user interactions. After the metric generation, significance tests, and subgraph mining, we found that people are 15 times more likely to communicate with like-minded people. However, cross-cutting communications increase simultaneously with homogeneous ones when the activity level rises. Also, homogeneous communications significantly use more words related to perception, while cross-cutting ones have more words about cognition. We also unveiled the dark side of the crosscutting communications as they are generally more toxic and aggressive. The use of outside links in the tweets is rare for both cross-cutting and homogeneous communications. Nonetheless, the cross-cutting tweets embed more URLs while they direct to less diverse domains than the homogeneous ones. Left-leaning media sources with mixed to high factuality are linked as the outside source for mostly homogeneous interactions. Our work made contributions in the ways of providing the new methodology of subgraph mining to research about partisan sharing and rendering new insights to the research in online activism.Master of Science in Information Scienc
