论文标题
众包音乐知识策划中的专业知识和动态：天才平台的案例研究
Expertise and Dynamics within Crowdsourced Musical Knowledge Curation: A Case Study of the Genius Platform
论文作者
论文摘要
许多平台主要从志愿者那里收集众包信息。随着这种类型的知识策展变得普遍，贡献格式差异很大，并且由不同平台之间的各种过程驱动。因此，一个平台的模型不一定适用于其他平台。在这里，我们研究了Genius的时间动态，该平台主要是为歌曲歌词的用户限制注释而设计的。天才的一个独特方面是，注释非常本地化 - 带注释的歌词可能只是一首歌的几行 - 但也有很高的相关性，例如歌曲，专辑，艺术家或类型。我们分析了与抒情注释及其编辑相关的几个动力学过程，这些过程与其他平台的模型有很大不同。例如，关于歌曲注释的专业知识遵循“ U Shape”，其中专家是不熟悉的早期和晚期贡献者，并立即贡献；我们开发了一种捕获此类行为的用户公用事业模型。我们还发现，在用户的寿命初期出现了一些贡献特征，这些贡献将（最终）专家与非专家区分开来。结合我们的发现，我们开发了一个模型，用于早期预测用户专业知识。
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.
