About -

The AAPI DigiTalk project was initiated as part of a post-graduate dissertation for the Digital Humanities department at King’s College London in September 2021. It is designed to function ongoingly and similarly to other online initiatives serving the AAPI community, such as Stop AAPI Hate, a national reporting center that tracks data pertaining to anti-Asian the incidents, and AAPI Data, an online “publisher of demographic data and policy research on Asian Americans and Pacific Islanders.” These initiatives and other members of the AAPI community have highlighted the need for the development of better data practices and methods that would allow for a more nuanced understanding of the diverse needs of the AAPI community. The AAPI DigiTalk Project examines how community-created online data can be used to understand the composition and concerns of the community itself, while also exploring and critiquing different methods for collecting, interpreting, and reflecting data.

FAQS

What does AAPI mean?

AAPI is a panethnic demographic label used in American public policy to refer to Asians, Asian-Americans, and Pacific Islanders. The AAPI DigiTalk project uses this acronym to remain consistent with public policy, research scholarship, and community organizers though it should be noted that the implications of using the AAPI label is widely debated. AAPI amalgamates over 50 different ethnic subgroups and over 100 languages, making it difficult for data collection, analysis, interpretation, and application to accurately reflect the diverse experiences, challenges, histories, and perspectives of AAPIs. Many AAPI organizers and research groups have called for the disaggregation of AAPI data, research, and policies. While the AAPI DigiTalk Project uses the acronym “AAPI” in reference to Asian, Asian-American, and Pacific Islander communities, mixed-method approaches to data sourcing, collecting, analyzing, and visualizing will work towards examining and reflecting the diversity of AAPI-oriented online communities.

More on AAPI identities
Data on AAPI ethnic diversity

How do I use your website?

The best place to start is the place where the project began: analyzing the #StopAAPIHate/#StopAsianHate movement on Twitter. The insights on this page are being used to direct the next steps for this project, which will be recorded via the blog. The main #StopAAPIHate Tweets page also features an interactive network visualization that allows users to find specific nodes and their connections to the #StopAAPIHate hashtag. While the Twitter dataset is not publically available, the visualizations on this page allow the user to interact with the data via the visualization for their own exploration and interpretation. An explanation for how the visualization was created and parameters for interpreting the visualization are included on the main page. Additional visualizations and insights pulled from other methods of data analysis (ex. Natural Language Processing and Topic Modelling) are used to help contextualize the data for the reader, as context is often necessary for interpretation. Visualizations are meant to be a means of encouraging data exploration and are accompanied by background information to help make the processes behind data collection, analysis, and visualization more transparent.

What is the research focus for this project?

The AAPI DigiTalk project focuses on analyzing digitally-born internet data and content pertaining to the AAPI community (ex. social media data, media content). The idea is that data and social media analysis can be used to understand the key messages, topics, and questions being voiced by the community online in order to create a more community-oriented direction for research analysis and application. For example, identifying the ways AAPIs express grief, solidarity, or concern online can be used to tailor research direction and development so that appropriate resources can be highlighted or created. In some cases, data analysis can be used to identify the types of resources that already exist within online communities, but are decentralized due to the multiplicity of platforms, algorithms, audiences, and modes of communication. Phase 1 is identifying key trends within the #StopAAPIHate movement on Twitter, since the hashtag and movement began on Twitter and spread from there to other platforms. A dataset of YouTube data pertaining to the #StopAAPIHate movement has been curated.

Can I use your datasets?

Please reach out via the contact form. The two datasets for #StopAAPIHate/#StopAsianHate on Twitter and on YouTube are still being curated for ongoing research and the creator is looking into submitting the dataset of Tweet IDs to Documenting the Now’s Twitter catalog: https://catalog.docnow.io. While the datasets are not publically available at the moment in order to guard against any use of or access to personal data, the creator of the project is happy to discuss ethical methods for internet research and approaches to developing a new dataset.