Characterizing Network Structure of Anti-Trans Actors on TikTok

• URL: http://arxiv.org/abs/2501.16507v1
• Date: Mon, 27 Jan 2025 21:14:18 GMT
• Title: Characterizing Network Structure of Anti-Trans Actors on TikTok
• Authors: Maxyn Leitner, Rebecca Dorn, Fred Morstatter, Kristina Lerman,
• Abstract summary: We develop a taxonomy of trans related sentiment to enable the classification of content on TikTok.<n>We analyze the reply network structures of pro-trans and anti-trans communities.
• Score: 8.193721412145031
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: The recent proliferation of short form video social media sites such as TikTok has been effectively utilized for increased visibility, communication, and community connection amongst trans/nonbinary creators online. However, these same platforms have also been exploited by right-wing actors targeting trans/nonbinary people, enabling such anti-trans actors to efficiently spread hate speech and propaganda. Given these divergent groups, what are the differences in network structure between anti-trans and pro-trans communities on TikTok, and to what extent do they amplify the effects of anti-trans content? In this paper, we collect a sample of TikTok videos containing pro and anti-trans content, and develop a taxonomy of trans related sentiment to enable the classification of content on TikTok, and ultimately analyze the reply network structures of pro-trans and anti-trans communities. In order to accomplish this, we worked with hired expert data annotators from the trans/nonbinary community in order to generate a sample of highly accurately labeled data. From this subset, we utilized a novel classification pipeline leveraging Retrieval-Augmented Generation (RAG) with annotated examples and taxonomy definitions to classify content into pro-trans, anti-trans, or neutral categories. We find that incorporating our taxonomy and its logics into our classification engine results in improved ability to differentiate trans related content, and that Results from network analysis indicate many interactions between posters of pro-trans and anti-trans content exist, further demonstrating targeting of trans individuals, and demonstrating the need for better content moderation tools

Related papers

• SegTrans: Transferable Adversarial Examples for Segmentation Models [47.89859018981832]
  Existing adversarial attack methods show poor transferability across different segmentation models.<n>We propose SegTrans, a novel transfer attack framework that divides the input sample into multiple local regions.<n>SegTrans only retains local semantic information from the original input, rather than using global semantic information to optimize perturbations.
  arXiv  Detail & Related papers  (2025-10-10T02:11:29Z)
• Semantics Alignment via Split Learning for Resilient Multi-User Semantic Communication [56.54422521327698]
  Recent studies on semantic communication rely on neural network (NN) based transceivers such as deep joint source and channel coding (DeepJSCC) Unlike traditional transceivers, these neural transceivers are trainable using actual source data and channels, enabling them to extract and communicate semantics. We propose a distributed learning based solution, which leverages split learning (SL) and partial NN fine-tuning techniques.
  arXiv  Detail & Related papers  (2023-10-13T20:29:55Z)
• Adaptive Nonlinear Latent Transformation for Conditional Face Editing [40.32385363670918]
  We propose a novel adaptive nonlinear latent transformation for disentangled and conditional face editing, termed AdaTrans. AdaTrans divides the manipulation process into several finer steps; i.e., the direction and size at each step are conditioned on both the facial attributes and the latent codes. AdaTrans enables a controllable face editing with the advantages of disentanglement, flexibility with non-binary attributes, and high fidelity.
  arXiv  Detail & Related papers  (2023-07-15T12:36:50Z)
• TransFool: An Adversarial Attack against Neural Machine Translation Models [49.50163349643615]
  We investigate the vulnerability of Neural Machine Translation (NMT) models to adversarial attacks and propose a new attack algorithm called TransFool. We generate fluent adversarial examples in the source language that maintain a high level of semantic similarity with the clean samples. Based on automatic and human evaluations, TransFool leads to improvement in terms of success rate, semantic similarity, and fluency compared to the existing attacks.
  arXiv  Detail & Related papers  (2023-02-02T08:35:34Z)
• TransRank: Self-supervised Video Representation Learning via Ranking-based Transformation Recognition [73.7566539108205]
  We observe the great potential of RecogTrans on both semantic-related and temporal-related downstream tasks. Based on hard-label classification, existing RecogTrans approaches suffer from noisy supervision signals in pre-training. To mitigate this problem, we developed TransRank, a unified framework for recognizing Transformations in a Ranking formulation.
  arXiv  Detail & Related papers  (2022-05-04T12:39:25Z)
• High-Performance Transformer Tracking [74.07751002861802]
  We present a Transformer tracking (named TransT) method based on the Siamese-like feature extraction backbone, the designed attention-based fusion mechanism, and the classification and regression head. Experiments show that our TransT and TransT-M methods achieve promising results on seven popular datasets.
  arXiv  Detail & Related papers  (2022-03-25T09:33:29Z)
• Sexism Identification in Tweets and Gabs using Deep Neural Networks [6.531659195805749]
  This paper explores the classification of sexism in text using a variety of deep neural network model architectures. It performs binary and multiclass sexism classification on the dataset of tweets and gabs from the sEXism Identification in Social neTworks (EXIST) task in IberLEF 2021. The models are seen to perform comparatively to those from the competition, with the best performances seen using BERT and a multi-filter CNN model.
  arXiv  Detail & Related papers  (2021-11-05T16:57:08Z)
• Modelling Adversarial Noise for Adversarial Defense [96.56200586800219]
  adversarial defenses typically focus on exploiting adversarial examples to remove adversarial noise or train an adversarially robust target model. Motivated by that the relationship between adversarial data and natural data can help infer clean data from adversarial data to obtain the final correct prediction. We study to model adversarial noise to learn the transition relationship in the label space for using adversarial labels to improve adversarial accuracy.
  arXiv  Detail & Related papers  (2021-09-21T01:13:26Z)
• Visual Boundary Knowledge Translation for Foreground Segmentation [57.32522585756404]
  We make an attempt towards building models that explicitly account for visual boundary knowledge, in hope to reduce the training effort on segmenting unseen categories. With only tens of labeled samples as guidance, Trans-Net achieves close results on par with fully supervised methods.
  arXiv  Detail & Related papers  (2021-08-01T07:10:25Z)
• Multi-Compound Transformer for Accurate Biomedical Image Segmentation [33.49158559361491]
  We propose a unified transformer network, termed Multi-Compound Transformer (MCTrans) MCTrans embeds the multi-scale convolutional features as a sequence of tokens and performs intra- and inter-scale self-attention. MCTrans can be easily plugged into a UNet-like network and attains a significant improvement over the state-of-the-art methods in biomedical image segmentation.
  arXiv  Detail & Related papers  (2021-06-28T03:45:44Z)
• Weak-shot Fine-grained Classification via Similarity Transfer [27.123162775124204]
  We consider the problem of learning novel categories from web data with the support of a clean set of base categories. We propose a method called SimTrans to transfer pairwise semantic similarity from base categories to novel categories.
  arXiv  Detail & Related papers  (2020-09-19T09:31:52Z)

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.