Author as Character and Narrator: Deconstructing Personal Narratives
from the r/AmITheAsshole Reddit Community
- URL: http://arxiv.org/abs/2301.08104v1
- Date: Thu, 19 Jan 2023 14:50:36 GMT
- Title: Author as Character and Narrator: Deconstructing Personal Narratives
from the r/AmITheAsshole Reddit Community
- Authors: Salvatore Giorgi, Ke Zhao, Alexander H. Feng, Lara J. Martin
- Abstract summary: In the r/AmITheAsshole subreddit, people anonymously share first person narratives that contain some moral dilemma or conflict.
We identify linguistic and narrative features associated with the author as the character or as a narrator.
- Score: 66.61453314286005
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: In the r/AmITheAsshole subreddit, people anonymously share first person
narratives that contain some moral dilemma or conflict and ask the community to
judge who is at fault (i.e., who is "the asshole"). In general, first person
narratives are a unique storytelling domain where the author is the narrator
(the person telling the story) but can also be a character (the person living
the story) and, thus, the author has two distinct voices presented in the
story. In this study, we identify linguistic and narrative features associated
with the author as the character or as a narrator. We use these features to
answer the following questions: (1) what makes an asshole character and (2)
what makes an asshole narrator? We extract both Author-as-Character features
(e.g., demographics, narrative event chain, and emotional arc) and
Author-as-Narrator features (i.e., the style and emotion of the story as a
whole) in order to identify which aspects of the narrative are correlated with
the final moral judgment. Our work shows that "assholes" as Characters frame
themselves as lacking agency with a more positive personal arc, while
"assholes" as Narrators will tell emotional and opinionated stories.
Related papers
- Generating Visual Stories with Grounded and Coreferent Characters [63.07511918366848]
We present the first model capable of predicting visual stories with consistently grounded and coreferent character mentions.
Our model is finetuned on a new dataset which we build on top of the widely used VIST benchmark.
We also propose new evaluation metrics to measure the richness of characters and coreference in stories.
arXiv Detail & Related papers (2024-09-20T14:56:33Z) - Are Large Language Models Capable of Generating Human-Level Narratives? [114.34140090869175]
This paper investigates the capability of LLMs in storytelling, focusing on narrative development and plot progression.
We introduce a novel computational framework to analyze narratives through three discourse-level aspects.
We show that explicit integration of discourse features can enhance storytelling, as is demonstrated by over 40% improvement in neural storytelling.
arXiv Detail & Related papers (2024-07-18T08:02:49Z) - HEART-felt Narratives: Tracing Empathy and Narrative Style in Personal Stories with LLMs [30.636456219922906]
Empathy serves as a cornerstone in enabling prosocial behaviors, and can be evoked through sharing of personal experiences in stories.
While empathy is influenced by narrative content, intuitively, people respond to the way a story is told as well, through narrative style.
We empirically examine and quantify this relationship between style and empathy using LLMs and large-scale crowdsourcing studies.
arXiv Detail & Related papers (2024-05-27T20:00:38Z) - NarrativePlay: Interactive Narrative Understanding [27.440721435864194]
We introduce NarrativePlay, a novel system that allows users to role-play a fictional character and interact with other characters in narratives in an immersive environment.
We leverage Large Language Models (LLMs) to generate human-like responses, guided by personality traits extracted from narratives.
NarrativePlay has been evaluated on two types of narratives, detective and adventure stories, where users can either explore the world or improve their favorability with the narrative characters through conversations.
arXiv Detail & Related papers (2023-10-02T13:24:00Z) - Persona-Guided Planning for Controlling the Protagonist's Persona in
Story Generation [71.24817035071176]
We propose a planning-based generation model named CONPER to explicitly model the relationship between personas and events.
Both automatic and manual evaluation results demonstrate that CONPER outperforms state-of-the-art baselines for generating more coherent and persona-controllable stories.
arXiv Detail & Related papers (2022-04-22T13:45:02Z) - Computational Lens on Cognition: Study Of Autobiographical Versus
Imagined Stories With Large-Scale Language Models [95.88620740809004]
We study differences in the narrative flow of events in autobiographical versus imagined stories using GPT-3.
We found that imagined stories have higher sequentiality than autobiographical stories.
In comparison to imagined stories, autobiographical stories contain more concrete words and words related to the first person.
arXiv Detail & Related papers (2022-01-07T20:10:47Z) - "Let Your Characters Tell Their Story": A Dataset for Character-Centric
Narrative Understanding [31.803481510886378]
We present LiSCU -- a new dataset of literary pieces and their summaries paired with descriptions of characters that appear in them.
We also introduce two new tasks on LiSCU: Character Identification and Character Description Generation.
Our experiments with several pre-trained language models adapted for these tasks demonstrate that there is a need for better models of narrative comprehension.
arXiv Detail & Related papers (2021-09-12T06:12:55Z) - Exploring aspects of similarity between spoken personal narratives by
disentangling them into narrative clause types [13.350982138577038]
We introduce a corpus of real-world spoken personal narratives comprising 10,296 narrative clauses from 594 video transcripts.
Second, we ask non-narrative experts to annotate those clauses under Labov's sociolinguistic model of personal narratives.
Third, we train a classifier that reaches 84.7% F-score for the highest-agreed clauses.
Our approach is intended to help inform machine learning methods aimed at studying or representing personal narratives.
arXiv Detail & Related papers (2020-05-26T14:34:07Z) - Annotation of Emotion Carriers in Personal Narratives [69.07034604580214]
We are interested in the problem of understanding personal narratives (PN) - spoken or written - recollections of facts, events, and thoughts.
In PN, emotion carriers are the speech or text segments that best explain the emotional state of the user.
This work proposes and evaluates an annotation model for identifying emotion carriers in spoken personal narratives.
arXiv Detail & Related papers (2020-02-27T15:42:39Z)
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.