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: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=612319
• 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.

社交媒体数据定义为在社交环境或社交互动过程中生成的任何数据。社交媒体用户每天都会生成大量数据。例如，每三天发出10亿条推文。这些数据是了解社会趋势，情感，观点和意图的宝贵来源。拟议的研究的目的是研究如何构建用于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，以预测复杂的社会经济系统。与其他数据挖掘问题类似，可以在社交数据挖掘伞下实施许多任务，包括分类，聚类，建议和预测。具体示例包括基于对产品，主题或内容，社交网络中的社交角色预测的情感对社交媒体合作者和用户进行分类，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，个人与专业人士）（社交联系专业人士），社交联系预测和推荐，新闻趋势预测新闻主题，在社区中进行媒体的趋势，并根据社区的群体进行群体和综合市场和综合市场，并在概述中进行综合和综合市场，并在社交上进行表演和概述。 采矿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，信号与噪声比非常低。一个著名的例子是Twitter，由名人主导。同样，大多数推文都是关于用户的每日，多余和最重要的活动。社交数据通常是时间的，也称为我们所说的大数据。其他挑战是隐私问题和社交媒体的信誉。在这项研究中，将解决其中一些问题。挖掘社交媒体数据包括三个主要组成部分：数据收集和预处理，分析和演示文稿。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，例如文本处理（只要我们处理内容），删除噪声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，例如分类，聚类，建议和预测。一个众所周知的例子是，鉴于当前的社交网络拓扑，预测未来的社会联系（将来将与谁建立联系）。它解决了链接预测的众所周知的问题。第三个组件使用可视化技术介绍了分析结果。在这项研究中，主要重点是为分析组成部分开发主要针对社会经济问题，例如预测股票市场绩效，社会政治危机和公共卫生风险。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从营销到社会和健康科学再到政治的广泛应用。让我们渐变地将Twitter视为一个非常大的社会传感器。尽管它非常嘈杂，但通过适当的噪音过滤，我们能够理解非常重要的社会趋势，例如客户意图，政治观点和社会失误模式。拟议研究的其他潜在应用包括：欺骗检测，用户对个性化，股票市场趋势预测，情感分析，人对人的建议，社区发现以及影响力和声誉分析，社会角色预测（谁在做什么），社交链接分类，检测网络滥用和垃圾邮件用户检测。