Towards Predicting Socio-economic Systems by Mining Social Media Data

社交媒体数据被定义为在社交环境中或在社交互动过程中产生的任何数据。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、观点和意图的宝贵来源。本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的保护伞下实现，包括分类、聚类、推荐和预测。具体的例子包括基于对产品、主题或内容的情感对社交媒体合作者和用户进行分类，社交网络中的社会角色预测，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（例如个人与专业），社交链接预测和推荐，新闻主题的趋势预测，预测社区中的下一个趋势主题，以及基于从社交媒体中取样的集体情绪和情绪来预测股市表现。

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.