Enhancing Rumor Detection Methods with Propagation Structure Infused Language Model

• URL: http://arxiv.org/abs/2508.07209v1
• Date: Sun, 10 Aug 2025 07:04:50 GMT
• Title: Enhancing Rumor Detection Methods with Propagation Structure Infused Language Model
• Authors: Chaoqun Cui, Siyuan Li, Kunkun Ma, Caiyan Jia,
• Abstract summary: We propose a continue pretraining strategy called Post Engagement Prediction (PEP) to infuse information from propagation structures into language models.<n> PEP makes models to predict root, branch, and parent relations between posts, capturing interactions of stance and sentiment crucial for rumor detection.<n>We also curate and release large-scale Twitter corpus: TwitterCorpus (269GB text), and two unlabeled claim conversation datasets with propagation structures (UTwitter and UWeibo)
• Score: 20.002086667357595
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Pretrained Language Models (PLMs) have excelled in various Natural Language Processing tasks, benefiting from large-scale pretraining and self-attention mechanism's ability to capture long-range dependencies. However, their performance on social media application tasks like rumor detection remains suboptimal. We attribute this to mismatches between pretraining corpora and social texts, inadequate handling of unique social symbols, and pretraining tasks ill-suited for modeling user engagements implicit in propagation structures. To address these issues, we propose a continue pretraining strategy called Post Engagement Prediction (PEP) to infuse information from propagation structures into PLMs. PEP makes models to predict root, branch, and parent relations between posts, capturing interactions of stance and sentiment crucial for rumor detection. We also curate and release large-scale Twitter corpus: TwitterCorpus (269GB text), and two unlabeled claim conversation datasets with propagation structures (UTwitter and UWeibo). Utilizing these resources and PEP strategy, we train a Twitter-tailored PLM called SoLM. Extensive experiments demonstrate PEP significantly boosts rumor detection performance across universal and social media PLMs, even in few-shot scenarios. On benchmark datasets, PEP enhances baseline models by 1.0-3.7\% accuracy, even enabling it to outperform current state-of-the-art methods on multiple datasets. SoLM alone, without high-level modules, also achieves competitive results, highlighting the strategy's effectiveness in learning discriminative post interaction features.

Related papers

• Self-Rewarding PPO: Aligning Large Language Models with Demonstrations Only [70.43369087819332]
  Supervised fine-tuning (SFT) has emerged as a crucial method for aligning large language models with human-annotated demonstrations.<n>We propose Self-Rewarding PPO, a novel fine-tuning method that leverages on-policy techniques to enhance generalization performance.
  arXiv  Detail & Related papers  (2025-10-24T02:02:13Z)
• CSE-SFP: Enabling Unsupervised Sentence Representation Learning via a Single Forward Pass [3.0566617373924325]
  Recent advances in pre-trained language models (PLMs) have driven remarkable progress in this field.<n>We propose CSE-SFP, an innovative method that exploits the structural characteristics of generative models.<n>We show that CSE-SFP not only produces higher-quality embeddings but also significantly reduces both training time and memory consumption.
  arXiv  Detail & Related papers  (2025-05-01T08:27:14Z)
• TASTE: Text-Aligned Speech Tokenization and Embedding for Spoken Language Modeling [46.60911294356232]
  We introduce Text-Aligned Speech Tokenization and Embedding (TASTE) to align speech token with corresponding text transcription during the tokenization stage.<n>We conduct extensive experiments and show that TASTE can preserve essential paralinguistic information while dramatically reducing the token sequence length.<n> Experimental results show that TASTE-based SLMs perform comparable to previous work on SALMON and StoryCloze.
  arXiv  Detail & Related papers  (2025-04-09T17:14:33Z)
• TPP-LLM: Modeling Temporal Point Processes by Efficiently Fine-Tuning Large Language Models [0.0]
  Temporal point processes (TPPs) are widely used to model the timing and occurrence of events in domains such as social networks, transportation systems, and e-commerce.<n>We introduce TPP-LLM, a novel framework that integrates large language models (LLMs) with TPPs to capture both the semantic and temporal aspects of event sequences.
  arXiv  Detail & Related papers  (2024-10-02T22:17:24Z)
• Relational Prompt-based Pre-trained Language Models for Social Event Detection [45.574891451459955]
  Social Event Detection (SED) aims to identify significant events from social streams, and has a wide application ranging from public opinion analysis to risk management. GNN-based methods often struggle with missing and noisy edges between messages, affecting the quality of learned message embedding. We propose a new pairwise message modeling strategy to construct social messages into message pairs with multi-relational sequences, and a new multi-relational prompt-based message learning mechanism. We show that the RPLM_SED model achieves state-of-the-art performance in offline, online, low-resource, and long-tail
  arXiv  Detail & Related papers  (2024-04-12T06:23:07Z)
• PEMA: An Offsite-Tunable Plug-in External Memory Adaptation for Language Models [6.622419351156256]
  Pre-trained language models (PLMs) show impressive performance in various downstream NLP tasks. Due to the substantial resources required, many PLM weights are confidential. We introduce Plug-in External Memory Adaptation (PEMA), a. Efficient Fine-Tuning (PEFT) method, enabling fine-tuning without requiring access to all the weights.
  arXiv  Detail & Related papers  (2023-11-14T23:20:51Z)
• FLIP: Fine-grained Alignment between ID-based Models and Pretrained Language Models for CTR Prediction [49.510163437116645]
  Click-through rate (CTR) prediction plays as a core function module in personalized online services. Traditional ID-based models for CTR prediction take as inputs the one-hot encoded ID features of tabular modality. Pretrained Language Models(PLMs) has given rise to another paradigm, which takes as inputs the sentences of textual modality. We propose to conduct Fine-grained feature-level ALignment between ID-based Models and Pretrained Language Models(FLIP) for CTR prediction.
  arXiv  Detail & Related papers  (2023-10-30T11:25:03Z)
• Improving Language Models Meaning Understanding and Consistency by Learning Conceptual Roles from Dictionary [65.268245109828]
  Non-human-like behaviour of contemporary pre-trained language models (PLMs) is a leading cause undermining their trustworthiness. A striking phenomenon is the generation of inconsistent predictions, which produces contradictory results. We propose a practical approach that alleviates the inconsistent behaviour issue by improving PLM awareness.
  arXiv  Detail & Related papers  (2023-10-24T06:15:15Z)
• Pre-trained Language Models for Keyphrase Generation: A Thorough Empirical Study [76.52997424694767]
  We present an in-depth empirical study of keyphrase extraction and keyphrase generation using pre-trained language models. We show that PLMs have competitive high-resource performance and state-of-the-art low-resource performance. Further results show that in-domain BERT-like PLMs can be used to build strong and data-efficient keyphrase generation models.
  arXiv  Detail & Related papers  (2022-12-20T13:20:21Z)
• RLIP: Relational Language-Image Pre-training for Human-Object Interaction Detection [32.20132357830726]
  Language-Image Pre-training (LIPR) is a strategy for contrastive pre-training that leverages both entity and relation descriptions. We show the benefits of these contributions, collectively termed RLIP-ParSe, for improved zero-shot, few-shot and fine-tuning HOI detection as well as increased robustness from noisy annotations.
  arXiv  Detail & Related papers  (2022-09-05T07:50:54Z)
• An Explanation of In-context Learning as Implicit Bayesian Inference [117.19809377740188]
  We study the role of the pretraining distribution on the emergence of in-context learning. We prove that in-context learning occurs implicitly via Bayesian inference of the latent concept. We empirically find that scaling model size improves in-context accuracy even when the pretraining loss is the same.
  arXiv  Detail & Related papers  (2021-11-03T09:12:33Z)
• Unsupervised Paraphrasing with Pretrained Language Models [85.03373221588707]
  We propose a training pipeline that enables pre-trained language models to generate high-quality paraphrases in an unsupervised setting. Our recipe consists of task-adaptation, self-supervision, and a novel decoding algorithm named Dynamic Blocking. We show with automatic and human evaluations that our approach achieves state-of-the-art performance on both the Quora Question Pair and the ParaNMT datasets.
  arXiv  Detail & Related papers  (2020-10-24T11:55:28Z)
• SPLAT: Speech-Language Joint Pre-Training for Spoken Language Understanding [61.02342238771685]
  Spoken language understanding requires a model to analyze input acoustic signal to understand its linguistic content and make predictions. Various pre-training methods have been proposed to learn rich representations from large-scale unannotated speech and text. We propose a novel semi-supervised learning framework, SPLAT, to jointly pre-train the speech and language modules.
  arXiv  Detail & Related papers  (2020-10-05T19:29:49Z)

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.