Chen Yan, Sherren Kate, Lee Kyung Young, McCay-Peet Lori, Xue Shan, Smit Michael
School for Resource and Environmental Studies, Dalhousie University, Halifax, NS, Canada.
School for Resource and Environmental Studies, Faculty of Management, Dalhousie University, Halifax, NS, Canada.
Front Big Data. 2024 May 30;7:1379921. doi: 10.3389/fdata.2024.1379921. eCollection 2024.
Social media has profoundly changed our modes of self-expression, communication, and participation in public discourse, generating volumes of conversations and content that cover every aspect of our social lives. Social media platforms have thus become increasingly important as data sources to identify social trends and phenomena. In recent years, academics have steadily lost ground on access to social media data as technology companies have set more restrictions on Application Programming Interfaces (APIs) or entirely closed public APIs. This circumstance halts the work of many social scientists who have used such data to study issues of public good. We considered the viability of eight approaches for image-based social media data collection: data philanthropy organizations, data repositories, data donation, third-party data companies, homegrown tools, and various web scraping tools and scripts. This paper discusses the advantages and challenges of these approaches from literature and from the authors' experience. We conclude the paper by discussing mechanisms for improving social media data collection that will enable this future frontier of social science research.
社交媒体已经深刻改变了我们的自我表达、交流以及参与公共话语的方式,产生了大量涵盖我们社会生活方方面面的对话和内容。因此,社交媒体平台作为识别社会趋势和现象的数据源变得越来越重要。近年来,随着科技公司对应用程序编程接口(API)设置了更多限制或完全关闭公共API,学者们获取社交媒体数据的机会不断减少。这种情况阻碍了许多利用此类数据研究公共利益问题的社会科学家的工作。我们考虑了八种基于图像的社交媒体数据收集方法的可行性:数据慈善组织、数据存储库、数据捐赠、第三方数据公司、自主开发工具以及各种网络爬虫工具和脚本。本文从文献和作者经验出发,讨论了这些方法的优点和挑战。我们通过讨论改进社交媒体数据收集的机制来结束本文,这些机制将推动社会科学研究的这一前沿领域的发展。