RP-DNN: A Tweet level propagation context based deep neural networks for early rumor detection in Social Media

• URL: http://arxiv.org/abs/2002.12683v2
• Date: Mon, 2 Mar 2020 10:47:53 GMT
• Title: RP-DNN: A Tweet level propagation context based deep neural networks for early rumor detection in Social Media
• Authors: Jie Gao, Sooji Han, Xingyi Song, Fabio Ciravegna
• Abstract summary: Early rumor detection (ERD) on social media platform is very challenging when limited, incomplete and noisy information is available. We present a novel hybrid neural network architecture, which combines a character-based bidirectional language model and stacked Long Short-Term Memory (LSTM) networks. Our models achieve state-of-the-art(SoA) performance for detecting unseen rumors on large augmented data which covers more than 12 events and 2,967 rumors.
• Score: 3.253418861583211
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Early rumor detection (ERD) on social media platform is very challenging when limited, incomplete and noisy information is available. Most of the existing methods have largely worked on event-level detection that requires the collection of posts relevant to a specific event and relied only on user-generated content. They are not appropriate to detect rumor sources in the very early stages, before an event unfolds and becomes widespread. In this paper, we address the task of ERD at the message level. We present a novel hybrid neural network architecture, which combines a task-specific character-based bidirectional language model and stacked Long Short-Term Memory (LSTM) networks to represent textual contents and social-temporal contexts of input source tweets, for modelling propagation patterns of rumors in the early stages of their development. We apply multi-layered attention models to jointly learn attentive context embeddings over multiple context inputs. Our experiments employ a stringent leave-one-out cross-validation (LOO-CV) evaluation setup on seven publicly available real-life rumor event data sets. Our models achieve state-of-the-art(SoA) performance for detecting unseen rumors on large augmented data which covers more than 12 events and 2,967 rumors. An ablation study is conducted to understand the relative contribution of each component of our proposed model.

Related papers

• A Unified Contrastive Transfer Framework with Propagation Structure for Boosting Low-Resource Rumor Detection [11.201348902221257]
  existing rumor detection algorithms show promising performance on yesterday's news. Due to a lack of substantial training data and prior expert knowledge, they are poor at spotting rumors concerning unforeseen events. We propose a unified contrastive transfer framework to detect rumors by adapting the features learned from well-resourced rumor data to that of the low-resourced with only few-shot annotations.
  arXiv  Detail & Related papers  (2023-04-04T03:13:03Z)
• Zero-Shot Rumor Detection with Propagation Structure via Prompt Learning [24.72097408129496]
  Previous studies reveal that due to the lack of annotated resources, rumors presented in minority languages are hard to be detected. We propose a novel framework based on prompt learning to detect rumors falling in different domains or presented in different languages. Our proposed model achieves much better performance than state-of-the-art methods and exhibits a superior capacity for detecting rumors at early stages.
  arXiv  Detail & Related papers  (2022-12-02T12:04:48Z)
• Evidential Temporal-aware Graph-based Social Event Detection via Dempster-Shafer Theory [76.4580340399321]
  We propose ETGNN, a novel Evidential Temporal-aware Graph Neural Network. We construct view-specific graphs whose nodes are the texts and edges are determined by several types of shared elements respectively. Considering the view-specific uncertainty, the representations of all views are converted into mass functions through evidential deep learning (EDL) neural networks.
  arXiv  Detail & Related papers  (2022-05-24T16:22:40Z)
• Rumor Detection with Self-supervised Learning on Texts and Social Graph [101.94546286960642]
  We propose contrastive self-supervised learning on heterogeneous information sources, so as to reveal their relations and characterize rumors better. We term this framework as Self-supervised Rumor Detection (SRD) Extensive experiments on three real-world datasets validate the effectiveness of SRD for automatic rumor detection on social media.
  arXiv  Detail & Related papers  (2022-04-19T12:10:03Z)
• Detect Rumors in Microblog Posts for Low-Resource Domains via Adversarial Contrastive Learning [8.013665071332388]
  We propose an adversarial contrastive learning framework to detect rumors by adapting the features learned from well-resourced rumor data to that of the low-resourced. Our framework achieves much better performance than state-of-the-art methods and exhibits a superior capacity for detecting rumors at early stages.
  arXiv  Detail & Related papers  (2022-04-18T03:10:34Z)
• A Generative Language Model for Few-shot Aspect-Based Sentiment Analysis [90.24921443175514]
  We focus on aspect-based sentiment analysis, which involves extracting aspect term, category, and predicting their corresponding polarities. We propose to reformulate the extraction and prediction tasks into the sequence generation task, using a generative language model with unidirectional attention. Our approach outperforms the previous state-of-the-art (based on BERT) on average performance by a large margins in few-shot and full-shot settings.
  arXiv  Detail & Related papers  (2022-04-11T18:31:53Z)
• End-to-End Active Speaker Detection [58.7097258722291]
  We propose an end-to-end training network where feature learning and contextual predictions are jointly learned. We also introduce intertemporal graph neural network (iGNN) blocks, which split the message passing according to the main sources of context in the ASD problem. Experiments show that the aggregated features from the iGNN blocks are more suitable for ASD, resulting in state-of-the art performance.
  arXiv  Detail & Related papers  (2022-03-27T08:55:28Z)
• UniCon: Unified Context Network for Robust Active Speaker Detection [111.90529347692723]
  We introduce a new efficient framework, the Unified Context Network (UniCon), for robust active speaker detection (ASD) Our solution is a novel, unified framework that focuses on jointly modeling multiple types of contextual information. A thorough ablation study is performed on several challenging ASD benchmarks under different settings.
  arXiv  Detail & Related papers  (2021-08-05T13:25:44Z)
• SRLF: A Stance-aware Reinforcement Learning Framework for Content-based Rumor Detection on Social Media [15.985224010346593]
  Early content-based methods focused on finding clues from text and user profiles for rumor detection. Recent studies combine the stances of users' comments with news content to capture the difference between true and false rumors. We propose a novel Stance-aware Reinforcement Learning Framework (SRLF) to select high-quality labeled stance data for model training and rumor detection.
  arXiv  Detail & Related papers  (2021-05-10T03:58:34Z)
• 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)
• Ensemble Deep Learning on Time-Series Representation of Tweets for Rumor Detection in Social Media [2.6514980627603006]
  We propose an ensemble model, which performs majority-voting on a collection of predictions by deep neural networks using time-series vector representation of Twitter data for timely detection of rumors. Experimental results show that the classification performance has been improved by 7.9% in terms of micro F1 score compared to the baselines.
  arXiv  Detail & Related papers  (2020-04-26T23:13:31Z)

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.