Addressing Event-Driven Concept Drift in Twitter Stream: A Stance Detection Application
Addressing Event-Driven Concept Drift in Twitter Stream: A Stance Detection Application
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DOI：
10.1109/access.2021.3083578
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发表时间：
2021-01-01
期刊：
影响因子：
3.9
通讯作者：
Renda, Alessandro
中科院分区：
文献类型：
--
作者：
Bechini, Alessio;Bondielli, Alessandro;Renda, Alessandro
用户在社交网络上发布的内容是“社交感知”广泛领域中众多应用的重要信息来源。 Twitter 平台尤其承载以推文形式表达的用户的想法、意见和评论：因此，推文通常使用文本挖掘和自然语言处理技术进行分析，以执行相关任务，从品牌声誉和情绪分析到立场检测。在大多数情况下，旨在完成这些任务的智能系统基于分类模型，该模型经过训练后，将部署到数据流中以进行在线监控。在这项工作中，我们展示了这种方法如何不足以完成从推文中进行立场检测的任务。事实上，每天收集的推文序列代表了一个数据流。正如数据流挖掘文献中众所周知的那样，分类模型可能会受到概念漂移的影响，即数据分布的变化可能会降低性能。我们提出了一项广泛的实验活动，用于在线监测 Twitter 上表达的意大利疫苗接种主题立场的案例研究。我们比较了不同的学习方案，并提出了一种新颖的方案，旨在解决事件驱动的概念漂移问题。
The content posted by users on Social Networks represents an important source of information for a myriad of applications in the wide field known as 'social sensing'. The Twitter platform in particular hosts the thoughts, opinions and comments of its users, expressed in the form of tweets: as a consequence, tweets are often analyzed with text mining and natural language processing techniques for relevant tasks, ranging from brand reputation and sentiment analysis to stance detection. In most cases the intelligent systems designed to accomplish these tasks are based on a classification model that, once trained, is deployed into the data flow for online monitoring. In this work we show how this approach turns out to be inadequate for the task of stance detection from tweets. In fact, the sequence of tweets that are collected everyday represents a data stream. As it is well known in the literature on data stream mining, classification models may suffer from concept drift, i.e. a change in the data distribution can potentially degrade the performance. We present a broad experimental campaign for the case study of the online monitoring of the stance expressed on Twitter about the vaccination topic in Italy. We compare different learning schemes and propose yet a novel one, aimed at addressing the event-driven concept drift.