The Effect of Similarity and Dissimilarity on Information Network Formation and Their Implications in Accurate Information Identification

In the context of the Bitcoin market and its virtual investment communities, this paper demonstrates that both similarity and dissimilarity in social media messages’ contents can be associated with high quotation probability among them. This finding advocates new understandings beyond the mainstream conclusion that network connections increase with similarity. It is also found that message networks offer significant implications to distinguish between accurate information and noise. Evidence shows that popular messages frequently quoted by others and redundant messages confirmed from different sources contain more accurate information for Bitcoin market predictions. Theoretical contributions and practical implications are discussed.