Measuring Information Exposure in Dynamic and Dependent Networks (ExpoNet)

Web 2.0是第二代万维网，允许用户在虚拟社区中在线交互、协作、创建和共享信息，它的出现从根本上改变了媒体环境、公众接触的内容类型以及曝光过程本身。个人面临着更广泛的选择(从社交媒体和传统媒体)、新的曝光模式(社交媒体和选择性的)以及替代的内容生产模式(例如，用户生成的内容)。为了了解观点和行为的变化(和稳定性)，有必要衡量一个人接触到了什么信息。社会科学家传统上用来获取信息曝光率的方法通常依赖于报纸阅读和电视新闻广播收看的自我报告。这些措施没有考虑到个人在各种平台上浏览和分享来自社交媒体和传统媒体的各种信息。根据经济合作与发展组织2013年全球科学论坛的报告，社会科学家之所以无法预测阿拉伯之春，部分原因是他们未能理解人类通过社交媒体进行交流的新方式以及他们接触信息的方式。社交媒体在预测最近英国大选结果方面的喜忧参半的记录表明，需要更好的工具和统一的方法来分析和提取这种新型数据的政治意义。我们认为，社会科学需要一套新的工具来理解媒体曝光及其在数字信息时代的影响。该工具将曝光建模为一个网络，并结合了社交和传统媒体来源。无论是在网上消费新闻，还是在社交媒体上生产/消费信息，消费公共事务新闻的基本动力都涉及通过各种手段(如浏览、社交分享、搜索)在用户和媒体内容之间形成联系。因此，网络媒体曝光是一个网络形成的过程，通过互动将内容的来源和消费者联系起来，需要从网络的角度来正确理解它。我们提出了一套可扩展的面向网络的工具，用于1)提取、分析和测量“大媒体数据”时代的媒体内容，2)模拟复杂信息网络中媒体内容的消费者和生产者之间的联系，以及3)了解网络结构与消费者态度/行为的共同发展。为了开发和验证这些工具，我们聚集了一个社会科学和计算机科学交界处的跨学科和国际研究团队。网络分析、文本挖掘、统计方法和媒体分析方面的专业知识将结合在一起，在三个案例研究中测试创新方法，包括2015年英国大选的信息动力学和关于气候变化的意见形成。然而，开发一套复杂的网络和文本分析工具是不够的。我们还寻求在分析在线‘大数据’的计算方法方面建设国家能力。

The advent of Web 2.0 - the second generation of the World Wide Web, that allows users to interact, collaborate, create and share information online, in virtual communities - has radically changed the media environment, the types of content the public is exposed to as well as the exposure process itself. Individuals are faced with a wider range of options (from social and traditional media), new patterns of exposure (socially mediated and selective), and alternate modes of content production (e.g. user-generated content). In order to understand change (and stability) in opinions and behaviour, it is necessary to measure to what information a person has been exposed. The measures social scientists have traditionally used to capture information exposure usually rely on self-reports of newspaper reading and television news broadcast viewing. These measures do not take into account that individuals browse and share diverse information from social and traditional media on a wide range of platforms. According to the OECD's Global Science Forum 2013 report, social scientists' inability to anticipate the Arab Spring was partly due to a failure to understand 'the new ways in which humans communicate' via social media and the ways they are exposed to information. And social media's mixed record for predicting the results of recent UK elections suggests better tools and a unified methodology are needed to analyze and extract political meaning from this new type of data.We argue that a new set of tools, which models exposure as a network and incorporates both social and traditional media sources, is needed in the social sciences to understand media exposure and its effects in the age of digital information. Whether one is consuming the news online or producing/consuming information on social media, the fundamental dynamic of consuming public affairs news involves formation of ties between users and media content by a variety of means (e.g. browsing, social sharing, search). Online media exposure is then a process of network formation that links sources and consumers of content via their interactions, requiring a network perspective for its proper understanding. We propose a set of scalable network-oriented tools to 1) extract, analyse, and measure media content in the age of "big media data", 2) model the linkages between consumers and producers of media content in complex information networks, and 3) understand co-development of network structures with consumer attitudes/behaviours.In order to develop and validate these tools, we bring together an interdisciplinary and international team of researchers at the interface of social science and computer science. Expertise in network analysis, text mining, statistical methods and media analysis will be combined to test innovative methodologies in three case studies including information dynamics in the 2015 British election and opinion formation on climate change. Developing a set of sophisticated network and text analysis tools is not enough, however. We also seek to build national capacity in computational methods for the analysis of online 'big' data.