Many platforms collect crowdsourced information primarily from volunteers. As this type of knowledge curation has become widespread, contribution formats vary substantially and are driven by diverse processes across differing platforms. Thus, models for one platform are not necessarily applicable to others. Here, we study the temporal dynamics of Genius, a platform primarily designed for user-contributed annotations of song lyrics. A unique aspect of Genius is that the annotations are extremely local -- an annotated lyric may just be a few lines of a song -- but also highly related, e.g., by song, album, artist, or genre. We analyze several dynamical processes associated with lyric annotations and their edits, which differ substantially from models for other platforms. For example, expertise on song annotations follows a "U shape" where experts are both early and late contributors with non-experts contributing intermediately; we develop a user utility model that captures such behavior. We also find several contribution traits appearing early in a user's lifespan of contributions that distinguish (eventual) experts from non-experts. Combining our findings, we develop a model for early prediction of user expertise.
翻译：许多平台主要收集来自志愿者的多方源信息。随着这种类型的知识整理变得非常广泛,贡献格式也大相径庭,并且由不同平台的不同过程驱动。 因此, 一个平台的模型不一定适用于其他平台。 在这里, 我们研究Genius的时间动态, 这个平台主要为用户提供的歌曲歌词说明设计。 Genius 的一个独特方面是, 注释非常本地 -- 附加说明的词句可能只是一首歌曲的几行 -- 但也非常相关, 例如, 通过歌曲、专辑、艺术家或流派。 我们分析了几个与文理说明及其编辑相关的动态过程, 这些过程与其他平台的模型大不相同。 例如, 歌曲说明方面的专业知识遵循一种“ U 形状 ”, 即专家是早期和晚期贡献者, 由非专家提供中间贡献者; 我们开发了一个用户实用模型, 捕捉到这种行为。 我们还发现一些贡献特征, 早期出现在用户的寿命中, 将专家与非专家区分( 动态) 。 结合我们的发现, 我们开发了一个早期预测用户专业知识的模型。
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