Revisiting the relevance of traditional genres: a network analysis of
fiction readers' preferences
- URL: http://arxiv.org/abs/2303.05080v1
- Date: Thu, 9 Mar 2023 07:31:56 GMT
- Title: Revisiting the relevance of traditional genres: a network analysis of
fiction readers' preferences
- Authors: Taom Sakal, Stephen Proulx
- Abstract summary: We investigate how well traditional fiction genres like Fantasy, Thriller, and Literature represent readers' preferences.
Using user data from Goodreads we construct a book network where two books are strongly linked if the same people tend to read or enjoy them both.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We investigate how well traditional fiction genres like Fantasy, Thriller,
and Literature represent readers' preferences. Using user data from Goodreads
we construct a book network where two books are strongly linked if the same
people tend to read or enjoy them both. We then partition this network into
communities of similar books and assign each a list of subjects from The Open
Library to serve as a proxy for traditional genres. Our analysis reveals that
the network communities correspond to existing combinations of traditional
genres, but that the exact communities differ depending on whether we consider
books that people read or books that people enjoy.
In addition, we apply principal component analysis to the data and find that
the variance in the book communities is best explained by two factors: the
maturity/childishness and realism/fantastical nature of the books. We propose
using this maturity-realism plane as a coarse classification tool for stories.
Related papers
- BookWorm: A Dataset for Character Description and Analysis [59.186325346763184]
We define two tasks: character description, which generates a brief factual profile, and character analysis, which offers an in-depth interpretation.
We introduce the BookWorm dataset, pairing books from the Gutenberg Project with human-written descriptions and analyses.
Our findings show that retrieval-based approaches outperform hierarchical ones in both tasks.
arXiv Detail & Related papers (2024-10-14T10:55:58Z) - Capturing Differences in Character Representations Between Communities: An Initial Study with Fandom [0.0]
This working paper focuses on the re-interpretation of characters, an integral part of the narrative story-world.
Using online fandom as data, computational methods were applied to explore shifts in character representations between two communities.
arXiv Detail & Related papers (2024-09-17T13:24:29Z) - LFED: A Literary Fiction Evaluation Dataset for Large Language Models [58.85989777743013]
We collect 95 literary fictions that are either originally written in Chinese or translated into Chinese, covering a wide range of topics across several centuries.
We define a question taxonomy with 8 question categories to guide the creation of 1,304 questions.
We conduct an in-depth analysis to ascertain how specific attributes of literary fictions (e.g., novel types, character numbers, the year of publication) impact LLM performance in evaluations.
arXiv Detail & Related papers (2024-05-16T15:02:24Z) - Fairness Through Domain Awareness: Mitigating Popularity Bias For Music
Discovery [56.77435520571752]
We explore the intrinsic relationship between music discovery and popularity bias.
We propose a domain-aware, individual fairness-based approach which addresses popularity bias in graph neural network (GNNs) based recommender systems.
Our approach uses individual fairness to reflect a ground truth listening experience, i.e., if two songs sound similar, this similarity should be reflected in their representations.
arXiv Detail & Related papers (2023-08-28T14:12:25Z) - An Analysis of Reader Engagement in Literary Fiction through Eye
Tracking and Linguistic Features [11.805980147608178]
We analyzed the significance of various qualities of the text in predicting how engaging a reader is likely to find it.
Furthering our understanding of what captivates readers in fiction will help better inform models used in creative narrative generation.
arXiv Detail & Related papers (2023-06-06T22:14:59Z) - Are Fairy Tales Fair? Analyzing Gender Bias in Temporal Narrative Event
Chains of Children's Fairy Tales [46.65377334112404]
Social biases and stereotypes are embedded in our culture in part through their presence in our stories.
We propose a computational pipeline that automatically extracts a story's temporal narrative verb-based event chain for each of its characters.
We also present a verb-based event annotation scheme that can facilitate bias analysis by including categories such as those that align with traditional stereotypes.
arXiv Detail & Related papers (2023-05-26T05:29:37Z) - Personality Understanding of Fictional Characters during Book Reading [81.68515671674301]
We present the first labeled dataset PersoNet for this problem.
Our novel annotation strategy involves annotating user notes from online reading apps as a proxy for the original books.
Experiments and human studies indicate that our dataset construction is both efficient and accurate.
arXiv Detail & Related papers (2023-05-17T12:19:11Z) - Conventions and Mutual Expectations -- understanding sources for web
genres [0.8057441774248633]
Genres can be understood in many different ways.
They are often perceived as a primarily sociological construction, or, alternatively, as a stylostatistically observable objective characteristic of texts.
This investigation discusses knowledge sources for studying genre variation and change by observing reader and author behaviour rather than performing analyses on the information objects themselves.
arXiv Detail & Related papers (2022-05-01T16:44:55Z) - Textual Stylistic Variation: Choices, Genres and Individuals [0.8057441774248633]
This chapter argues for more informed target metrics for the statistical processing of stylistic variation in text collections.
This chapter discusses variation given by genre, and contrasts it to variation occasioned by individual choice.
arXiv Detail & Related papers (2022-05-01T16:39:49Z) - Author Clustering and Topic Estimation for Short Texts [69.54017251622211]
We propose a novel model that expands on the Latent Dirichlet Allocation by modeling strong dependence among the words in the same document.
We also simultaneously cluster users, removing the need for post-hoc cluster estimation.
Our method performs as well as -- or better -- than traditional approaches to problems arising in short text.
arXiv Detail & Related papers (2021-06-15T20:55:55Z) - Modeling Social Readers: Novel Tools for Addressing Reception from
Online Book Reviews [0.0]
We study the readers' distillation of the main storylines in a novel using a corpus of reviews of five popular novels.
We make three important contributions to the study of infinite vocabulary networks.
We present a new sequencing algorithm, REV2SEQ, that generates a consensus sequence of events based on partial trajectories aggregated from the reviews.
arXiv Detail & Related papers (2021-05-03T20:10:14Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.