Social network users publicly share a wide variety of information with their followers and the general public ranging from their opinions, sentiments and personal life activities. There has already been significant advance in analyzing the shared information from both micro (individual user) and macro (community level) perspectives, giving access to actionable insight about user and community behaviors. The identification of personal life events from user’s profiles is a challenging yet important task, which if done appropriately, would facilitate more accurate identification of users’ preferences, interests and attitudes. For instance, a user who has just broken his phone , is likely to be upset and also be looking to purchase a new phone. While there is work that identifies tweets that include mentions of personal life events, our work in this paper goes beyond the state of the art by predicting a future personal life event that a user will be posting about on Twitter solely based on the past tweets. We propose two architectures based on recurrent neural networks , namely the classification and generation architectures, that determine the future personal life event of a user. We evaluate our work based on a gold standard Twitter life event dataset and compare our work with the state of the art baseline technique for life event detection. While presenting performance measures, we also discuss the limitations of our work in this paper.

中文翻译：

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通过循环神经网络预测 Twitter 上未来的个人生活事件

社交网络用户与他们的追随者和公众公开分享各种各样的信息，包括他们的观点、情感和个人生活活动。在从微观（个人用户）和宏观（社区级别）角度分析共享信息方面已经取得了重大进展，可以获取有关用户和社区行为的可操作见解。从用户的个人资料中识别个人生活事件是一项具有挑战性但重要的任务，如果做得恰当，将有助于更准确地识别用户的偏好、兴趣和态度。例如，一个刚刚摔坏了手机的用户可能会心烦意乱，并希望购买一部新手机。虽然有工作可以识别包含提及个人生活事件的推文，我们在本文中的工作超越了最先进的技术，仅根据过去的推文预测用户将在 Twitter 上发布的未来个人生活事件。我们提出了两种基于循环神经网络的架构，即分类和生成架构，它们决定了用户未来的个人生活事件。我们根据黄金标准 Twitter 生活事件数据集评估我们的工作，并将我们的工作与最先进的生活事件检测基线技术进行比较。在介绍性能指标的同时，我们还讨论了本文工作的局限性。即分类和生成架构，它们决定了用户未来的个人生活事件。我们根据黄金标准 Twitter 生活事件数据集评估我们的工作，并将我们的工作与最先进的生活事件检测基线技术进行比较。在介绍性能指标的同时，我们还讨论了本文工作的局限性。即分类和生成架构，它们决定了用户未来的个人生活事件。我们根据黄金标准 Twitter 生活事件数据集评估我们的工作，并将我们的工作与最先进的生活事件检测基线技术进行比较。在介绍性能指标的同时，我们还讨论了本文工作的局限性。