Melting Meili: Understanding Climate Change with Glacier Data
The data visualisation illustrates the trends in glacier mass balance (the gain and loss of ice from the glacier system) from 2012 to 2022. The data shows negative balances fluctuating over the years, with significant ice loss that has implications such as local hazard situations, water resources instability in glacier-fed regions, and sea-level rise. The significant plummets in 2021 and 2022 underscore the urgency of mitigating the impacts of climate change and natural disaster response.
Missing Girls: Do reproductive choices affect gender balance?
A data visualisation exploring the association between gender ratio and fertility rate in gender-skewed countries.
Data Journalism Communities Research: Communities and missing voices in the DDJ Twitter environment
数据新闻社群研究：DDJ推特环境中的社群与失声
This article is based on a group project in my module Data Journalism in King’s College London.
This research observes users and communities around the #ddj hashtag on Twitter. It intends to find the most important ones and to explore the geographical factors associated with them. It aims to understand the online ecosystem of data journalism and the real people behind it.
这篇文章基于我在伦敦国王学院数据新闻课上参与的团队项目。这项研究观察了 Twitter 上 #ddj 标签周围的用户和社区，找到最重要的社群，并探索与之相关的地理因素。它旨在了解数据新闻的在线生态系统及其背后的真实人物。
👉 点击阅读全文
China’s National Image on Twitter:
The English-language Media and Audience’s Projection During the COVID-19 Pandemic
推特上的中国国家形象：新冠疫情期间的英语媒体以及受众投射
With the global spread of the COVID-19 virus, international discussions about China have experienced a sudden surge on social media. This study aims to investigate how the coverage of influential English news organisations affects China’s national image on Twitter during the COVID-19 pandemic. This research extracted 135 tweets from media with different ideological leanings and 5,577 comments below for deploying content analysis. The method is set to explore the connection between the media’s inclination and the social media users’ responses. Also, the analytical results are presented in visualised forms.
🔗 Google Drive link to the full text
伴随着 COVID-19 病毒的全球传播，关于中国的国际讨论在社交媒体上激增。 本研究旨在探讨在新型冠状大流行期间，有影响力的英语新闻机构的报道如何影响中国在推特上的国家形象。 本研究从不同意识形态光谱的媒体中提取、编码并分析了 135 条推文和 5577 条评论，旨在探索媒体意识形态与社交媒体用户反应之间的联系。此外，分析的结果都以可视化的形式展现。
🔗 百度网盘链接
https://pan.baidu.com/s/17bTawEsYS2-7IcOIgH_gqA?pwd=bsta
提取码: bsta
