免费硕士论文 Narrative Engineering in the Attention Economy

1. Introduction

The contemporary media landscape is characterized by an overabundance of information, a phenomenon that has given rise to the attention economy [20,38]. In this environment, attention has become a scarce and valuable resource, functioning increasingly as a form of currency that shapes individual engagement and economic exchanges [13,43]. The dynamics of the attention economy, driven by platforms and algorithms that compete intensely for user engagement, significantly influence content creation, dissemination, and consumption across various media [6,31]. Understanding how to effectively capture and sustain this scarce resource is paramount for creators and communicators.

Within this context, narrative remains a fundamental human practice through which individuals make meaning and connect with the world [23,35]. Narrative engineering can be understood as the deliberate design and construction of narrative elements to achieve specific communicative or experiential goals, including the crucial task of capturing and maintaining audience attention in a saturated information environment [1,5,8]. This involves leveraging insights into narrative structure, cognitive processing, and the affordances of different media to craft compelling and engaging experiences [33,40]. Algorithmic systems on social media platforms, for instance, demonstrate how content and interface metrics shape story elements and drive engagement, highlighting a form of data-influenced narrative design focused on attention capture [7].

Narratives manifest across a spectrum of forms, traditionally categorized by length and medium. Literary storytelling, encompassing genres such as novels and poetry, has historically demanded sustained attention and offered deep engagement with complex themes and structures, sometimes framed as a form of resistance against the distractions of mass media [36,41]. However, digital transformations are reshaping literary forms, incorporating non-linear structures, multimodal elements, and interactivity that challenge traditional reading practices and scholarly approaches [12,16,25,32]. Long-form storytelling, prevalent in film, television series, and complex video games, relies on developing intricate plots, character arcs, and immersive worlds over extended durations. Streaming platforms, in particular, have fostered experimentation and richness in serialized narratives, freeing storytellers from traditional broadcast constraints [22,28]. In contrast, short-form storytelling, exemplified by social media posts, short videos, and ‘stories’ on platforms like Instagram and Snapchat, thrives on brevity, immediacy, and often leverages cognitive compression techniques to convey high ideational density rapidly [7,18,39]. Digital media has further blurred the lines, giving rise to networked, interactive, hypermedia, and transmedia narratives that integrate elements across different forms and platforms [11,34].

While existing research explores aspects of the attention economy, computational narrative analysis, and the characteristics of different storytelling forms, there is a notable knowledge gap concerning a comparative analysis of narrative engineering techniques specifically employed across short-form, long-form, and literary storytelling within the attention economy [1,20,24,42]. Much of the literature focuses on the impact of technology on narrative or the economic dynamics of attention independently. There is a need for a systematic comparison of how distinct narrative engineering strategies are applied, adapted, and interact with audience attention mechanisms in these varied forms, considering the unique constraints and opportunities each presents in the current attention-scarce environment. For instance, while the impact of streaming on long-form narratives is documented [22], and comparative analyses of narrative techniques across different media exist [28], these studies often do not deeply integrate the specific pressures and mechanisms of the attention economy as a central comparative lens. Furthermore, understanding how narrative engineering can potentially mitigate the negative impacts of the attention economy, such as promoting more mindful engagement, requires a comparative perspective across forms [24].

Addressing this gap, this survey aims to synthesize existing knowledge to provide a comprehensive comparative analysis of narrative engineering techniques in short-form, long-form, and literary storytelling within the context of the attention economy [22,24,28]. The primary objectives are to: (1) identify and characterize the distinct narrative engineering strategies employed in each form; (2) analyze how these strategies interact with the mechanisms of attention capture and maintenance in the digital environment; (3) compare and contrast the effectiveness and implications of these techniques across forms; and (4) outline future research directions for narrative engineering in the attention economy. By drawing together insights from diverse fields, this study seeks to contribute a clearer understanding of how storytelling adapts and is engineered to thrive, or resist, in the competitive landscape of contemporary attention [36].

2. The Attention Economy: Foundations, Mechanisms, and Impact

The contemporary media landscape is fundamentally shaped by the dynamics of the attention economy, an economic and social paradigm predicated on the scarcity of human attention in an environment characterized by an unprecedented abundance of information [13,20,36,38]. This concept, rooted in early observations about the limitations of human information processing capacity, has evolved significantly with the advent and pervasive integration of digital technologies, which have vastly amplified the volume of accessible content and intensified the competition for limited cognitive resources [24,38]. Theoretical perspectives converge on attention’s status as a valuable resource, viewing it variously as a form of capital [43] or a symbolic currency subject to economic principles and exchange dynamics [13,19].

The operation of the attention economy is heavily mediated by technological mechanisms, primarily algorithms and metrics, which exert significant influence over content creation, distribution, and user exposure [6,7,31]. Algorithms function as powerful gatekeepers and curators, determining content visibility and shaping creator incentives through the structure of traffic allocation [6,31]. This algorithmic governance encourages content creators to adapt their practices to optimize for visibility and engagement. Complementary to algorithmic influence, various metrics (such as likes, views, and shares) quantify attention and performance, structuring narrative elements and serving as visible indicators of success and reputation within digital ecosystems [7,13]. These mechanisms collectively drive a system where capturing and retaining attention becomes paramount.

The pervasive influence of the attention economy profoundly impacts user behavior, attention spans, and consumption patterns [20,24]. Faced with an overwhelming array of options, users engage in highly selective and often fragmented information consumption, leading to observations of reduced attention spans and a tendency towards inattentive engagement [20,24,44]. This environment necessitates that content must be strategically designed to effectively capture and maintain interest. Furthermore, the cognitive impact of constant digital engagement and multitasking is a subject of concern, with implications for cognitive functions and the ability to engage with complex or longer-form content [20,24,36]. Understanding the foundational concepts, operating mechanisms, and multifaceted impacts of the attention economy is crucial for analyzing how narrative forms and engagement are evolving in the digital age.

2.1 Theoretical Foundations and Key Concepts

The concept of the attention economy is fundamentally predicated on the principle that in an information-saturated environment, attention becomes a scarce and valuable resource. This foundational idea is often attributed to Herbert Simon, who prophetically identified attention as the limiting factor in a world overflowing with information [13,20,36,38,43]. Simon’s perspective highlights the psychological reality that the human mind can only process a finite amount of information at any given time, necessitating selective engagement [20]. This scarcity forms the basis for attention’s emergence as a key economic element.

Theoretical frameworks for understanding the attention economy diverge in their emphasis but converge on the centrality of attention. Some approaches view attention primarily as a scarce resource subject to economic principles [19]. This perspective analyzes the dynamics of its allocation, competition, and value in various markets [19]. The idea extends to conceptualizing attention as a form of capital, often termed “attention capital” [43], drawing on thinkers such as Gabriel Tarde, Michael Davenport, Richard Lanham, Georg Franck, and Yves Citton [43]. Georg Franck, for instance, posits that in the competition for attention, vanity serves as a necessary drive to gain recognition and maintain self-esteem [13]. Georg Simmel offers a sociological antecedent, suggesting that the detachment of individuals from traditional social structures contributed to the conditions for the attention economy [13].

Another lens views attention as a currency or commodity, actively traded and commodified within digital ecosystems [36]. This perspective underscores how attention is captured, measured, and exchanged, often for monetary gain through mechanisms like advertising [44]. The competition for this commodified attention can manifest as an “exposure game” on online platforms, where content creators strategically optimize their output to maximize visibility and engagement, akin to search engine optimization [31]. This strategic adaptation leads to a “spiral of attention scarcity,” where the drive to capture attention results in increasingly attention-grabbing content [24].

Some theoretical approaches delve into the cognitive and psychological dimensions of attention within this economy. The dual-stream model, for example, distinguishes between “calcified” attention (attracting attention externally) and “flow” attention (the subjective experience of attention) [13]. Calcified attention can create self-reinforcing feedback loops that attract further attention [13]. Critically, certain accounts caution against simplistic psychological explanations, such as the ‘brain disease’ rhetoric of addiction or a strict dichotomy between top-down and bottom-up attention. These critiques argue for more empirically grounded accounts informed by recent neuroscience and psychology research [9].

Synthesizing these perspectives, the attention economy can be defined as an economic and social system operating within information-rich environments where human attention is conceptualized and treated as a scarce, valuable resource, a form of capital, or a commodity. It is characterized by intense competition among content creators and platforms to capture and retain this limited resource, shaping media production towards attention-optimized formats and influencing consumption patterns through mechanisms like algorithmic filtering and pervasive advertising [23,44]. The implications for media production and consumption are profound; the scarcity of attention incentivizes the creation of engaging, often short-form, content designed for rapid consumption and widespread distribution, while simultaneously highlighting the importance of cognitive capacities like literary interpretation to navigate and critically engage with the information landscape [36]. These theoretical foundations underscore the systemic pressures that shape narrative forms and engagement in the contemporary media ecosystem [13,19,36,43].

2.2 Algorithmic Influence and Metrics

Algorithms fundamentally shape the digital content landscape, acting as critical determinants of content visibility and audience exposure within the attention economy [6,23,31]. These computational systems, including architectures such as factorization and two-tower models, govern the distribution of content, thereby significantly influencing content creator incentives [31]. Research demonstrates that even subtle differences in algorithmic design, such as the choice between non-negative and unconstrained factorization, can profoundly alter the dynamics of content exposure and the resulting equilibrium states for creators [31]. Beyond mere distribution mechanisms, algorithms function as instruments of governance and discipline on platforms, influencing content providers’ behavior through the structure of traffic allocation [6].

In response to the pervasive influence of these algorithmic gatekeepers, content creators develop and employ specific strategies to optimize their content for maximum visibility and reach [6,20]. These strategies often involve adapting content creation practices to “play with” or actively “please” the algorithms that curate user feeds [6]. The goal is to increase the likelihood of their content being selected and promoted by the algorithm to wider audiences, a necessity for success in algorithm-curated environments.

Complementing the influence of algorithms are the various metrics that quantify engagement and performance. These metrics are not merely passive indicators but actively structure storytelling elements, signal and generate engagement, and serve as resources for storytellers’ self-presentation [7]. Studies identify different categories of metrics, including content metrics, interface metrics, and algorithmic metrics, each playing a distinct role in shaping narrative practices [7]. Metrics such as likes, views, followers, and subscribers become readily observable manifestations of attention, demonstrating the immediacy and circularity inherent in the attention flow from consumer to producer [13]. This “calcified” attention acts as a potent signifier of reputation and status, providing tangible evidence of past success in capturing attention and influencing how creators are perceived and present themselves [7,13]. The emphasis on content metrics is further highlighted in the context of automated content analysis, which is often powered by algorithms on social media platforms [15].

A critical concern arising from algorithmic governance is the potential for misalignment between the types of content that are algorithmically incentivized and those that might be considered desirable from other perspectives, such as fostering diverse viewpoints or promoting civic discourse [31]. Algorithms designed primarily to maximize engagement or retention may inadvertently favor sensational or polarizing content. Research suggests that modeling creator behavior in response to algorithmic structures can be a valuable approach to auditing algorithms and identifying such potential misalignments, paving the way for interventions aimed at fostering a healthier content ecosystem [31].

2.3 User Behavior and Attention Spans

In the contemporary digital landscape, characterized by an unprecedented abundance of information, user behavior and attention patterns have undergone significant transformations, giving rise to the concept of the “attention economy” [13,20]. This environment presents individuals with a vast array of content options, leading to a pervasive feeling that available time is insufficient to consume everything desired [13]. Consequently, users are compelled to be highly selective in their information consumption and attention allocation [13,20]. The scarcity of attention, rather than information, has become a defining characteristic of the digital age [20].

Trends in user behavior reflect this shift. Predictions suggest a continuation of elevated media consumption levels and an increasing tendency towards inattentive engagement with information [24]. This fragmentation of attention is evidenced by observations such as smartphone users checking their devices numerous times daily, with typical session durations being remarkably short [44]. This necessitates that content creators compete intensely for a finite pool of user attention, often leveraging algorithmic means to capture and retain engagement [31]. The dynamics of user behavior and engagement can be investigated through the analysis of digital trace data [15].

Beyond consumption patterns, the increased reliance on technology is posited to have implications for cognitive abilities. Cognitive research indicates that multitasking, a common behavior in the digital environment, can lead to reduced efficiency and increased error rates [20]. Furthermore, there are forecasts of an increased reliance on technology for performing mental tasks, potentially impacting fundamental cognitive functions [24]. The digital age also raises concerns regarding the ability of individuals to engage with longer, more complex forms of content, such as extensive literary works, suggesting a potential alienation of attention influenced by market-driven media forms [36].

The insights from this evolving landscape underscore the critical need for strategic narrative design. Given the scarcity of attention and the fragmented, selective nature of user engagement, narratives must be crafted to effectively capture and maintain interest. Motivational states such as boredom and curiosity also play a significant role in how attention is allocated, suggesting that effective narrative design must consider psychological factors influencing engagement [19]. Adaptability in storytelling approaches is crucial to navigate the challenges posed by reduced attention spans and altered cognitive habits, implying that the way stories are structured and disseminated must evolve to resonate with contemporary digital consumption patterns [23].

3. Narrative Engineering: Principles, Elements, and Techniques

Narrative engineering can be understood as the deliberate and strategic construction and manipulation of narrative elements to achieve specific outcomes, such as persuasion, information delivery, or, critically, audience engagement [5]. It is inherently an audience-centered approach [32] and draws upon an interdisciplinary foundation, incorporating principles from design [1], computational approaches to narrative analysis and generation [10,17], and cognitive science examining how narratives are processed [18,33]. A core principle involves maintaining narrative coherence while allowing flexibility across different platforms by separating underlying structure from presentation [30]. Computational intelligence is increasingly used to empower authors in creating engineered narratives, particularly in interactive contexts [21].

Fundamental narrative elements are systematically leveraged and manipulated within narrative engineering. Plot and narrative structure, viewed often as event-centric sequences [14,21], are key. Computational methods are employed to analyze event relationships, temporal flow, and overall structure [10,33], including the identification of narrative tropes which influence progression and meaning [40]. While traditional narratives favor linearity, technological media enable fragmented, interactive, and multimodal structures [16]. Narrative engineering in design context specifically engineers plot and structure to guide users through experiences compellingly [1]. Character development is crucial for engagement and coherence [5], often involving understanding audience needs [1], modeling character involvement in events [10], and adapting character arcs across diverse media like novels, games, and TV series [28]. Digital and VR platforms reconfigure character presentation [12,32], with generative AI offering new possibilities for character creation [4]. Setting is engineered to create atmosphere and enhance narrative, becoming particularly integral in location-based AR experiences [1,5,8]. Symbolism and dialogue are utilized to add layers of meaning, evoke emotion, and manage direct audience communication [1,5]. These elements are often represented using various abstraction methods [29], with digital contexts prompting a shift towards visual and sequential thinking [34]. Elements such as omission, abstraction, and metaphor are also adapted based on medium and desired effect [3].

In the context of the attention economy, narrative engineering employs a diverse array of strategies to capture and sustain audience attention. These techniques are designed to influence audience psychology and boost engagement. Foundational techniques include strategic use of hooks, suspense, emotional appeals, and careful pacing [5,28]. Structural elements like plot development and point of view contribute to creating engaging narratives across media [28]. The leveraging of recognizable narrative tropes is another strategy for audience engagement [40]. Digital media introduces powerful interactive and multi-modal strategies such as interactivity, hypermedia, transmedia storytelling, virtuality, and connectivity [32,34]. Interactivity, through features like choices and adaptive systems, fosters audience agency and deeper investment [12,17,21]. AI-driven personalization and collaborative platforms further enhance engagement [4]. Visual, sensory, and experiential techniques are vital, particularly in digital and immersive formats, utilizing integrated visuals and sound, spectacle, and leveraging physical environments in AR [1,8,39]. Cognitive strategies like compression, conceptual blending, metaphor, framing, omission, and abstraction are employed to make information more accessible and enhance creative engagement by managing informational density [3,18]. Incorporating noncentral events can also contribute to creative ideation and potentially surprise the audience [14]. Innovation in narrative structure, such as using unconventional formats or fragmented narratives, also serves as an attention-grabbing technique [16]. Ultimately, storytelling, through the strategic application of these engineered elements and techniques, functions as a fundamental tool for capturing attention in the competitive attention economy, aiming to create narratives that are not only accessible but also psychologically compelling and deeply engaging [5,21].

3.1 Core Elements of Narrative Engineering

Narrative engineering fundamentally involves the deliberate construction and manipulation of core storytelling elements—namely, plot, character, setting, symbolism, and dialogue—to shape audience understanding, emotional responses, and overall engagement [1,5]. This process extends across various media, adapting traditional narrative theories [37] within computational and digital contexts [12,35]. The Narrative Context Protocol (NCP), for instance, aims to standardize the encoding of these elements into clear, universally understood representations to preserve authorial intent [30].

The construction of plot and narrative structure is a central focus. Narrative engineering often approaches plot through an event-centric perspective, defining “events” as points of notable shifts in location, topic, or time [14]. The importance of an event can be quantified, for example, by its semantic centrality [14]. Computational methods analyze how temporally constructed events combine to build narrative structure [33] and identify interconnected events within narratives, particularly in fragmented online content [10]. Tropes, as recurring narrative devices, are computationally analyzed for their role in shaping story progression and meaning [40]. While traditional approaches often rely on linear progression, digital technologies facilitate fragmented, interactive, and multimodal storytelling that challenges linear structures [16]. Author-driven systems allow laying out specific plot points, with other elements like character animations synthesized to fit this structure [21]. Plot and structure are engineered to guide users through experiences, making the path feel natural and compelling, especially in design contexts like websites or applications [1].

Character development is another critical element, integral to creating engaging and coherent stories [5]. This involves understanding audience desires and behaviors to create representative personas in design [1]. In computational narrative, characters are often represented through their involvement in events and their evolution through time [10]. Some approaches prioritize characters’ emotional states, particularly in interactive media like games, suggesting a focus on character emotion over purely plot-driven narratives [17]. Across different narrative forms, character treatment varies significantly; novels often rely on a protagonist’s journey to drive the narrative and engage readers emotionally, while game narratives leverage player interaction to shape engagement and immersion, and TV series utilize character arcs for building emotional connections over time [28]. Generative AI advancements have expanded capabilities for creating complex characters [4]. Digital and VR narratives are noted for reconfiguring how characters are developed and presented [12,32].

The setting and environment are engineered to create an atmosphere that captivates the audience and enhances the narrative [1,5]. In location-based augmented reality experiences, the physical setting becomes a crucial element, directly driving the plot and user interaction [8].

Symbolism is utilized to add layers of meaning and evoke specific emotions, often through visual elements like colors, shapes, and imagery, particularly in digital design [1,5]. Dialogue serves as a direct channel of communication with the audience, requiring a balance between conveying information and maintaining engagement [1,5].

Narrative engineering employs various abstraction methods (continuous, unit-based, discrete) to represent these elements [29]. Digital contexts necessitate a re-evaluation of classical narrative conceptions [34], prompting shifts towards visual and sequential creative thinking [34]. Elements like omission, abstraction, and metaphor are also considered core elements, adapted and emphasized based on the medium and desired outcome [3]. While the detailed comparison of how these elements are treated across short-form, long-form, and literary storytelling is beyond the scope of this specific subsection, it is clear that narrative engineering tailors the emphasis and implementation of plot, character, setting, symbolism, and dialogue to suit the specific medium and objectives, aiming to maximize audience understanding, emotional resonance, and engagement [1,5].

3.2 Strategies for Attention Capture and Engagement

In the fiercely competitive environment of the attention economy, effectively capturing and retaining audience attention is paramount for narrative forms across various media. Researchers have identified a diverse array of narrative techniques employed to influence audience psychology and enhance engagement levels. These strategies span traditional structural elements, leveraging cognitive processes, and exploiting the unique capabilities of digital and interactive media.

Fundamental narrative techniques play a crucial role in initially capturing attention and sustaining it. These include the strategic use of narrative hooks at the outset, building suspense, and employing emotional appeals to create a compelling experience for the audience [5]. Pacing and overall structural design are also critical for maintaining audience engagement throughout the narrative arc, governing the flow and intensity of the story [5,28]. Effective pacing involves adjusting tempo, structuring scenes deliberately, and utilizing techniques such as cliffhangers and resolutions to manage anticipation and provide emotional closure [28]. Furthermore, understanding and skillfully utilizing narrative tropes can serve as a strategy for engaging audiences, as these recognizable elements often resonate deeply with readers, tapping into shared cultural or literary understanding [40].

The advent of digital media has introduced a wealth of interactive and multi-modal strategies for attention and engagement. Key among these are interactivity, hypermedia, transmedia storytelling, virtuality, and connectivity [32,34]. Interactivity, in particular, enhances engagement by requiring reader participation and decision-making, transforming the audience from passive recipients into active agents within the narrative world [12,17]. Offering choices at pivotal story points enables reader agency, fostering a deeper sense of investment [17]. The development of authoring tools that facilitate the creation of such interactive and event-centric narratives also supports enhanced engagement [21]. Furthermore, digital technologies allow for multimodal interaction, combining gestural, verbal, and various media elements to create adaptive narrative systems that can respond dynamically to user input [17]. The capacity of artificial intelligence to adapt narratives based on individual user preferences offers unique and personalized experiences, serving as a potent strategy for sustained attention and engagement [4]. Collaborative storytelling platforms, where readers can interact and contribute, also demonstrate improved engagement levels [4]. Interactive methods for generating narrative structures, whether manual, AI-assisted, or fully automatic, can also contribute to creating more engaging and potentially attention-grabbing narratives [29].

Beyond traditional and interactive structures, visual, sensory, and experiential techniques are vital, particularly in digital and immersive formats. Visual storytelling, integrating visuals and sound harmoniously, creates immersive experiences [1]. Emotional appeals can be amplified through visual cues and design elements [1]. Techniques like spectacle, designed to create moments of awe or surprise, are employed to elicit strong emotional responses and capture attention [1]. In virtual reality, the interactive and multi-sensory nature inherently drives audience participation and engagement [32]. Location-based augmented reality applications can leverage the physical environment as a narrative hook, drawing users in and maintaining their attention through seamless AR interaction [8]. For short cultural films, the effective use of digital resources, flexible filming techniques, and advanced video editing are highlighted as methods to enhance engagement [39].

Cognitive strategies also play a significant role in capturing and maintaining attention by influencing how information is processed and encoded. Techniques such as conceptual blending, metaphor, framing, omission, and abstraction are considered effective for encoding and decoding creative compression in narratives, thereby capturing and maintaining attention [3,18]. Integrating less obvious or noncentral information into the narrative has been shown to enhance creative ideation, suggesting that departures from the dominant narrative flow can contribute to originality and potentially engagement by surprising or challenging the audience [14]. Narrators engage audiences by constructing narratives through specific informational methods, highlighting the interaction between rational and sensitive aspects of human information processing [33].

Furthermore, innovation in narrative structure and format can serve as attention-grabbing strategies. Examples from literary fiction include incorporating atypical elements like PowerPoint presentations, utilizing unconventional typography and multi-layered narratives, and employing fragmented structures inspired by serial storytelling [16].

Implicit across many of these techniques is the importance of cultural awareness and a broad outlook for capturing attention, particularly in cultural contexts [39]. Ultimately, a deeper understanding of narrative structure and elements, often facilitated by computational analysis, underlies the effective deployment of these diverse strategies for storytelling and engagement [10,37]. The core aim of these varied approaches is to navigate the challenges of the attention economy by crafting narratives that are not only accessible but also psychologically compelling and deeply engaging [21].

4. Comparative Analysis of Storytelling Forms

Feature	Short-Form Storytelling	Long-Form Storytelling	Literary Storytelling
Length	Extreme brevity, rapid consumption	Sustained attention over extended duration	Depth, reflection, immersive engagement
Techniques	Cognitive compression, efficient visuals	Complex character arcs, intricate plots	Nuanced language, complex symbolism
Priorities	Brevity, accessibility	Depth, complexity, entertainment	Artistic expression, interpretive value
Attention Economy	Maximizing impact through efficiency	Maintaining engagement across time	Counterpoint to fragmented digital attention

Narrative engineering within the attention economy necessitates distinct strategies tailored to the affordances and constraints of various storytelling forms [20,27,28,41]. This section provides a comparative analysis of three prevalent forms: short-form, long-form, and literary storytelling, examining their inherent characteristics, the adaptation of narrative techniques, and the inherent trade-offs among brevity, depth, accessibility, complexity, entertainment, and artistic expression.

Short-form storytelling is defined by extreme brevity and rapid consumption, heavily relying on cognitive compression to condense complex ideas, emotions, and narrative elements into minimal temporal and spatial confines [3,18]. Narrative engineering here focuses on maximizing impact through highly efficient visual and textual communication, leveraging digital resources, flexible filming, and sophisticated editing to create immediate engagement [39]. Platforms and algorithms significantly shape short-form narratives, favoring visually-textually hybrid works optimized for rapid digestion and shareability, with metrics influencing content creation and audience interaction [6,7,16].

In contrast, long-form storytelling demands sustained audience attention over extended durations, representing a significant investment within the attention economy [36]. Narrative engineering for long-form narratives emphasizes the development of complex character arcs and intricate plotlines to maintain engagement across time [22,40]. The advent of streaming services has particularly influenced long-form storytelling, enabling enhanced serialization, intricate plot complexities unfolding over multiple seasons, and fostering binge-watching culture through structural techniques like frequent cliffhangers [22]. Platform features and community engagement also contribute to the overall consumption ecosystem for long-form content [7].

Literary storytelling distinguishes itself by prioritizing depth, reflection, and immersive engagement, often serving as a counterpoint to the fragmented attention prevalent in the digital age [36,41]. It employs nuanced language, complex symbolism, and multifaceted thematic exploration to encourage sustained, profound interpretive engagement, posited as an “antidote to mass media distraction” [36]. While rooted in traditional forms, literary storytelling is increasingly integrating digital elements, experimenting with non-linear narratives, multimodal formats, and leveraging technologies like VR and AI to create interactive, multi-sensory experiences and explore themes of technology and simulation [12,16,17,32]. Discussions of electronic literature and expanded literature highlight the evolving nature of literate culture [41]. Furthermore, parallels are drawn between immersive digital practices and traditional narrative structures found in Indigenous cultures, suggesting alternative models for narrative engagement [25].

Comparing these forms reveals distinct approaches to narrative engineering driven by their fundamental characteristics and the demands of the attention economy [20,27,28,41]. While concepts like compression exist across forms, their application and cognitive processing differ; for example, the specific cognitive strategies used in micronarratives are uniquely crucial in brief formats [3,18]. Narrative elements such as character development vary significantly: novels offer deep psychological dives, game narratives incorporate player agency, and streaming series develop complex arcs over multiple installments [22,28]. Digital storytelling, encompassing many contemporary short-form and long-form narratives and evolving literary forms, contrasts with traditional storytelling through its reliance on multimedia, varied interaction levels, and digital platforms for accessibility [27]. The influence of platforms and metrics, including content, interface, and algorithmic measures, exerts reciprocal influences on narrative strategies across digital forms [7]. A trend towards convergence is observed, blurring the lines as storytellers adapt to new technological environments [34].

The trade-offs inherent in these forms are central to narrative engineering in the attention economy. Short-form storytelling excels in brevity and accessibility, optimized for fleeting attention but often sacrificing depth and complexity [3,27]. Long-form allows for greater depth and complexity, demanding more sustained attention but potentially offering higher entertainment value through intricate plots and character development [22,28]. Literary storytelling, while potentially less immediately accessible or universally entertaining in a mass-market sense, prioritizes artistic expression and reflection, cultivating a different type of attention focused on deeper engagement and interpretive value, offering insights into fundamental valuation mechanisms beyond mere market metrics [36,41]. Thus, the choice and engineering of narrative within these forms represent strategic decisions balancing the demands of audience attention, medium affordances, and the intended depth and purpose of the storytelling [20,27,28,41].

4.1 Short-Form Storytelling

Short-form storytelling represents a prominent narrative mode within the attention economy, characterized by its extreme brevity and rapid delivery [3,18]. A defining characteristic is the reliance on cognitive compression, where ideas, emotions, and narrative arcs are condensed into minimal space and time [18]. This compression is not merely a constraint but functions as a key creative tool, concentrating meaning to achieve narrative impact despite limited duration [18]. The inherent brevity is posited to contribute directly to engagement, aligning with the rapid consumption patterns prevalent in digital environments [3].

Prevalent narrative techniques in short-form storytelling often involve highly efficient visual and textual communication. Studies examining short videos, for instance, highlight the extensive use of digital resources, flexible filming methods, and sophisticated video editing to convey cultural narratives or personal subject matter [39]. These techniques facilitate the incorporation of diverse elements, from global cultural perspectives to intangible cultural heritage, within constrained formats [39]. The effectiveness of strategies for capturing attention quickly within these limited timeframes hinges on the ability to immediately establish narrative hooks or visually striking content, leveraging the condensed nature of the medium [3].

Despite the challenges posed by brevity, conveying meaning or narrative impact is achieved through strategic compression and the concentration of core ideas [18]. This requires authors and creators to distill complex themes or emotional resonance into their most potent forms, relying on viewers’ cognitive abilities to infer and connect elements rapidly.

The aesthetics and consumption of short-form narratives are significantly shaped by the platforms and algorithms that host them [27]. Platforms like Instagram and Snapchat demonstrate how metrics, which often inform algorithmic promotion, heavily influence the type of content produced and how users engage with it [7]. The design of these platforms favors visually-textually hybrid works and formats that are easily digestible and shareable, as exemplified by minimalist poetic forms adapted for social media aesthetics [16]. Furthermore, the content ecosystem on large-scale platforms, such as the traffic media platforms prevalent in China, inherently accommodates and promotes short-form narratives, reinforcing their centrality in contemporary media consumption [6].

4.2 Long-Form Storytelling

Long-form narratives, encompassing novels, multi-season television series, and feature films, represent a significant investment of audience attention, often characterized as a “luxury” within the attention economy [36]. Sustaining engagement over extended durations necessitates sophisticated narrative engineering strategies. Key among these are the development of complex character arcs and intricate plotlines, which are fundamental to retaining audience interest across lengthy narratives [22,40]. Literary examples, such as Dave Eggers’ The Circle or Patricia Lockwood’s No One Is Talking About This, demonstrate how contemporary novels utilize complex thematic exploration of digital life to engage readers [16]. The analysis of formats like movie scripts further illustrates the prevalent storytelling techniques employed to maintain audience captivation throughout a production’s runtime [40].

The advent of streaming platforms has significantly reshaped the landscape of long-form consumption and narrative construction, particularly through enhanced serialized storytelling [22]. Unlike traditional episodic broadcasts, streaming facilitates the development of intricate plot complexities and nuanced character trajectories unfolding over multiple seasons [22]. Series such as “The Crown” exemplify this, leveraging the serialized format afforded by streaming to delve deeply into historical events and character dynamics [22]. Furthermore, the culture of “binge-watching” cultivated by these platforms influences creators to structure narratives more akin to extended films, incorporating frequent cliffhangers and tightly interwoven narrative threads to encourage continuous viewing [22]. The success of “Breaking Bad,” revitalized through streaming, highlights how a tightly woven narrative and deliberate character development can be particularly effective when consumed in marathon sessions [22].

Beyond structural techniques, platform-specific features and community engagement also play a role in enhancing long-form consumption. Platforms hosting long-form content, like Reddit, demonstrate how metrics and the dynamics of online communities can influence content creation and audience engagement [7]. While direct narrative interactivity might be less prominent in traditional long-form, the social and interactive layers provided by platforms and associated communities contribute to the overall engagement ecosystem surrounding these narratives [7].

4.3 Literary Storytelling

Literary storytelling distinguishes itself through its capacity to foster depth, reflection, and immersion, offering a counterpoint to the often fragmented engagement characteristic of the attention economy [36]. Unlike forms optimized for rapid consumption, literary narratives typically utilize nuanced language, complex symbolism, and multifaceted thematic exploration to engage readers intellectually and emotionally, encouraging sustained attention and profound interpretive engagement [36]. This focus on deep reading and sophisticated textual analysis can be seen as a deliberate “antidote to mass media distraction,” providing “deeper knowledge about the fundamental mechanisms of valuation” and prioritizing reflective engagement over market-driven metrics [36].

In response to the pressures and possibilities of the digital age, literary forms are both adapting and asserting their distinct values. Some authors integrate digital elements directly into narrative structures, such as Jennifer Egan’s use of a PowerPoint presentation or Mark Z. Danielewski’s experimental typography, demonstrating how digital formats can influence narrative shape and visual presentation [16]. Digital narratives are emerging as formally innovative literary forms that simultaneously reflect upon and comment on human experience, offering new avenues for artistic expression [12]. Examples like Shelley Jackson’s “Patchwork Girl” illustrate how literary works experiment with structure in digital environments, often building upon or reinterpreting traditional narratives [12]. Discussions surrounding electronic literature, technopoetics, and “expanded literature” highlight the stimulating challenges these forms present for the evolution of literate culture in the 21st century [41]. This engagement includes critiquing established literary norms, such as the traditional “world literature” framework which may exclude narratives that do not conform to specific systematic rules, contrasting these with the immediacy and interactivity often valued digitally [25].

Furthermore, emerging technologies such as Virtual Reality (VR) and Artificial Intelligence (AI) are significantly impacting literary creation and reception. VR and other digital narratives challenge traditional methods of literary analysis and interpretation, opening up novel pathways for investigating meaning within literary works, particularly in contemporary English literature [25,32]. The incorporation of digital interactivity into storytelling practices directly influences the dynamic relationship between artistic production and reception [25]. AI is also beginning to play a role, notably in genres like Literature Role Playing Games (LitRPG), where AI may control elements of the ongoing story and non-player character behaviors [17]. While not always interactive in the traditional gaming sense, LitRPG serves as a literary space to explore ethical dimensions, methods of interaction, and the interface between human characters and technological systems, potentially inspiring real-world technological development [17]. This demonstrates how literary forms engage with technology not only structurally but also thematically, prompting reflection on the implications of simulation and artificial intelligence [17]. Thus, literary storytelling in the attention economy navigates a complex landscape, leveraging its inherent characteristics of depth and reflection while simultaneously experimenting with and critiquing the digital tools and environments that define contemporary media consumption [16,32,36].

4.4 Comparison Across Forms

A comparative analysis of narrative engineering across short-form, long-form, and literary storytelling reveals both shared principles and distinct strategies shaped by medium affordances and constraints, particularly within the context of the attention economy [20,27,28,41]. While principles like compression are applicable across formats, the specific ways they are implemented and processed cognitively differ significantly [3]. For instance, compression strategies such as conceptual blending and metaphor play a unique and crucial role in micronarratives, highlighting their effectiveness in brief formats compared to their potentially different application in more expansive, longer forms [18].

Differences in narrative strategies are evident in fundamental elements like character development and world-building [28]. Novels typically offer deep dives into inner thoughts, enabling nuanced character portrayal over extended narratives [28]. Game narratives incorporate player agency as a core mechanism for character engagement, allowing interaction to shape narrative arcs [28]. Television series, especially within the flexibility of streaming platforms, can develop complex character arcs and detailed worlds over multiple episodes, unconstrained by traditional time slots, thereby influencing pacing and depth [22,28]. The shift to streaming also facilitates diverse content for niche audiences and global narratives, enriching the storytelling landscape [22].

Traditional storytelling forms, relying on spoken or written words and direct interaction, contrast sharply with digital storytelling, which leverages multimedia elements, often limited or indirect interaction, and digital platforms for accessibility [27]. Digital formats prioritize immersive, visually stimulating experiences and benefit from editing and refining capabilities not present in traditional, often improvisational, forms [27]. This technological shift fosters adaptations like non-linear narratives and multimodal elements, though the unique incorporation of digital elements varies by form [16]. Examples include the distinct reading behaviors and interface designs of Storyspace hypertexts versus network-based narratives and web browser reading experiences, or the difference between interactive fiction requiring typed responses and hypertext fictions relying on link choices [12]. Furthermore, metrics derived from social media platforms, including content, interface, and algorithmic measures, exert reciprocal influences on storytelling strategies in these digital spaces [7]. Interestingly, certain aspects of emerging digital formats, particularly their immersive and interactive practices, find parallels in traditional, hierarchical storytelling structures found in Indigenous cultures [25].

In the attention economy, the balance between brevity and depth, accessibility and complexity, and entertainment and reflection becomes critical. Short-form narratives excel in capturing fleeting attention through compression and readily accessible multimedia [3,27]. Long-form narratives, while demanding more sustained attention, can build greater depth and complexity [22,28]. Literary storytelling, in contrast to mass media, is posited to demand a fundamentally different type of attention, one that fosters deeper engagement and reflection, potentially serving as a counterpoint to the superficiality often associated with the attention economy [36,41]. The value derived from literature is suggested to be distinct, offering deeper insights into valuation mechanisms [36]. Across digital platforms, there is a trend towards convergence, pushing beyond classical conceptions of narrative as storytellers adapt to new technological environments and audience behaviors [34].

5. Technology and the Transformation of Narrative

Digital technologies and platforms exert a pervasive influence on contemporary storytelling practices, fundamentally reshaping how narratives are created, distributed, and consumed [11,16,19,23,27,38,42]. This transformation is driven by features inherent to digital environments, such as algorithmic curation, data-driven processes, and the affordances for interaction and sharing [6,7,10,13,15,16,19,20,31,38]. These dynamics contribute to shifts in narrative structures, often favoring fragmented and non-linear forms [10,16], and redefine the relationship between creators and audiences.

A central aspect of this transformation is the integration of interactivity and social sharing into narratives, profoundly impacting user engagement and experience [1,8]. Digital media facilitates a significant shift from passive consumption to active user participation and co-creation, allowing users to influence narrative progression [1,11,12,25,34]. The design of interactive elements and careful narrative structuring demonstrably enhances user experience, immersion, and overall engagement in digital contexts [8,21,34]. Immersive technologies, such as Virtual Reality (VR) and Augmented Reality (AR), play a crucial role in creating new forms of narrative experience by enabling multi-sensory and location-based digital stories that further blur the lines between the narrative world and the user’s reality [8,32].

Concurrently, the growing field of computational narrative utilizes computational techniques, including Natural Language Processing (NLP), Machine Learning (ML), and Artificial Intelligence (AI), for both analyzing existing narrative structures and generating new content [2,5,33]. Computational narrative analysis employs algorithmic methods to extract, interpret, and model narrative features from large datasets, providing quantitative insights into structure, sentiment, and patterns like tropes [5,10,14,15,26,29,37,40]. Complementary approaches use fMRI and computational models to understand neural responses to narratives and creator incentives in algorithmic environments [14,31]. Computational narrative generation leverages AI and ML to create narrative elements, plots, characters, and dialogue, aiming to automate or assist the storytelling process [2,4,5,17,21,37]. This includes developing systems for interactive narrative design and supporting collaborative authoring [21,29,30].

Collectively, these technological advancements create new possibilities for narrative creation and consumption, enabling data-driven insights [23,42], interactive experiences [1,8], and computationally assisted or generated stories [2,4]. However, they also introduce challenges related to fully capturing narrative nuance and emotional depth computationally, bridging the gap between narrative analysis and generation, maintaining authorial intent in AI-driven environments [30], and navigating the complexities of the attention economy inherent in platform-based distribution [13,19,20]. These intersecting developments underscore the profound impact of technology on the evolving landscape of narrative.

5.1 Computational Narrative: Analysis and Generation

Computational narrative encompasses a diverse range of approaches aimed at both analyzing existing narratives and generating new ones using computational techniques. The field draws heavily on advancements in natural language processing (NLP), machine learning (ML), and artificial intelligence (AI) to process, understand, and create complex narrative structures across various forms [2,5].

Computational narrative analysis employs algorithmic methods to extract, interpret, and model narrative features from textual and visual data. Various techniques are utilized, including automated textual and visual content analysis, which allows for systematic examination of large datasets [15]. Text mining techniques are applied to understand trends and methodologies within the research field itself [26]. More specific approaches involve the extraction of narrative elements, sentiment, and structural components [5]. NLP techniques, such as summarization, commonsense inference, and event detection, are fundamental to processing narrative text and can be enhanced by integrating theoretical frameworks from narratology [37]. Computational methods also enable the identification of specific narrative patterns, such as tropes, through algorithms trained on annotated corpora, aiming to automatically predict these elements within texts [40]. Furthermore, computational techniques are applied to visual analytics of narrative structure, covering data sources, abstraction methods, and visual design [29]. Goals for computational narrative understanding extend to addressing challenges in processing multi-modality, temporal patterning, and socio-cultural schemas [10,35]. While not strictly computational analysis of text, related computational methods using fMRI and techniques like intersubject pattern correlation (pISC) and representational similarity analysis (RSA) are employed to study neural responses to narratives, providing insights into audience perception at a cognitive level [14]. Computational modeling, such as “exposure games,” can also analyze creator incentives and algorithmic impact on content diversity and bias within narrative ecosystems, simulating strategically produced content [31]. The strength of these analytical methods lies in their capacity to process vast quantities of data, identify subtle patterns potentially missed by human analysis, and offer quantitative insights into narrative structure and dynamics. However, limitations exist in fully capturing the nuanced meaning, emotional depth, and cultural context inherent in complex narratives, although integrating narratological theories can help mitigate some of these challenges [37].

Computational narrative generation focuses on using AI and ML to create narrative content, ranging from assisting authors to automating the process entirely [5,33]. Generative AI models learn from extensive datasets of existing narratives, including novels, scripts, and short stories, to acquire an understanding of structure, dialogue, and themes [4]. Techniques employed include story grammars, unsupervised learning of narrative event chains, frame semantics, plot generation based on Case-Based Reasoning (CBR), planning-based approaches, Markov models, grammars, schema-based approaches, and various forms of case-based reasoning [2]. These methods support the generation of narrative elements like plot, characters, and dialogue [5]. Specific applications include the automatic generation of animation content, where tools like CANVAS utilize AI to synthesize 3D animations based on author-defined constraints and plot points [21]. The Narrative Context Protocol (NCP) provides a structured schema for representing narrative context, acting as a tool for generation systems to align output with authorial intent [30]. The potential of computational generation is significant, enabling the unlocking of new content and the discovery of connections and patterns beyond immediate human awareness, thereby expanding creative possibilities [23,42]. AI assists in creative processes, potentially alleviating the “authorial bottleneck” by automating parts of the writing process [2]. Challenges in narrative generation include the need for further investigation into areas like automated spatial generation and the development of standard computational models [17]. Handling narrative generation in unknown domains presents difficulties, sometimes addressed through methods like crowdsourcing [17]. There is also a recognized gap between computational narrative analysis and generation, with efforts underway to propose mappings between models to bridge this divide [2]. The rise of generative AI also introduces broader considerations regarding its impact on creative economies and the economics of attention [19]. While AI can generate coherent text, ensuring the creation of compelling, original, and deeply resonant narratives that fully capture human experience remains a key challenge.

In summary, computational narrative research has developed sophisticated techniques for both analyzing narrative structures and generating content. Analysis methods leverage NLP, machine learning, and specialized algorithms to extract, model, and visualize narrative features, providing quantitative insights but facing challenges in capturing qualitative depth. Generation techniques employ various AI models and algorithms to create plots, characters, and dialogue, holding immense potential for augmenting creativity and exploring new narrative spaces, yet grappling with challenges related to complexity, control, and achieving true narrative artistry [10]. Bridging the gap between understanding narratives computationally and effectively generating them remains a central goal.

5.2 Digital Platforms, Interactivity, and User Experience

Digital platforms fundamentally reshape narrative structures and consumption patterns, moving beyond mere storage and distribution to act as dynamic channels for communication and interaction [13]. These platforms influence the landscape of attention, simultaneously attracting it and contributing to its scarcity within the attention economy [13,19]. Online environments, such as social media, blogging websites, and streaming services, serve as primary delivery mechanisms for contemporary narratives [10,22]. Features inherent to these platforms, such as algorithmic curation and the affordances of shared content, significantly influence how stories are created, distributed, and consumed [6,12,15,16,31]. This includes encouraging fragmented and non-linear narrative structures that mirror the discontinuous nature of online interaction [10,16]. Furthermore, platforms facilitate the global dissemination of stories and connect audiences across geographical boundaries [11,22,23], while also shifting audience focus from the core text to surrounding paratextual elements like opinions and interpretations [44]. The economic models of many platforms, often based on selling user attention to advertisers, introduce complexities related to data collection, surveillance, and the potential for manipulating user behavior, which implicitly impacts the context of narrative consumption [20].

A central transformation in digital storytelling is the increasing importance of interactivity and user agency [25,34]. Digital media facilitates a shift from passive narrative consumption to active user participation and co-creation [1,11]. Users can transition from being mere viewers to active participants whose actions directly influence the story’s progression [1,12]. This includes direct audience participation in immersive environments like VR [32] and instances where readers become characters within the narrative, as seen in examples like “Online Caroline” [12]. The emphasis on immediacy and interactivity is a defining characteristic of digital narratives, contrasting with traditional, less dynamic formats [25].

The integration of interactivity and careful narrative design is critical for enhancing user experience, immersion, and engagement [21,34]. Research demonstrates a direct relationship between well-designed narrative elements and the quality of the user experience in digital contexts [8]. For instance, a study on a location-based AR application found that a compelling narrative significantly improved user engagement and overall experience [8]. Gamification techniques and interactive storytelling applied in web and app design leverage user actions to alter narrative paths and foster engagement [1]. The design of platforms and the metrics they emphasize also influence user experience and storytelling practices, as illustrated by case studies on platforms like Instagram, Reddit, and Snapchat [7]. Interactive methods for narrative generation, ranging from manual to AI-assisted and AI-driven approaches, are directly tied to shaping user experience [29].

Creating compelling interactive and co-creative narratives requires specific design considerations. This involves providing authors with computational tools that enhance their capacity to construct engaging and interactive experiences [21]. Design must facilitate user choices and enable transformative experiences where narratives evolve based on input [21]. Co-creation is supported through interactive elements that enable users to contribute to the narrative outcome [1,12]. Collaborative storytelling platforms and systems, such as the Narrative Context Protocol (NCP), aim to standardize narrative data exchange and support transparent tracking of revisions and collaborative decision-making, enabling collective narrative construction [4,30]. The application of generative algorithms can lead to adaptive story worlds where narrative elements and characters respond dynamically to player or user interaction [17]. Future developments suggest narratives will increasingly position readers as active participants, blurring the distinction between creators and consumers through interactive and personalized experiences potentially enabled by AI [4,27].

6. Creator Perspectives, Authorial Control, and Ethics

The evolving media landscape and the dynamics of the attention economy present significant challenges and necessitate adaptations for story creators [34]. Creators must navigate the algorithmic nature of digital platforms and the increasing influence of data in shaping narratives [23]. This involves strategically adapting content and distribution methods to align with algorithmic incentives, a perspective highlighted by studies modeling the behavior of independent content providers [6,31]. The lack of established guidelines often forces creators to adopt trial-and-error approaches [34]. Furthermore, creators incorporate network-specific communication technologies into their work, encountering challenges in blurring the lines between fiction and reality in narratives mimicking self-representation [12]. Adapting narratives across different media, such as balancing gameplay and storytelling or translating novels to screenplays, requires maintaining the core essence of the original narrative [28]. While digital storytelling empowers new voices, it simultaneously challenges traditional notions of narrative authority [11]. The impact of platform metrics, such as views, shares, and likes, also affects creator expression and potentially limits authorial control by influencing what gets produced and circulated [7]. The negative aspects of the attention economy manifest as “getting cancelled” or “deplatforming”, representing a collective withholding of attention as punishment [13]. For live streamers, concepts like “playbour”, “aspirational work”, and “hope labour” underscore the often uncompensated labor and difficulty in accumulating attention [13]. Despite these pressures, the importance of authenticity and inclusivity in crafting genuine, relatable, and representative stories remains a key perspective in design and storytelling [1]. Developing authoring aids is seen as crucial for helping storytellers adapt to the digital age [5,21].

The complexities surrounding authorial control and narrative coherence are amplified in emerging forms like multi-agent systems and AI-assisted narratives. Traditional notions of a sole, authoritative author are challenged by interactive fiction and reader participation [16]. In computational narrative systems, authorial agency is distinguished from player and system agency and is limited by how the system encodes and interprets the author’s template [17]. Maintaining narrative coherence becomes challenging, particularly in automated synthesis, requiring tools like event-centric representations [21]. The concept of the “authorial bottleneck problem” in computational narrative generation highlights the difficulty creators face, suggesting that bridging narrative analysis and generation could offer more control over automatically generated content [2]. The subjective nature of identifying narrative elements like tropes also implicitly impacts authorial control [40]. A proposed solution to address these issues, particularly in AI-driven storytelling, is the Narrative Context Protocol (NCP). The NCP aims to preserve the integrity and originality of creators’ contributions, ensure clear attribution and rights tracking, and fundamentally protect authorial decisions and intent [30]. AI tools are also being developed with the specific goal of empowering storytellers and enhancing authorial control over interactive narratives [21].

The use of narrative engineering techniques in the attention economy raises significant ethical implications. These techniques can be employed to capture and maintain attention, influence behavior, and potentially generate biased or misleading content [9,10,20,24]. The manipulation of attention, predatory advertising, misinformation, and political manipulation are key concerns [20,24]. Data-driven insights can target vulnerable individuals, raising questions of fairness, privacy, and responsible data use [20]. Algorithms contribute to these concerns by potentially creating “filter bubbles” [23], exerting classifying and disciplining power over content [6], and leading to misalignment between desirable content and content incentivized by algorithmic systems [31]. Implicit ethical considerations also arise from data privacy and the potential for misinterpreting social media content through analysis [15].

Specifically, the integration of generative AI into storytelling introduces ethical concerns regarding originality, ownership, and bias in generated content [4,21,40]. The potential for AI to perpetuate biases from training data underscores the importance of using diverse datasets [4,21,40]. Other ethical risks include the devaluation of facts, the rise of fake news [44], and the use of narratives as strategic tools for propaganda or manipulating user interpretations [10].

Addressing these issues necessitates considering the ethical responsibilities of various stakeholders. Creators have a responsibility for authenticity and inclusivity [1] and must navigate the ethical challenges of blurred fiction/reality [12]. Platforms and researchers bear responsibility for the ethical implications of algorithmic governance and content distribution [6,31]. Mitigating bias in AI systems requires human scrutiny to ensure resonance and coherence [4], the use of diverse datasets [4], and potentially pre-deployment audits for algorithmic curation systems [31]. Advocating for resistance to manipulative attention economy practices and promoting public values in internet regulation are also highlighted as essential steps [24]. Protecting creator integrity, attribution, and intent through mechanisms like the NCP is crucial for ethical AI-driven storytelling [30].

7. Conclusion and Future Directions

This survey has explored the complex interplay between narrative engineering, the attention economy, and technological advancements, offering a comparative perspective across short-form, long-form, and literary storytelling [4,28,29]. The findings underscore that storytelling is undergoing a profound transformation, driven by the dynamics of attention scarcity and the capabilities of digital technologies, particularly artificial intelligence [4,13,19]. Digital narratives are evolving beyond classical conceptions, with visual and sequential thinking becoming dominant, and incorporating elements like interactivity, hypermedia, and transmedia [16,34]. Short-form narratives, or micronarratives, exemplify the cognitive strategies of compression as a hallmark of creativity in the digital age [3,18]. Meanwhile, platforms like streaming services have fundamentally altered the structural possibilities for longer narratives [22]. The rise of immersive formats like VR and AR further transforms how stories are told and experienced, enriching user quality of experience through narrative setup [8,32].

The emergence of the attention economy signifies a broader shift where attention functions as a prime medium of exchange [13,19]. In this landscape, audiences are increasingly viewed as “producers of attention” [13], a scarce resource sought after by platforms and content providers. Algorithmic governance plays a crucial role, shaping content creation incentives and distribution channels [6,31]. This environment can lead to a scenario where discourse about a text becomes prioritized over the text itself, potentially weakening both the narrative and the individual’s engagement with it [44]. The transformative power of data and machines in spreading stories globally emphasizes the need to evaluate and contextualize this information effectively [23,42].

Generative AI is poised to revolutionize narrative creation and consumption, promising endless creativity, personalized experiences, and content efficiency [4]. Computational intelligence tools are enhancing the creation of interactive narratives [21], while computational narrative understanding and generation are active research areas [2,5,10]. Bridging the gap between analysis and generation is seen as a critical step for improving content creation workflows [2]. Computational methods for analyzing narrative structure, such as visual analytics and trope analysis, are being developed, though challenges remain, particularly concerning multimodal data and the representation of complex narrative elements [5,29,40].

These developments have significant implications for various stakeholders. For storytellers, they offer new tools and formats but also necessitate navigating algorithmic landscapes and developing new guidelines for digital spaces [6,34]. Platforms bear the responsibility of designing algorithms that align with platform goals without introducing bias or negative impacts on content and creators [31]. Audiences face the challenge of navigating attention scarcity and potential manipulation, highlighting the importance of critical engagement and informed choices about attention allocation [20,44]. Researchers are called to adopt interdisciplinary approaches and develop new methodologies to understand these rapidly evolving phenomena [2,10,13,15,19].

Addressing the ethical challenges inherent in narrative engineering within the attention economy is paramount. This includes navigating the ethical implications of AI-driven narrative creation and consumption [4], the effects and ethics of algorithm-driven narrative selection and data-driven content [42], and the privacy implications and potential biases of automated content analysis [15]. There is a critical need to mitigate the negative impacts of attention commodification, promote ethical data practices, and empower individuals to reclaim agency over their attention [20,24]. Ensuring the preservation of authorial intent across multi-agentic systems, especially with AI integration, is also a key challenge [30]. Furthermore, researchers must develop an empirically responsible account of attention and mental agency to fully grasp the harms associated with the attention economy [9].

Based on these conclusions, several avenues for future research emerge. A primary direction involves expanding computational narrative analysis to include multimodal integrated data and developing methods for presenting narrative structures across different platforms and devices [29,35]. Further research is needed on the cognitive effects of the attention economy, including the long-term impacts of attention scarcity and the development of more nuanced models of attentional control [9,19]. Exploring cognitive strategies for creative processes, such as the role of noncentral event encoding, remains a promising area [14].

Developing frameworks for responsible narrative design and algorithmic governance is crucial, including exploring strategies for resisting the attention economy and evaluating the impact of different policies [6,24,31]. Future work should also refine computational tools for narrative generation, potentially integrating user feedback mechanisms and extending capabilities to immersive formats like VR/AR [21,32]. Investigating the socio-cultural implications of narrative elements like tropes and their impact on comprehension is also relevant [40]. The preservation of cultural memory and the study of pre-digital storytelling cultures offer valuable perspectives for navigating the digital age [12,25]. Fundamentally, future research necessitates a robust interdisciplinary approach, combining insights from computational sciences, communication studies, psychology, economics, and the humanities to address the multifaceted challenges and opportunities in narrative engineering within the attention economy [2,7,9,10,13,15,19,33,37,43]. Leveraging approaches like literary interpretation to restructure engagement and understanding within the attention economy offers a promising direction [36].

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