Abstract
Online social networks support users in a wide range of activities, such as sharing information and making recommendations. In Twitter, the hashtag #ff, or #followfriday, arose as a popular convention for users to create contact recommendations for others. Hitherto, there has not been any quantitative study of the effect of such human-generated recommendations. This paper is the first study of a large-scale corpus of human friendship recommendations based on such hashtags, using a large corpus of recommendations gathered over a 24 week period and involving a set of nearly 6 million users. We show that these explicit recommendations have a measurable effect on the process of link creation, increasing the chance of link creation between two and three times on average, compared with a recommendation-free scenario. Also, ties created after such recommendations have up to 6% more longevity than other Twitter ties. Finally, we build a supervised system to rank user-generated recommendations, surfacing the most valuable ones with high precision (0.52 MAP), and we find that features describing users and the relationships between them, are discriminative for this task.
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Gavilanes, R.G., O’Hare, N., Aiello, L.M., Jaimes, A. (2013). Follow My Friends This Friday! An Analysis of Human-Generated Friendship Recommendations. In: Jatowt, A., et al. Social Informatics. SocInfo 2013. Lecture Notes in Computer Science, vol 8238. Springer, Cham. https://doi.org/10.1007/978-3-319-03260-3_5
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DOI: https://doi.org/10.1007/978-3-319-03260-3_5
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-03259-7
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