Abstract
We perform a first-of-its-kind characterization of topical homophily - familiarity co-occurring with topic-participation similarity of user pairs - by correlating topic participation similarity and degree of familiarity of users on Twitter. We quantify similarity between a user pair by measuring their distribution of participation in topics, wherein topics are defined as clusters of hashtags formed using semantically related user-generated content. We examine the topic participation similarity of users against different degrees of familiarity: edges, shared neighbors, and structural communities. We provide varying relaxation in identifying topics, and characterize the correlation of topical similarity with the degree of familiarity over the range of relaxation. We empirically substantiate the characteristics of topical homophily, over the varying relaxation of identified topics. We empirically show that homophily grows linearly with increase of familiarity, reaches a peak, and subsequently falls, indicating that, familiarity correlates with similarity up to a point, beyond which, similarity occurs for other reasons.
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Dey, K., Shrivastava, R., Kaushik, S., Garg, K. (2019). Assessing Topical Homophily on Twitter. In: Aiello, L., Cherifi, C., Cherifi, H., Lambiotte, R., Lió, P., Rocha, L. (eds) Complex Networks and Their Applications VII. COMPLEX NETWORKS 2018. Studies in Computational Intelligence, vol 813. Springer, Cham. https://doi.org/10.1007/978-3-030-05414-4_29
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DOI: https://doi.org/10.1007/978-3-030-05414-4_29
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