Evaluating Social Media to Identify and Leverage Engagement with Arts and Culture Experiences

该项目旨在（1）建立在Qualia项目的社交媒体分析工作的基础上，将该研究引入一个新的方向，以更深入地了解对艺术和文化体验的介导反应，（2）通过包括国际专业知识和跨学科工作来扩展这项先前的研究-跨越艺术技术，传播，社会学和计算机科学-提供有关社交媒体分析的新见解，（3）开发社交媒体话语与社交媒体用户持有的真实观点之间关系的初步模型，基于对社交媒体上发生的艺术和文化体验的研究讨论，（四）为开发新的分类情感分析工具奠定实证基础，该工具使用在线民族志和对话分析来识别社交媒体用于交流的方式，重点关注社交媒体内容艺术和文化经验，（5）为艺术和文化话语开发一个新的原型开源情感分析工具[SMILE]，以提供对之前Qualia项目中有关自动化社交媒体分析的初步发现的实际测试，（6）通过从业者研讨会和网络传播新的情感分析工具和相关研究以及实用建议，该项目将研究为艺术和文化组织提供昂贵的数字化工具和技术的潜力，同时利用机器学习技术推动应用研究的前沿。与此同时，我们将开发有关“媒体效应”的新知识，在社交媒体中传播艺术和文化话语。我们建议进行在线民族志，重点是一个小的分层随机样本的Twitter用户讨论合作伙伴的艺术和文化组织在Twitter上，旨在揭示在线和离线话语之间的关系。当社交媒体话语被广泛视为受众洞察的可靠来源时，对社交媒体话语所能揭示的局限性的现实理解是至关重要的。最终，SMILE项目将提供一个用于Twitter数据的原型情感分析工具（"SMILE工具"），为这项关于社交媒体分析局限性的研究提供一个焦点。情感分析工具最初需要开发一个手动注释的语料库，根据艺术和文化话语进行调整和校准。SMILE情感分析工具从Twitter中提取与指定搜索（即艺术组织）相关的推文进行处理。SMILE工具超越了积极/消极，而是根据“体验质量”类别，具体的情感反应和通过拟议的在线民族志研究和与合作伙伴艺术组织的合作确定的其他相关类别对反应进行分类。SMILE分析工具可以为组织提供对其在线受众的更深入了解，同时建立一个衡量文化需求和兴趣的可转移工具。与此同时，代表艺术，计算机科学和社会科学观点的合作伙伴之间的跨学科对话将与工具开发过程一起进行。这一对话将得出明确的结论，说明这种理解公众的方法在技术、方法和实践方面的局限性。这里提出的工作将使用一个在线民族志研究开发一个初步的理论模型，Twitter的互动。虽然我们目前的重点是艺术和文化话语，但该项目对大数据分析具有广泛的影响。

This project aims to (1) Build upon work on social media analytics from the Qualia project by taking that research in a new direction to develop deeper insights about mediated responses to arts and culture experiences, (2) expand on this prior research by including international expertise and cross disciplinary working- spanning arts technology, communication, sociology and computer science- to deliver new insights about social media analytics, (3) develop a preliminary model of the relationship between discourse on social media and authentic views held by social media users, based on researching discussions about arts and culture experiences occurring on social media, (4) establish an empirical basis for developing a new categorical sentiment analysis tool focusing on social media content using online ethnography and conversation analysis to identify the ways in which social media are used to communicate about arts and culture experiences and (5) Develop a new prototype open source sentiment analysis tool [SMILE] for arts and culture discourse to provide a practical test of the initial findings about automated social media analysis from the preceding Qualia project and (6) disseminate new sentiment analysis tool and associated research and practical recommendations through practitioner workshops and web-based communications.The project will research the potential of making otherwise expensive digital tools and techniques available to arts and culture organisations, while pushing the frontiers of applied research using machine learning technology. Meanwhile, we will develop new knowledge about the 'medium effects' that characterise arts and culture discourse within social media. We propose to conduct an online ethnography focusing on a small stratified random sample of Twitter users discussing partner arts and culture organisations on Twitter, aiming to uncover the relationship between online and offline discourse. A realistic understanding of the limits of what social media discourse can reveal is essential at this time when such data is widely seen as an unproblematic source of audience insights.Ultimately, the SMILE Project will deliver a Prototype Sentiment Analysis Tool for use on Twitter data ('SMILE tool') to provide a focal point for this research on the limits of social media analytics. The Sentiment Analysis tool will initially require developing a manually annotated corpus tuned and calibrated to arts and culture discourse. The SMILE sentiment analysis tool pulls tweets related to specified searches (i.e. for an arts organisation) from Twitter for processing. The SMILE tool extends beyond positive / negative, instead categorising responses according to 'quality of experience' categories, specific emotional responses and other relevant categories identified through the proposed online ethnography research and engagement with partner arts organisations. The SMILE analytic tool may offer organisations a more robust understanding of their online audiences, while establishing a transferable tool for measuring cultural demand and interests. Meanwhile, an interdisciplinary dialogue amongst the partners representing arts, computer science and social science perspectives will run alongside the tool development process. This dialogue will yield clear findings about the technical, methodological and practical limitations of this approach to understanding publics. The work proposed here will use an online ethnography study to develop a preliminary theoretical model for Twitter interactions. While our immediate focus is arts and culture discourse, the project holds broad implications for big data analysis.