Content Rating Classification for Fan Fiction

• URL: http://arxiv.org/abs/2212.12496v1
• Date: Fri, 23 Dec 2022 17:40:03 GMT
• Title: Content Rating Classification for Fan Fiction
• Authors: Yu Qiao and James Pope
• Abstract summary: Fan fiction content ratings are done voluntarily or required by regulation. The problem is to take fan fiction text and determine the appropriate content rating. We propose natural language processing techniques to automatically determine the content rating.
• Score: 35.539218522504605
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Content ratings can enable audiences to determine the suitability of various media products. With the recent advent of fan fiction, the critical issue of fan fiction content ratings has emerged. Whether fan fiction content ratings are done voluntarily or required by regulation, there is the need to automate the content rating classification. The problem is to take fan fiction text and determine the appropriate content rating. Methods for other domains, such as online books, have been attempted though none have been applied to fan fiction. We propose natural language processing techniques, including traditional and deep learning methods, to automatically determine the content rating. We show that these methods produce poor accuracy results for multi-classification. We then demonstrate that treating the problem as a binary classification problem produces better accuracy. Finally, we believe and provide some evidence that the current approach of self-annotating has led to incorrect labels limiting classification results.

Related papers

• I only read it for the plot! Maturity Ratings Affect Fanfiction Style and Community Engagement [0.0]
  We consider the textual profiles of different fanfiction maturity ratings, how they vary across fan groups, and how this relates to reader engagement metrics. We find that explicit fanfiction in particular has a distinct textual profile when compared to other maturity ratings.
  arXiv  Detail & Related papers  (2025-04-07T07:20:59Z)
• Detecting Content Rating Violations in Android Applications: A Vision-Language Approach [0.0]
  We propose and evaluate a vision approach to predict the content ratings of mobile game applications and detect content rating violations. Our method achieves 6% better relative accuracy compared to the state-of-the-art CLIP-fine-tuned model in a multi-modal setting. Applying our classifier in the wild, we detected more than 70 possible cases of content rating violations, including nine instances with the 'Teacher Approved' badge.
  arXiv  Detail & Related papers  (2025-02-07T06:21:43Z)
• Isolating authorship from content with semantic embeddings and contrastive learning [49.15148871877941]
  Authorship has entangled style and content inside. We present a technique to use contrastive learning with additional hard negatives synthetically created using a semantic similarity model. This disentanglement technique aims to distance the content embedding space from the style embedding space, leading to embeddings more informed by style.
  arXiv  Detail & Related papers  (2024-11-27T16:08:46Z)
• Analytical and Empirical Study of Herding Effects in Recommendation Systems [72.6693986712978]
  We study how to manage product ratings via rating aggregation rules and shortlisted representative reviews. We show that proper recency aware rating aggregation rules can improve the speed of convergence in Amazon and TripAdvisor.
  arXiv  Detail & Related papers  (2024-08-20T14:29:23Z)
• LLM-based Rewriting of Inappropriate Argumentation using Reinforcement Learning from Machine Feedback [16.57980268646285]
  This paper studies how inappropriate language in arguments can be computationally mitigated. We propose a reinforcement learning-based rewriting approach that balances content preservation and appropriateness. We evaluate different weighting schemes for the reward function in both absolute and relative human assessment studies.
  arXiv  Detail & Related papers  (2024-06-05T15:18:08Z)
• Using Natural Language Explanations to Rescale Human Judgments [81.66697572357477]
  We propose a method to rescale ordinal annotations and explanations using large language models (LLMs) We feed annotators' Likert ratings and corresponding explanations into an LLM and prompt it to produce a numeric score anchored in a scoring rubric. Our method rescales the raw judgments without impacting agreement and brings the scores closer to human judgments grounded in the same scoring rubric.
  arXiv  Detail & Related papers  (2023-05-24T06:19:14Z)
• Like a Good Nearest Neighbor: Practical Content Moderation and Text Classification [66.02091763340094]
  Like a Good Nearest Neighbor (LaGoNN) is a modification to SetFit that introduces no learnable parameters but alters input text with information from its nearest neighbor. LaGoNN is effective at flagging undesirable content and text classification, and improves the performance of SetFit.
  arXiv  Detail & Related papers  (2023-02-17T15:43:29Z)
• Deep Architectures for Content Moderation and Movie Content Rating [3.04585143845864]
  Movie content rating and TV show rating are the two most common rating systems established by professional committees. A desirable solution is to use computer vision based video content analysis techniques to automate the evaluation process. In this paper, related works are summarized for action recognition, multi-modal learning, movie genre classification, and sensitive content detection.
  arXiv  Detail & Related papers  (2022-12-08T19:50:53Z)
• Integrating Rankings into Quantized Scores in Peer Review [61.27794774537103]
  In peer review, reviewers are usually asked to provide scores for the papers. To mitigate this issue, conferences have started to ask reviewers to additionally provide a ranking of the papers they have reviewed. There are no standard procedure for using this ranking information and Area Chairs may use it in different ways. We take a principled approach to integrate the ranking information into the scores.
  arXiv  Detail & Related papers  (2022-04-05T19:39:13Z)
• Classifying YouTube Comments Based on Sentiment and Type of Sentence [0.0]
  We address the challenge of text extraction and classification from YouTube comments using well-known statistical measures and machine learning models. The results show that our approach that incorporates conventional methods performs well on the classification task, validating its potential in assisting content creators increase viewer engagement on their channel.
  arXiv  Detail & Related papers  (2021-10-31T18:08:10Z)
• Stay on Topic, Please: Aligning User Comments to the Content of a News Article [7.3203631241415055]
  We propose a classification algorithm to categorize user comments posted to a new article base don their alignment to its content. The alignment seek to match user comments to an article based on similarity off content, entities in discussion, and topic. We conduct a user study to evaluate human labeling performance to understand the difficulty of the classification task.
  arXiv  Detail & Related papers  (2021-03-03T18:29:00Z)
• Abstractive Opinion Tagging [65.47649273721679]
  In e-commerce, opinion tags refer to a ranked list of tags provided by the e-commerce platform that reflect characteristics of reviews of an item. Current mechanisms for generating opinion tags rely on either manual or labelling methods, which is time-consuming and ineffective. We propose an abstractive opinion tagging framework, named AOT-Net, to generate a ranked list of opinion tags given a large number of reviews.
  arXiv  Detail & Related papers  (2021-01-18T05:08:15Z)
• Rank over Class: The Untapped Potential of Ranking in Natural Language Processing [8.637110868126546]
  We argue that many tasks which are currently addressed using classification are in fact being shoehorned into a classification mould. We propose a novel end-to-end ranking approach consisting of a Transformer network responsible for producing representations for a pair of text sequences. In an experiment on a heavily-skewed sentiment analysis dataset, converting ranking results to classification labels yields an approximately 22% improvement over state-of-the-art text classification.
  arXiv  Detail & Related papers  (2020-09-10T22:18:57Z)

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.