Lab Activity: Gun Island
Select specific videos as source and generate infographic & Slide Deck on it. Post it on your blog. Also check, if these infographics or slides help you understand the novel or not.
For this activity i have chosen video on :
Etymological Mystery | Title of the Novel | Gun Island | Amitav Ghosh
Based on this video here is infographic and slide desk :
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Learning outcome
Slide deck
- Words are like History Books: I learned that the words we use every day, like "Gun," actually hide secret histories and connections to the past that we have forgotten
. - The Mystery of "Gun Island": I discovered that the title doesn't refer to real weapons, but is actually a centuries-old code name for the city of Venice
. - How Language Travels: I realized that a single word can travel from Venice to Arabia and finally to India, changing its from a city name to "bullets" or "guns" along the way
. - The Past is Still Here: I learned that the Sanskrit word for ghost (Bhuta) also means "the past," showing that our present lives are always "haunted" by what happened before us
. - The Real Meaning of "Possession": I understood that being "possessed" in history wasn't about magic, but was actually a way to describe people who had lost their freedom and will to control their own lives
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3. Generate one short video on the video resource which you found difficult to understand. See, if the AI generated video helps you understand the topic or not.
For this Activity i have choose video titled :Part 1: Historification of Myth and Mythification of History | Gun Island | Amitav Ghosh
here is original video:
Video generated by Notebook LM:
- I learned that the "Gun Merchant" legend is not just a fairy tale, but a coded map of real history that links ancient India to places like Venice and Egypt
- I realized that the word "Banduki" (Gun) isn't about weapons at all; it is actually an old Bengali secret name for the city of Venice
- I discovered that myths are used to record scary events, like plagues or natural disasters, because a story is often the safest way to pass a warning down through generations
- I now understand that the "supernatural forces" in the legend, like angry snakes and storms, are ancient ways of describing the climate change we are seeing today
| - I see that history never truly ends; for example, the merchant being sold as a slave hundreds of years ago is a direct mirror of modern human trafficking happening right now
Research Activity:
Topic: Digital Humanities and Modern Folklore
Prompt 1: Create a table showing each source with its publication dates, author credentials, and whether its primary source, secondary analysis or opinion piece.
The most frequently referenced sources and authors across this notebook are as follows:
1. Computational Folkloristics (Source 7)
• Authors: James Abello, Peter M. Broadwell, and Timothy R. Tangherlini.
• Who referred to it specifically:
◦ Joe Ondrak in "Digesting creepypasta: social media horror narratives as gothic fourth-generation digital fiction" (Source 9) references this article for its introduction of "distant reading" as a corrective to traditional close reading.
◦ Tjaša Arčon, Marko Robnik-Šikonja, and Polona Tratnik in "Large language models for folktale type automation based on motifs" (Source 15) cite this work to explain network-based methods for disentangling complex interconnections between motifs and tale types.
◦ "The Computational Turn in Folkloristics" (Source 17) identifies this as a vital foundational text that weds algorithmic approaches to classic interpretive problems.
2. LEGENDARY PERFORMANCES: Folklore, Repertoire and Mapping (Source 2)
• Author: Timothy Tangherlini.
• Who referred to it specifically:
◦ Source 7 ("Computational Folkloristics") cites this study to establish the fundamental premise that traditional storytelling is closely related to the physical environment.
◦ "The Computational Turn in Folkloristics" (Source 17) references this work when discussing "cognitive maps" and how storytellers traditionally tell legends situated close to their homes.
◦ Source 15 ("Large language models for folktale type automation based on motifs") references this work as a pioneer in utilizing the network lens for topic modeling.
3. Digesting creepypasta: social media horror narratives as gothic fourth-generation digital fiction (Source 9)
• Author: Joe Ondrak.
• Who referred to it specifically:
◦ Parthiva Sinha in "Creepypasta and Internet Literature: Unmasking Digital Horrors..." (Source 8) relies heavily on this thesis to explain the evolution of creepypasta, specifically citing Ondrak’s work on hauntology, ontological ambiguity, and the transformation of Gothic traditions.
◦ "The Computational Turn in Folkloristics" (Source 17) highlights Ondrak’s concept of "ontological flattening" as a central theory in the study of digital-native folklore.
4. Motif-Index of Folk-Literature (Foundational External Work)
• Author: Stith Thompson.
• Who referred to it specifically:
◦ Source 15 ("Large language models for folktale type automation based on motifs") uses Thompson’s index as the "ground truth" to test whether AI can accurately detect narrative motifs in Cinderella variants.
◦ Source 7 ("Computational Folkloristics") cites this as the primary classification system for constitutive narrative elements.
◦ "The Computational Turn in Folkloristics" (Source 17) discusses the transition from this manual indexing to automated AI classification.
5. Memes in Digital Culture (Foundational External Work)
• Author: Limor Shifman.
• Who referred to it specifically:
◦ Siyue Yang in "An Analysis of Factors Influencing Transmission of Internet Memes..." (Source 5) uses Shifman’s definition of Internet memes to explore selection criteria and the replication lifecycle.
◦ Joe Ondrak in Source 9 utilizes Shifman’s spectrum of viral and memetic spread to analyze how digital horror narratives move across networks.
◦ "Memes as Modern Digital Folklore" (Source 16) identifies Shifman as the authority on memes as participatory media reflecting cultural trends.
6. Deep Maps and Spatial Narratives (Foundational External Work)
• Editors: David Bodenhamer, John Corrigan, and T.M. Harris.
• Who referred to it specifically:
◦ Charles Travis in "Heritage GIS: Deep Mapping, Preserving, and Sustaining..." (Source 1) cites this book to define "Deep Mapping" as a cartographic practice that acknowledges how human agents build identities through memory.
◦ Christopher Brockman in "Varying Lenses of GIS" (Source 18) references Bodenhamer’s call to tap digital technologies to move narrative beyond the linear constraints of written language.
7. Folk Culture in the Digital Age (Source 12)
• Editor: Trevor J. Blank.
• Who referred to it specifically:
◦ Dr. Sweta Ghosh in "A Study on Changes in Folk tales due to New Media" (Source 3) references Blank’s theory on hybridizing folk culture through the human-computer interface.
◦ Parthiva Sinha in Source 8 cites Blank to explore the relationship between creepypasta and contemporary legends.
◦ "The Computational Turn in Folkloristics" (Source 17) references this volume as an essential resource documenting emergent digital cultural scenes.
Within the landscape of this notebook, Source 7 (Computational Folkloristics) and the research of Stith Thompson and Limor Shifman act as the primary hubs of a digital terminal; they provide the essential "operating code" and organizational logic that all other studies whether they are examining 16th-century woodcuts or the Slender Man must plug into to be processed by the academic community. Prompt 3: Summarize the primary perspective of the top five most substantial sources.
This source asserts that creepypasta is a distinct, emergent genre that manifests as a continuation of the Gothic and horror literary traditions. The central perspective is the concept of "ontological flattening," where real user responses and fictional story elements exist in the same textual space without explicit indicators of fictionality. By leveraging the structure of social media platforms, creepypasta creates an ontological ambiguity that allows fictional threats to "become real" through networked interaction and performative spread.
3. The Computational Turn in Folkloristics: A Systematic Analysis (Source 17)
This systematic meta-analysis documents the transition of folklore transmission from "physical campfires to digital servers". It highlights the role of "algorithmic culture," where software agents on platforms like X, Reddit, and YouTube act as "folklore connectors" that exert a priori influence on which traditions circulate. The primary perspective is that digital folklore is agentive, meaning the technology itself through engagement-based prioritization and profiling determines the evolution and survival of cultural motifs.
4. Large Language Models for Folktale Type Automation based on Motifs (Source 15)
This study demonstrates that Artificial Intelligence (AI), specifically Large Language Models like GPT-4.5, can identify narrative motifs with a 98% success rate compared to human experts. The authors argue that this technology enables large-scale, cross-lingual comparisons that were previously hindered by language barriers and the "bottleneck" of manual annotation. Their primary perspective is that AI is now a reliable tool for recognizing folktale types and identifying subtle subversions within individual narrative variants.
This paper presents a framework for using Geographical Information Systems (GIS) to "deep map" cultural heritage sites. It critiques traditional GIS practices as being "overly positivistic" and abstract. Instead, it proposes a richly layered cartographic method that synthesizes physical geography with historical, literary, and folkloric texts to reveal the "spectral and affective" dimensions of a landscape essentially turning a map from a static image into a database of human memory and storytelling.
1. Media Specificity vs. Traditional Folklore Frameworks
A significant gap exists in how digital horror is analyzed; most current studies privilege a folkloric analysis of spread and variance, treating digital content merely as "remediated folklore". Researchers argue that this approach significantly underplays the impact of digital platforms in shaping narratives and reader interactions. Future research should focus on creepypasta as a born-digital genre of fiction with its own media-specific rules, rather than just a digital version of oral tradition.
2. Limitations in Computational Narrative Analysis
While Artificial Intelligence (AI) has shown high success in identifying motifs, current computational models are limited by manual motif indices that are too specific and fail to reflect the full range of narrative variation. There is a recorded need for:
• Detailed Motif Definitions: Large Language Models (LLMs) interpret words literally, requiring extremely detailed definitions to avoid misclassification (e.g., distinguishing "hurried escape" from general "flight").
• Structural and Affective Attributes: Current systems often lack the ability to code for narrative structures, sentiment detection, or character emotions, which would provide deeper insight into target corpora.
• Data-Driven Typology: There is a call for an automatized, data-driven folktale typology that aligns more closely with actual patterns rather than 19th-century silos.
3. The "Black-Box" of Algorithmic Agency
There is a lack of transparency regarding "algorithmic culture" and how proprietary software agents act as "folklore connectors". Because these processes are "black-boxed" by commercial interests, it is difficult for scholars to trace why certain stories go viral or how algorithms prioritize sensational and confrontational content to maintain user engagement.
4. Psychological and Empirical Impact on Audiences
Despite the popularity of digital-native folklore, there is a dearth of academic literature regarding the psychological effects these stories have on readers. Potential areas for further study include:
• Long-term effects of consuming "ontologically flattened" horror where fiction and reality blur.
• Empirical research into how different demographics perceive the reality status of digital legends.
• Ethical implications of digital folklore in relation to online safety and the potential for individual harm.
5. Geospatial "Abstractness" vs. Intangible Heritage
In the realm of Heritage GIS, many current practices are criticized for being overly positivistic and focusing only on material ruins. A major research gap exists in creating models that do not elide intangible questions of culture, such as the affective experience, cultural memory, and "spectral" dimensions of a landscape.
6. Application to Disinformation and Conspiracies
Scholars suggest that the framework of "ontological flattening" (where fiction and reality exist in the same textual space) should be applied to online conspiracy theories like QAnon. Research is needed to understand how these narratives leverage social media's structure to gain believability and radicalize readers, which could help develop methodologies to counteract disinformation.
Prompt 5: Draft literature review ending with hypotheses and research questions pertaining to this research gap.
The study of traditional expressive culture has undergone a transformative restructuring as the loci of folklore transmission have shifted from "physical campfires to digital servers". This evolution has given rise to computational folkloristics, a sub-discipline that leverages algorithmic approaches like natural language processing (NLP) and machine learning to address classic interpretive problems. While traditional methods relied on "close reading" of small corpora, modern researchers now utilize "distant reading" to identify motifs, themes, and tropes across millions of available texts.
The Emergence of Born-Digital Folklore
Current literature defines creepypasta and Internet memes as the primary artifacts of this digital-native culture. Unlike traditional folklore, which is often viewed as "remediated oral tradition," scholars like Joe Ondrak argue that creepypasta is an emergent genre of digital fiction that derives its affect specifically through its medium. A central concept in this area is "ontological flattening," a state where fictional stories, real user responses, and the platform interface exist in the same textual space without explicit indicators of fictionality. This creates an ontological ambiguity far more effective than traditional print hoaxes, as the "writer is technically a platform-user just like yourself". Similarly, memes are conceptualized as "visual dialects" that reflect contemporary values and shape collective identities through rapid replication and mutation.
AI and Narrative Mapping
The application of Artificial Intelligence has proven highly successful in structural analysis, with models like GPT-4.5 demonstrating a 98% success rate in identifying narrative motifs compared to human experts. These tools allow for cross-lingual comparisons that were previously hindered by language barriers. Parallel to textual analysis, Heritage GIS (Geographical Information Systems) has introduced "deep mapping" to excavate intangible cultural knowledge from landscapes. By integrating historical, literary, and folkloric texts with topographic data, researchers reveal the "spectral and affective dimensions" of heritage sites, such as the Spanish Armada wrecks or "Yeats Country".
The Identified Research Gap
Despite these advancements, the sources highlight a critical research gap regarding media-specific agency. Current frameworks often underplay how digital platforms specifically their proprietary algorithms actively shape the evolution of motifs. These algorithms act as "black boxes" that prioritize sensational or confrontational content to maintain user engagement. Furthermore, while AI can detect structural motifs, there is a recorded inability to code for affective attributes, sentiment detection, or character emotions at scale. Finally, there is a significant lack of empirical data regarding the long-term psychological impact on audiences consuming "ontologically flattened" narratives, which has implications for understanding online radicalization and disinformation.
Hypotheses
• H1: Digital narratives that utilize "ontological flattening" (blurring the line between user and text) result in a significantly higher user belief-persistence than narratives presented with traditional markers of fictionality.
• H2: Proprietary platform algorithms act as selective filters that favor motifs containing "negative stimuli," thereby causing digital folklore to mutate into more "aggressive" or "darker" variants faster than traditional oral legends.
• H3: The inclusion of affective markers (sentiment and emotion) in LLM-based classification will reveal deeper "value-based cognitive matrices" in folk narratives than structural motif-detection alone.
Research Questions
• RQ1: To what extent do the architectural "black boxes" of social media algorithms dictate the survival and viral spread of specific digital motifs over others?
• RQ2: How does the experience of "techno-Weird" narratives where the story itself appears to make contact with the reader affect the reader's baseline trust in digital communication?
• RQ3: Can an automatized, data-driven folktale typology be developed that moves beyond 19th-century manual indices to accurately reflect the hybrid nature of born-digital fiction?
• RQ4: How can the framework of ontological flattening be used to create specific methodologies for counteracting radicalization in "post-postmodern" online conspiracy theories like QAnon?
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