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Right, this sentence made it unclear to me whether they only used the popularity features to compute past-performance, or whether they included past-performance in addition to other popularity features. To your question, other work on success prediction of tweets [1, 2] demonstrates that past-performance is indeed much more predictive than the typical content features. This way of looking at success of "cultural products" assumes it depends to varying extents on both inherent "quality" (measured by content features), and the social processes of sharing (which are much harder to understand ahead of time, as the paper I referenced in my parent post shows). [1] Martin, T., Hofman, J. M., Sharma, A., Anderson, A., & Watts, D. J. (2016). Exploring Limits to Prediction in Complex Social Systems. Proceedings of the 25th International Conference on World Wide Web - WWW ’16, 683–694. https://doi.org/10.1145/2872427.2883001 [2] Bakshy, E., Hofman, J. M., Mason, W. A., & Watts, D. J. (2011). Everyone’s an Influencer: Quantifying Influence on Twitter. Proceedings of the Fourth ACM International Conference on Web Search and Data Mining - WSDM ’11, 65. https://doi.org/10.1145/1935826.1935845 |