=Paper=
{{Paper
|id=Vol-3869/p01
|storemode=property
|title=The Impact of Digital Analysis and Large Language Models in
Digital Humanity
|pdfUrl=https://ceur-ws.org/Vol-3869/p01.pdf
|volume=Vol-3869
|authors=Andrea Cigliano,Francesca Fallucchi,Marco Gerardi
|dblpUrl=https://dblp.org/rec/conf/icyrime/CiglianoFG24
}}
==The Impact of Digital Analysis and Large Language Models in
Digital Humanity==
https://ceur-ws.org/Vol-3869/p01.pdf
The Impact of Digital Analysis and Large Language Models in
Digital Humanity
Andrea Cigliano1 , Francesca Fallucchi1,2 and Marco Gerardi1
1
Department of Engineering Science, Guglielmo Marconi University, 00193 Rome, Italy
2
Leibniz Institute for Educational Media, Georg Eckert Institute, 38118 Brunswick, Germany
Abstract
The advent of digital analysis tools and Large Language Models (LLMs) has significantly altered the landscape of digital humanities,
introducing new methodologies for processing and interpreting vast amounts of data. This paper provides a comprehensive analysis of
these technologies, examining their implications for research within digital humanities. We focus on the transformative effects of digital
analysis tools and LLMs, assessing their potential to enhance understanding and accessibility of complex humanistic data, while also
discussing the inherent challenges and ethical considerations these technologies introduce. By analyzing case studies and reviewing
recent developments in the field, this study aims to provide a nuanced understanding of how digital analysis and LLMs is reshaping
scholarly practices in humanities disciplines.
Keywords
LLM: Large Language Model, NLP: Natural Language Model, GenI: Generative Artificial Intelligence, DH: Digital Humanity, AI: Artificial
Intelligence
1. Introduction • Digital Archives and Preservation: Digital Li-
braries, Online Repositories, Digitization Technolo-
Digital humanities integrate computational tools into tra- gies, Long-term Digital Preservation Strategies.
ditional humanities disciplines to explore new research • Virtual and Augmented Reality: 3D Modeling and
methodologies. The incorporation of advanced digital anal- Reconstruction, Virtual Museums and Exhibitions,
ysis and LLMs promises substantial enhancements in text Immersive Historical Experiences.
analysis, data visualization, and cultural data analytics. This • Digital Publishing and Communication: Open Ac-
paper aims to systematically evaluate these impacts, offer- cess Platforms, Interactive E-books, Academic Blog-
ing insights into both the advancements and complications ging, Social Media for Scholarly Communication.
posed by these technologies. Digital humanities encompass
• Computational Methods:Distant Reading, Network
a multidisciplinary field where digital technology intersects
Analysis, Topic Modeling, Sentiment Analysis.
with arts, humanities, and social sciences research. The
• Digital Pedagogy: Online Learning Platforms, Dig-
advent of advanced AI technologies, especially LLMs like
ital Literacy Programs, Interactive Educational Re-
OpenAI’s GPT series, has introduced new tools for textual
sources.
analysis, data interpretation, and even the generation of
human-like text, offering both unprecedented opportuni- • Ethical and Legal Considerations: Data Privacy and
ties and significant challenges. This paper explores these Security, Intellectual Property Rights, Ethical AI De-
dynamics, offering insights into the integration of LLMs in velopment.
DH and discussing the broader implications for researchers. • Interdisciplinary Collaborations: Partnerships with
Computer Science, Collaborations with Social Sci-
ences, Integration with STEM Fields.
2. Landscape • Emerging Technologies: Internet of Things (IoT)
in Cultural Heritage, Quantum Computing Applica-
Landscape represents the interconnected technologies, tions, Non-Terrestrial Networks (NTNs) for monitor-
methodologies, and considerations that shape the field of ing applications [11], Advanced Natural Language
DH [1]. Each of these areas contributes to how humani- Generation (LLMs).
ties scholars engage with digital tools and data to conduct
research, preserve cultural heritage, and disseminate knowl-
edge. We can distinguish the following components of a 2.1. Digital Analysis
landscape relating to the DH: Digital analysis involves the use of software and algorithms
to analyze cultural and historical data. It includes tech-
• Core Technologies: Artificial Intelligence and Ma-
niques like data mining, visualization, and statistical anal-
chine Learning [2, 3], Neural networks and trasform-
ysis, which allow scholars to uncover patterns and trends
ers [4, 5], Natural Language Processing [6], Big
that are not apparent through traditional methods.
Data Analytics [7, 8], Cloud Computing [9, 10],
Blockchain.
• Research Tools: Text Analysis Software, Data Visual- 2.2. Large Language Models (LLMs)
ization Tools, Geographic Information Systems(GIS), LLMs like OpenAI’s GPT-3 represent a breakthrough in nat-
Digital Asset Management Systems, Collaborative ural language processing technology from November 2022.
Platforms. These models, trained on extensive datasets, are capable of
ICYRIME 2024: 9th International Conference of Yearly Reports on Infor- generating coherent text, completing linguistic tasks, and
matics, Mathematics, and Engineering. Catania, July 29-August 1, 2024 providing new tools for textual interpretation and genera-
$ a.cigliano@unimarconi.it (A. Cigliano); f.fallucchi@unimarconi.it tion in the humanities. Generative AI refers to algorithms
(F. Fallucchi); m.gerardi@unimarconi.it (M. Gerardi)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License capable of creating content, from text to images, by learning
Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
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Andrea Cigliano et al. CEUR Workshop Proceedings 1–7
Figure 1: DH Landscape synthesis
from large datasets. LLMs, a subset of generative AI, are 4.0.1. Textual Analysis and Interpretation
trained on diverse internet corpora [12] and can generate
This is particularly useful in experimental literature, auto-
coherent, contextually relevant text based on input prompts.
mated storytelling, and the creation of dialogue or narrative
Models such as GPT-3 have demonstrated capabilities that
for digital artifacts [14]. These models are adept at inter-
include answering questions, writing essays, summarizing
preting and providing context to vast archives of text, help-
texts, and more. Generative AI and Large Language Models
ing scholars identify themes, trends, and patterns across
(LLMs) have gained significant attention within the digital
large datasets that would be unmanageable for manual re-
humanities due to their ability to process and generate large
view. In this context we can distinguish the following sub-
volumes of text-based data. Their classification within this
applications:
context can be broadly organized into different categories
based on their applications, methodologies, and impacts. • Semantic Analysis: LLMs can analyze large bodies
Here’s an overview of how Generative AI and LLMs are of text to understand contextual meanings, identify
classified in the digital humanities. themes, and detect nuances in language that are
often missed by traditional analysis methods. This
capability is crucial for Literary analysis, historical
3. Methodology documentation, and cultural studies.
This study synthesizes data from various case studies, peer- • Sentiment Analysis: Digital tools can assess the
reviewed articles, and firsthand experiments with LLMs sentiment of historical texts, literary works, and
in digital humanities projects. Through qualitative and even large collections of social media posts to gauge
quantitative analysis [13], we assess how these technologies public sentiments over time or reactions to specific
are currently applied and theorize their future trajectories events or figures in history.
within the field. • Stylistic Analysis: LLMs are used to study stylistic
elements of writing across different time periods or
authors. This application helps in authorship attri-
4. Applications of Digital Analysis bution and understanding the evolution of language
and LLMs and writing styles in literature.
The applications of digital analysis tools and Large Language 4.0.2. Data Mining and Big Data Analysis
Models (LLMs) in digital humanities are vast and diverse.
These technologies have revolutionized how researchers, We can distinguish two macro components: Pattern Recog-
educators, and practitioners approach the humanities, offer- nition, in this case Digital analysis tools can sift through
ing new insights and methodologies that were previously vast amounts of data to identify patterns and trends [15].
unattainable. Below is an exploration of several key appli- In historical studies, for example, this can reveal migration
cations and how they impact the field of digital humanities. patterns, economic changes, or social dynamics of a par-
ticular era. Network Analysis, where in digital humanities,
network analysis tools can map relationships among various
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Andrea Cigliano et al. CEUR Workshop Proceedings 1–7
entities within literature, such as characters in novels, his- 4.0.8. Cultural Analytics and Critique
torical figures, or even concepts and ideas that are prevalent
LLMs identify and analyze patterns in art, music, literature,
within a specific cultural or intellectual community.
and other cultural artifacts over large temporal and spatial
scales (Cultural Pattern Recognition). This includes using
4.0.3. Language Translation and Transcription AI to critique and analyze its own impact on digital human-
Machine Translation in LLMs models, provide tools for ities, assessing how technology shapes cultural and societal
translating rare or endangered languages, making ancient understanding(Critical AI Studies).
manuscripts or texts accessible to a global audience. This
application is particularly significant in preserving linguis- 4.0.9. Archival and Curation Tools
tic heritage and making non-English academic resources
Digital Archiving: AI facilitates the organization, categoriza-
available to a broader audience. Another case is Automatic
tion, and searchability of digital archives, enhancing the ac-
Transcription, where LLMs and digital tools automate the
cessibility of vast digital collections. Curatorial Assistance:
transcription of audio recordings into text, such as oral his-
AI tools assist in curating digital exhibitions and virtual
tories and lectures, which can then be analyzed or archived
museum tours by selecting themes and artifacts based on
digitally for future research. In general LLMs and GenAI
scholarly input and visitor interactions. These categories re-
aids in transcribing handwritten or archaic documents into
flect the multifaceted roles that generative AI and LLMs play
digital formats, which are then more accessible for anal-
in the digital humanities [17, 18]. They not only enhance
ysis and interpretation(Historical Text Transcription). AI
traditional research methodologies but also introduce new
models facilitate the translation of texts across languages,
modes of scholarly inquiry and interaction with digital texts
making non-native and ancient literature accessible to a
and datasets. As these technologies evolve, their classifica-
global audience without the immediate need for human
tion may expand to include more specialized functions and
translators(Translation).
applications tailored to the nuanced needs of humanities
research.
4.0.4. Digital Archiving and Preservation
We can distinguish Document Digitization and Categoriza- 4.0.10. Creation of Digital Exhibits and Virtual
tion, in this case Digital tools automate the scanning and Spaces
categorization of physical documents into digital archives.
Interactive Exhibits: Museums and educational institutions
LLMs can enhance this process by automatically tagging and
utilize digital tools to create virtual tours and interactive
organizing the documents based on their content. Preserva-
exhibits that allow users to explore artifacts, artworks, and
tion of Digital Artifacts: Digital humanities also involve the
historical documents from their devices, enhancing accessi-
preservation of digital art and online culture, where digital
bility and engagement. Virtual Reality (VR) and Augmented
analysis tools help in archiving web-based art and social
Reality (AR): These technologies are employed to create
media content.
immersive educational experiences, such as virtual reality
environments that simulate historical sites or events [19].
4.0.5. Semantic and Sentiment Analysis
Understanding the deeper meanings, connotations, 4.0.11. Educational Tools and E-Learning
and contexts of words in historical and contemporary
Adaptive Learning Systems: Digital analysis tools can help
texts(Semantic Analysis). Analyzing the emotions or
develop educational platforms that adapt to the learning
sentiments expressed in texts, which can be particularly
pace and style of individual students, particularly in hu-
useful in studies of literature, social media, and historical
manities education, by analyzing student interactions and
documents to gauge public opinion and cultural trends over
providing customized feedback. Online Courses and Work-
time(Sentiment Analysis).
shops: LLMs can assist in the creation of educational con-
tent, generating reading materials, quizzes, and interactive
4.0.6. Data Visualization and Mapping discussions that facilitate online learning in the humanities.
AI can generate dynamic visual representations of textual
data, making it easier for researchers to explore complex 4.0.12. Public Engagement and Outreach
datasets and uncover relationships (Interactive Data Dis-
Social Media Analysis: Digital tools analyze social media
plays). Linking textual data with geographical information
data to study contemporary cultural trends and public opin-
systems (GIS) to map the occurrence of certain themes or
ions, providing humanities researchers with real-time data
discussions across different locations(Geospatial Mapping).
on human behavior and societal changes. Crowdsourcing
and Collaborative Research: Platforms designed with the
4.0.7. Ethical and Responsible AI Use help of digital tools enable collaborative projects where
In digital humanities, the classification of AI tools also in- scholars and the public can contribute to and engage with
volves identifying potential biases in AI-generated content humanities research globally.
and developing methodologies to mitigate these biases (Bias
Mitigation) [16]. Using AI to verify the authenticity of 4.1. Digital Humanities: Scope and Tools
digital artifacts and trace their historical and cultural ori-
gins(Authenticity and Provenance Verification). Digital humanities involve applying digital tools to humani-
ties subjects, enabling new research methodologies like text
mining, machine learning, and data visualization. These
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Andrea Cigliano et al. CEUR Workshop Proceedings 1–7
tools facilitate the exploration of cultural patterns, histori- 2. Data Visualization Tools: - Tableau, Gephi: Useful for
cal data, and literary analysis at scales previously unman- creating visual representations of data, helping to illustrate
ageable. DH is an interdisciplinary field that merges the complex relationships and patterns. - Timeline Tools: Tools
techniques and methodologies of the digital world with like TimelineJS help researchers create interactive timelines
the study of the humanities. This field includes, but is not that highlight historical events and trends.
limited to, disciplines like literature, history, art, music, phi- 3. Mapping Tools: - GIS Software: ArcGIS and QGIS are
losophy, cultural studies, and linguistics. The scope of DH used to analyze and display spatial data, providing insights
is vast, integrating computational methods into the tradi- into geographical aspects of historical and cultural studies. -
tional humanities to enhance research, teaching, and the StoryMap JS: Allows for the creation of maps that combine
dissemination of knowledge. narrative text with images and multimedia content.
4. Digital Archiving Systems: - Omeka: Provides a web
4.1.1. Enhanced Research Methods platform for publishing collections and exhibitions, widely
used by libraries, museums, and archives. - Digital Asset
• Textual Analysis: Digital tools allow for the analysis Management Systems (DAMS): Helps institutions manage
of large volumes of text, enabling scholars to uncover their digital content effectively.
patterns, trends, and insights at a scale not feasible 5. Programming and Development Environments: -
manually; Python and R: Popular programming languages for han-
• Historical GIS (Geographic Information Systems): In- dling data-intensive projects in DH. - Jupyter Notebooks:
tegrates geographical data into humanities research, Offers an interactive environment for coding, visualizing,
allowing for the spatial analysis of historical events and publishing data analysis projects.
and cultural developments. 6. Collaborative Tools: - Wiki Software: Facilitates col-
• Cultural Analytics: Uses data analysis to study pat- laborative writing and knowledge sharing. - Slack, GitHub:
terns in visual culture, music, and other art forms, Used for project management and collaboration in larger
identifying trends and influences over time. digital projects.
7. Content Management Systems (CMS): - WordPress,
4.1.2. Interdisciplinary Collaboration Drupal: Often used to create and manage digital content,
facilitating dynamic web presence for DH projects.
• Promotes collaboration across disciplines, combin- The integration of these tools into humanities research
ing methods from social sciences, computer sciences, has not only transformed traditional methodologies but also
and humanities to tackle complex research ques- expanded the boundaries of what can be explored and under-
tions. stood in the humanities. As digital technologies continue to
• Encourages partnerships between academicians, evolve, the scope and tools of digital humanities will likely
technologists, and sometimes the public, fostering a expand further, continuing to redefine the engagement with
richer, more diverse research environment, includ- humanistic studies in the digital age.
ing emulation platforms [20].
4.1.3. Public Engagement and Education
4.3. Methodology
This study employs a qualitative research approach, syn-
• Digital exhibitions and virtual reality experiences
thesizing information from various case studies, scholarly
make humanities more accessible to the public.
articles, and empirical research findings to evaluate the im-
• Open-source projects and tools democratize access pact of LLMs in digital humanities. The research includes a
to knowledge, allowing broader participation in hu- meta-analysis of published digital humanities projects uti-
manities scholarship. lizing generative AI and a review of literature discussing
theoretical and methodological implications.
4.1.4. Preservation and Archiving
Digital archiving ensures the preservation of important his- 5. Applications of LLMs in Digital
torical and cultural materials, making them accessible world-
wide and safeguarding them against physical degradation. Humanities
4.1.5. Scholarly Communication 5.1. Text Analysis and Interpretation
Digital platforms facilitate the sharing of research outputs LLMs are particularly potent in their ability to process and
and scholarly discourse more dynamically and widely than analyze large volumes of text, providing insights into lin-
traditional publishing methods. guistic patterns, stylistic features, and historical language
evolution. Examples include the use of AI for analyzing
vast archives of unstructured text in libraries and museums.
4.2. Tools in Digital Humanities Text analysis and interpretation form a core component of
The tools used in digital humanities are diverse, ranging the intersection between Large Language Models (LLMs)
from data analysis software to digital storytelling platforms, and digital humanities. LLMs, due to their deep learning
each serving different aspects of humanities research: foundations and substantial training datasets, offer sophisti-
1. Text Analysis Tools: - Natural Language Processing cated tools for automating and enhancing the examination
(NLP) Software: Tools like NLTK, spaCy, and GPT-3 help of textual data. This synergy is particularly influential in the
analyze text for syntax, sentiment, and semantic content. field of digital humanities, where traditional hermeneutic
- Concordance Software: Allows scholars to locate every methods meet modern computational analytics. Text analy-
occurrence of a word or phrase, facilitating analysis of texts. sis involves using algorithms to understand, interpret, and
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Andrea Cigliano et al. CEUR Workshop Proceedings 1–7
derive meaningful information from textual data. In the con- and Collaboration: Digital humanities promote open access
text of digital humanities, LLMs apply several techniques: to information and collaborative research projects. Plat-
forms like digital archives, open-source tools, and collabo-
• Tokenization and Parsing: Breaking down text into
rative research networks democratize access to knowledge
manageable pieces or tokens to analyze structural
and facilitate global research partnerships.
and grammatical relationships.
• Named Entity Recognition (NER): Automatically
identifying and classifying key elements in text into 6.1. Data Privacy and Security
predefined categories such as names of people, or- Concerns about data privacy and the ethical use of digital
ganizations, locations, etc. archives are paramount, particularly when dealing with
• Sentiment Analysis: Determining the affective state sensitive or personal historical data.
of the text, whether positive, negative, or neutral,
which can be particularly useful in analyzing lit-
erary works or historical documents to gauge the 6.2. Dependence on Technology Providers
sentiments expressed. The reliance on proprietary AI technologies, often devel-
oped by major corporations, poses risks related to accessi-
5.2. Enhancing Accessibility of Historical bility, transparency, and continuity of research.
Documents
Generative AI assists in the transcription and translation 7. Future Directions and discussion
of historical manuscripts, making them more accessible to
researchers worldwide. This not only democratizes access DH continue to evolve, driven by advancements in AI, Ma-
but also enriches global understanding of historical contexts. chine Learning and Data Visualization technologies. Future
research may focus on improving the interpretability of
machine-assisted analyses and enhancing the integration of
5.3. Creative Writing and Artistic Endeavors qualitative and quantitative methods. Ethical considerations
In creative domains, LLMs have been used to generate lit- and bias are crucial aspects to address when discussing the
erary texts and assist in experimental poetry and narrative integration of AI and Large Language Models (LLMs) in
projects, often blurring the lines between human and ma- digital humanities. Inherent Biases in Training Data, LLMs
chine creativity. are trained on vast amounts of text data, which can per-
petuate existing biases in historical and cultural narratives.
These biases may disproportionately represent certain per-
6. Challenges and Limitations spectives, cultures, or time periods, potentially skewing
research outcomes. Another type of risk is the underrep-
Regarding the challenges and limitations we can identify resenting minority voices, non-Western perspectives, and
the following strands: Technological Dependence, the re- marginalized groups in AI-assisted research. This could
liance on sophisticated computational tools requires ongo- lead to reinforcing existing power structures and narratives
ing technical support and significant resources, which can in humanities scholarship [23]. The "black box" nature of
be a barrier for institutions with limited funding. Data Pri- many AI systems makes it challenging to understand how
vacy and Ethical Concerns, as with all digital data, there are they arrive at certain conclusions or interpretations [24, 25].
concerns regarding privacy, particularly when dealing with This lack of transparency can be problematic in humani-
sensitive or personal historical records. Ethical consider- ties research, where understanding the reasoning behind
ations also arise in how data is interpreted and presented interpretations is crucial. Using AI to analyze historical
[21]. The Impact on research and scholarship is relevant, in or cultural data raises questions about consent, especially
particular scale and Scope: Digital tools allow researchers to when dealing with sensitive or personal information from
handle datasets of unprecedented size and complexity, often the past [26]. There’s a need to balance research benefits
referred to as "big data" in humanities [22]. This capacity with respect for privacy and cultural sensitivities. As AI
has led to the emergence of "distant reading" techniques, becomes more capable of generating human-like text, there
where scholars analyze patterns across thousands of texts, are concerns about maintaining the authenticity of histor-
rather than studying a single piece in depth ("close read- ical documents and distinguishing between AI-generated
ing"). And another fied is Interdisciplinary Research: Digital and human-created content. Over-reliance on AI tools could
analysis fosters interdisciplinary research approaches that potentially lead to a reduction in critical thinking skills or
blend techniques from computer science, statistics, and the traditional research methods in humanities [27]. There’s a
arts and humanities. This crossover has enriched academic need to find a balance between leveraging AI capabilities and
research, leading to new insights and methodologies. En- maintaining core humanities research skills. Researchers
hanced Accuracy and New Insights: Automated analysis must consider the ethical implications of using AI-generated
can process information with a level of accuracy and speed insights, especially when these insights could impact cul-
unattainable by manual methods. Moreover, it can reveal tural understanding or policy decisions. The adoption of
insights that were previously obscured by the sheer volume AI tools in digital humanities could create or exacerbate
of data or the limitations of human analysis. Educational existing inequalities between well-funded institutions and
Transformations in this scope are the following impact in those with fewer resources [28]. This raises questions about
teaching and Learning: Digital humanities have transformed equitable access to advanced research tools and method-
pedagogical approaches within humanities disciplines. In- ologies. AI systems may not always understand or respect
teractive tools and online archives provide students with cultural nuances, leading to misinterpretations or insensi-
direct access to primary materials and analytical tools, sup- tive handling of cultural data. Determining responsibility
porting more active learning environments. Open Access
5
Andrea Cigliano et al. CEUR Workshop Proceedings 1–7
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