Lab Activity: Gun Island

Hello Learners. I'm a student. I'm writing this blog as a part of thinking activity. This task is given by Dilip sir Barad. so, in which i have tried to some in intresting questions. this blog is based on Gun Island by Amitav Ghosh. (Click here.)

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 : 

                         climate Change | The great Derangement | Gun Island | Amitav Ghosh

                    Based on this video here is infographic and slide desk :

                                                          Infographic:

                                                    Learning Outcome :

This infographic enables learners to understand Amitav Ghosh’s critique of the realist novel and its difficulty in representing climate change within conventional narrative forms. It develops awareness of how anthropocentric, probability-driven storytelling marginalizes ecological crises and limits literary engagement with environmental realities. At the same time, it highlights the potential of climate fiction to overcome these limitations by using imagination, emotional depth, and alternative narrative strategies. Overall, learners gain critical insight into how novels can shape ecological consciousness and connect climate change to human experience in meaningful ways.

                                                        Slide deck

This presentation enables learners to understand how the concept of the uncanny provides a critical framework for interpreting cultural and literary responses to climate change. It develops awareness of how environmental crises disrupt familiar social, cultural, and narrative structures, producing feelings of uncertainty, estrangement, and ecological anxiety. Learners gain insight into the limitations of realist representation and the importance of myth, symbolism, and non-realist narrative strategies in conveying the emotional and ethical dimensions of climate change. Overall, the presentation strengthens the learner’s ability to critically analyze literature and culture in relation to climate consciousness and environmental responsibility.

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 2 | Historification of Myth and Mythification of History | Gun Island | Amitav Ghosh |

                                  

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.

Prompt 2: Which sources are cited or referenced most frequently in this notebook?

The source that appears most often in references throughout the notebook is:

1. Computational Folkloristics (Source 7)

Authors: James Abello, Peter M. Broadwell, and Timothy R. Tangherlini

This work is repeatedly cited by several later studies:

• Joe Ondrak, in “Digesting Creepypasta: Social Media Horror Narratives as Gothic Fourth-Generation Digital Fiction” (Source 9), refers to this article for its concept of “distant reading,” which is presented as an alternative or supplement to traditional close reading methods.

• Tjaša Arčon, Marko Robnik-Šikonja, and Polona Tratnik, in “Large Language Models for Folktale Type Automation Based on Motifs” (Source 15), cite this study when discussing network-based analytical approaches used to map and interpret complex relationships between motifs and folktale types.

• “The Computational Turn in Folkloristics” (Source 17) describes this article as a foundational and influential text, emphasizing its importance in combining computational (algorithmic) techniques with traditional folkloristic interpretation.

• Overall, Computational Folkloristics stands out as a key theoretical and methodological reference that has shaped later research in digital and computational folklore studies.

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.

1. Computational Folkloristics (Source 7)

The authors argue that the digital revolution has rendered traditional "close reading" methods insufficient for the massive scale of modern folklore collections. They advocate for a "distant reading" approach that uses algorithmic methods to analyze motifs, themes, and tropes across systems of lore. Their primary perspective is that folklore should be re-conceptualized as a multimodal network or "hypergraph" that semantically interconnects storytellers, physical locations, and linguistic links, thereby liberating the field from the rigid silos of 19th-century classification schemes.

2. Digesting Creepypasta: Social Media Horror Narratives as Gothic Fourth-Generation Digital Fiction (Source 9)

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.

5. Heritage GIS: Deep Mapping, Preserving, and Sustaining (Source 1)

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.

Prompt 4: Identify ‘Research Gap’ for further research in this area.

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".

At the same time, contemporary studies identify creepypasta and internet memes as central forms of born-digital folklore. Researchers argue that creepypasta derives its power from the digital medium itself, especially through features such as “ontological flattening,” where fiction, user reactions, and platform design coexist in a shared textual environment. Similarly, memes are understood as participatory and rapidly evolving forms of cultural expression that construct shared meaning through replication and variation.

However, despite these important developments, a significant research gap remains. Most studies either focus on computational methods applied to traditional folklore or analyze digital folklore primarily through qualitative and theoretical approaches. There is limited integrated research that systematically combines computational tools with interpretive folkloristic analysis to examine how digital-native genres like creepypasta and memes function structurally, culturally, and emotionally within online ecosystems. Furthermore, the relationship between algorithmic amplification (platform design, recommendation systems) and the evolution of digital folklore remains underexplored.

Literature Review Conclusion, Research Gap, Research Questions, and Hypotheses

The review of existing scholarship demonstrates that folklore studies have significantly evolved with the rise of digital technologies. The shift from oral and print traditions to online platforms has produced new research frameworks, particularly in computational folkloristics. Scholars have increasingly adopted digital methodologies such as natural language processing (NLP), machine learning, and network analysis to examine large corpora of texts. The move from traditional “close reading” to “distant reading” has enabled researchers to detect recurring motifs, narrative patterns, and structural relationships across vast datasets.

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: Computational analysis of digital folklore will reveal recurring structural motifs and narrative patterns comparable to those found in traditional folklore, demonstrating continuity across media forms.

H2: The phenomenon of ontological flattening significantly enhances the emotional engagement and perceived authenticity of creepypasta narratives.

H3: Internet memes operate as dynamic cultural units whose rapid replication and modification strengthen collective identity formation in digital communities.

H4: Algorithmic amplification and platform design significantly influence which digital folklore artifacts gain visibility, longevity, and cultural impact.

Research Questions

1. How can computational folkloristic methods (such as NLP and network analysis) be effectively applied to born-digital folklore genres like creepypasta and internet memes?

2. In what ways does the digital platform environment contribute to the narrative structure and emotional impact of creepypasta through mechanisms such as ontological flattening?

3. How do internet memes function as adaptive folklore forms in shaping collective identity and cultural values within digital communities?

4. What role do algorithmic systems and platform architectures play in influencing the transmission, mutation, and longevity of digital folklore artifacts?