Purpose

This study aims to predict popular contributors through text representations of user-generated content in open crowds.

Design/methodology/approach

Three text representation approaches – count vector, Tf-Idf vector, word embedding and supervised machine learning techniques – are used to generate popular contributor predictions.

Findings

The results of the experiments demonstrate that popular contributor predictions are considered successful. The F1 scores are all higher than the baseline model. Popular contributors in open crowds can be predicted through user-generated content.

Research limitations/implications

This research presents brand new empirical evidence drawn from text representations of user-generated content that reveals why some contributors' ideas are more viral than others in open crowds.

Practical implications

This research suggests that companies can learn from popular contributors in ways that help them improve customer agility and better satisfy customers' needs. In addition to boosting customer engagement and triggering discussion, popular contributors' ideas provide insights into the latest trends and customer preferences. The results of this study will benefit marketing strategy, new product development, customer agility and management of information systems.

Originality/value

The paper provides new empirical evidence for popular contributor prediction in an innovation crowd through text representation approaches.

中文翻译：

---

预测创新人群中受欢迎的贡献者：My Starbucks Ideas 案例

目的

本研究旨在通过公开人群中用户生成内容的文本表示来预测受欢迎的贡献者。

设计/方法/方法

三种文本表示方法——计数向量、Tf-Idf 向量、词嵌入和监督机器学习技术——用于生成流行的贡献者预测。

发现

实验结果表明，流行的贡献者预测被认为是成功的。F 1分数均高于基线模型。可以通过用户生成的内容来预测公开人群中的热门贡献者。

研究限制/影响

这项研究提供了从用户生成内容的文本表示中提取的全新经验证据，揭示了为什么一些贡献者的想法在公开人群中比其他人更具病毒性。

实际影响

这项研究表明，公司可以向受欢迎的贡献者学习，帮助他们提高客户敏捷性并更好地满足客户的需求。除了提高客户参与度和引发讨论之外，受欢迎的贡献者的想法还提供了对最新趋势和客户偏好的洞察。这项研究的结果将有利于营销策略、新产品开发、客户敏捷性和信息系统管理。

原创性/价值

该论文通过文本表示方法为创新人群中的流行贡献者预测提供了新的经验证据。