5.4.2023
As a highly cross-disciplinary community, or cluster of different communities, DH projects span the disciplines of history, literature, linguistics, art history, anthropology, etc. DH bring digital methods to bear on traditional humanities scholarship, but it is not intended to replace the traditional humanities, rather, it is creating a new research methodology that allows humanities scholars to criticize the use of big data in digital age.
In the humanities, there are “born digital” data and digitized data produced through mass digitization processes.[i] The use of big data in the humanities potentially shapes the way humanities scholars consider and examine research questions. An example of the application of big data to the discipline of history is shown in “China GovernmentEmployee Database – Qing (CGED-Q) 中国历史官员量化数据库(清代).” Conducted by the Lee–Campbell ResearchGroup at SHSS, HKUST, the project constructed a dataset from surviving editions of Qing civil officials 縉紳錄 and military officials 中樞備覧 for studying Qing bureaucracy. As of July 2020, the database has a total of 8,933,629 records containing information on the origin, family background, mode of appointment, behavior, and living conditions of 1,705,780officials and their relatives, which users can explore to learn about the various characteristics of government employees during the Qing (1644–1911)dynasty.
Network visualization can show the hidden agents and structure in texts and images. “Six Degrees of Francis Bacon,” a DH project created by CarnegieMellon University and Georgetown University, precisely describes the social network of early modern Britain. The project not only answers the question “Do you know if Francis Bacon knew Thomas Hobbes?” but also reveals the inherent gender bias of the written records (Fig.3). The project used biographic records from the Oxford Dictionary of National Biography to build a database. Of the 16,000 entries, women accounted for only 6%.[i]
Many galleries, libraries, museums, and archives (GLAM) are digitalizing and publishing digitalized objects more creatively, contributing to sustainable and accessible open data collections for public displays. The British Museum is looking for innovative methods to display its online collections. With support from theGoogle Cultural Institute, the British Museum launched the interactive digital exhibit “Museum of theWorld.” The 3D animated timeline using WebGraphics Library technology was designed to display the museum’s collections from a first-person perspective. More than 4,500 items in theBritish Museum were grouped into five categories (Art and design, Living and dying, Power and identity, Religion and belief, and Trade and conflict), which encourages users to compare objects from different continents in the same category. The greatest challenge for digital art historians is that there are not many high-definition original images available for training. This is especially true for artists with few surviving works. Therefore, the use of machine learning to distinguish forgeries from originals remains questionable.
Sentiment analysis is a natural language processing technique that identifies the polarity of a text. It is also an emerging field that lies at the crossroads of linguistics, literature, and computers and seeks to automatically identify the sentiments in texts. It has been used to explore many types of literary texts, such as novels, poetry, plays, songs, social media content, etc. The Viral Texts project built theoretical models to examine why fiction and poetry “go viral” or were “reprinted” in 19th-century newspapers and magazines.Through two primary text reuse detection methods—n-gram shingling and locality sensitive hashing, the project found that anonymous mass copying of these texts made them go viral (Fig.6).They also created a visualization of the connections among newspapers induced by shared reprinted texts.[i]
DH are not simply a combination of humanities and digital tools. Recent practices and projects suggest that DH create new ways of thinking and doing in the humanities. DH also teach users how to think about data critically, which is a continuation of the traditional approach in the humanities.
[i] D. Smith, R. Cordell and E. Dillon,"Infectious texts: Modeling text reuse in nineteenth-century newspapers," in 2013 IEEE International Conference on Big Data,Silicon Valley, CA, USA, 2013 pp. 86-94.
[i] Porras, Stephanie. "Keeping Our Eyes Open: VisualizingNetworks and Art History." Artl@s Bulletin 6,no. 3 (2017): Article 3.
[i] Schiuma, G., & Carlucci, D.(2018, May 8). Big Data in the Arts and Humanities: Theory and Practice(Data Analytics Applications) (1st ed.). Auerbach Publications.