Towards Predicting Socio-economic Systems by Mining Social Media Data

通过挖掘社交媒体数据来预测社会经济系统

• 批准号: RGPIN-2014-06591
• 负责人: Makrehchi, Masoud
• 金额: $ 1.09万
• 依托单位: University of Ontario Institute of Technology
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=658553
• 关键词: Towards; Predicting; Socio; economic; Systems

Social media data is defined as any data generated either in a social context or during a social interaction. Large amount of data is generated everyday by social media users. For example, one billion tweets are twitted every three days. This data is a valuable source to understand social trends, sentiments, opinions, and intentions. The objective in the proposed research is to study how to build models for mining social media data to understand major social trends and patterns and design effective tools to predict complex socio-economic systems. Similar to other data mining problems, many tasks can be implemented under social data mining umbrella including classification, clustering, recommendation, and prediction. Specific examples include classifying social media collaborators and users based on their sentiments toward a product, topic, or content, social role prediction in social networks, extracting transaction network from social network, classifying the type of relationship in social networks (for example personal vs. professional), social link prediction and recommendation, trend prediction for news topics, predicting next trending topic in a community, and predicting stock market performance based on collective mood and sentiment sampled from social media. **Mining social media data has own challenges. Social data is very noisy. It means, in social media data, the signal to noise ratio is very low. A well-known example is Twitter which is dominated by celebrities. Also the majority of tweets are about daily, redundant, and non-important activity of the users. Social data is usually temporal and also known as what we call it big data. Other challenges are privacy issues and credibility of social media. In this research, some of these issues will be addressed. Mining social media data comprises three main components: data collection and pre-processing, analytics, and presentation. In data collection, social data is collected using APIs provided by social sites. Some pre-processing tasks are also applied such as text processing (as long as we are dealing with content), noise removal, and anonymization to protect user privacy. Analytics component includes a wide range of machine learning and data mining tasks such as classification, clustering, recommendation, and prediction. One well-known example is to predict future social links (who will become connected to whom in future) given current social network topology. It addresses the well-known problem of link prediction. The third component presents the analytics result using visualization techniques. In this research, the main focus is to develop methods for the analytics component mainly for socio-economic problems such as predicting stock market performance, social-political crisis, and public health risks. For the two other components, we will employ available tools. **Mining social media data has a wide range of applications from marketing to social and health sciences to politics. Let's asymptotically assume Twitter as a very large social sensor. Although it is very noisy, by appropriate noise filtering, we are able to understand very important social trends such as customer intentions, political opinions, and patterns of social miss-conduct. Other potential applications of the proposed research include: deception detection, user profiling for personalization, stock market trend prediction, sentiment analysis, person to person recommendation, community detection, and influence and reputation analysis, social role prediction (who is doing what), social link classification, detecting network abuse, and spam-user detection.

社交媒体数据被定义为在社交环境中或在社交交互期间生成的任何数据。社交媒体用户每天都会产生大量的数据。例如，每三天就有10亿条推文。这些数据是了解社会趋势，情绪，意见和意图的宝贵来源。该研究的目的是研究如何建立挖掘社交媒体数据的模型，以了解主要的社会趋势和模式，并设计有效的工具来预测复杂的社会经济系统。与其他数据挖掘问题类似，许多任务可以在社会数据挖掘框架下实现，包括分类，聚类，推荐和预测。具体示例包括基于社交媒体合作者和用户对产品、主题或内容的情感、社交网络中的社交角色预测、从社交网络中提取交易网络、对社交网络中的关系类型进行分类（例如个人对专业）、社交链接预测和推荐、新闻话题的趋势预测、预测社区中的下一个趋势话题，以及基于从社交媒体中采样的集体情绪和情绪来预测股市表现。** 挖掘社交媒体数据有自己的挑战。社交数据非常嘈杂。这意味着，在社交媒体数据中，信噪比非常低。一个著名的例子是由名人主导的Twitter。此外，大多数推文都是关于用户的日常，冗余和不重要的活动。社交数据通常是暂时的，也被称为大数据。其他挑战是隐私问题和社交媒体的可信度。在本研究中，其中一些问题将得到解决。挖掘社交媒体数据包括三个主要部分：数据收集和预处理，分析和呈现。在数据收集中，社交数据是使用社交网站提供的API收集的。还应用了一些预处理任务，如文本处理（只要我们处理的是内容）、噪声消除和匿名化，以保护用户隐私。分析组件包括广泛的机器学习和数据挖掘任务，如分类，聚类，推荐和预测。一个众所周知的例子是预测未来的社会联系（谁将成为连接到谁在未来）给定当前的社会网络拓扑结构。它解决了众所周知的链接预测问题。第三个组件使用可视化技术呈现分析结果。在这项研究中，主要重点是开发分析组件的方法，主要用于预测股票市场表现，社会政治危机和公共卫生风险等社会经济问题。对于其他两个组件，我们将使用可用的工具。** 挖掘社交媒体数据具有广泛的应用，从营销到社会和健康科学再到政治。让我们渐进地假设Twitter是一个非常大的社会传感器。虽然它非常嘈杂，但通过适当的噪音过滤，我们能够了解非常重要的社会趋势，如客户意图，政治观点和社会不当行为的模式。其他潜在的应用所提出的研究包括：欺骗检测，个性化用户分析，股市趋势预测，情感分析，个人对个人的推荐，社区检测，影响力和声誉分析，社会角色预测（谁在做什么），社会链接分类，检测网络滥用，和垃圾邮件用户检测。