Contrastive Domain Adaptation for Early Misinformation Detection

Yue, Zhenrui; Zeng, Huimin; Kou, Ziyi; Shang, Lanyu; Wang, Dong

doi:10.1145/3511808.3557263

Cited by 21 publications

(3 citation statements)

References 33 publications

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“…Comparison to Previous Models over Different Timestamps. Table 2 compares our system's performance to five state-of-the-art misinformation detection models: CAMI (Yu et al 2017), FNED (Liu and Wu 2020), GRU (Ma et al 2016), and (Yue et al 2022). In order to understand the model's real-time performance and its ability to adapt to rapidly evolving information landscapes, we compare over three different time frames: 24 hours, 12 hours, and 30 minutes.…”

Section: Experiments and Evaluationmentioning

confidence: 99%

Clock against Chaos: Dynamic Assessment and Temporal Intervention in Reducing Misinformation Propagation

Ghosh,

Mitra,

Nakov

2024

ICWSM

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As social networks become the primary sources of information, the rise of misinformation poses a significant threat to the information ecosystem. Here, we address this challenge by proposing a dynamic system for real-time evaluation and assignment of misinformation scores to tweets, which can support the ongoing efforts to counteract the impact of misinformation public health, public opinion, and society. We use a unique combination of Temporal Graph Network (TGN) and Recurrent Neural Networks (RNNs) to capture both structural and temporal characteristics of misinformation propagation. We further use active learning to refine the understanding of misinformation, and a dual model system to ensure the accurate grading of tweets. Our system also incorporates a temporal embargo strategy based on belief scores, allowing for comprehensive assessment of information over time. We further outline a retraining strategy to keep the model current and robust in the dynamic misinformation landscape. The evaluation results across five social media misinformation datasets show promising accuracy in identifying false information and reducing propagation by a significant margin.

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Section: Experiments and Evaluationmentioning

confidence: 99%

Clock against Chaos: Dynamic Assessment and Temporal Intervention in Reducing Misinformation Propagation

Ghosh,

Mitra,

Nakov

2024

ICWSM

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“…[281,282] contributes with a systematic literature review in computer vision domain adaptation, identifying vital research topics [283]. These studies highlight the potential of domain adaptation across diverse domains, including object detection in video [284], face recognition [285], medical image analysis [286], natural language processing [287], robotics [288,289], 3D vision [290][291][292], etc. Additionally, specialized strategies like multi-task learning [293][294][295] and transfer learning [296] demonstrate their capability to achieve state-of-the-art performance across various visual recognition tasks and learn domain-invariant representations.…”

Section: Paper Contribution Advantagesmentioning

confidence: 99%

An In-Depth Analysis of Domain Adaptation in Computer and Robotic Vision

Tanveer,

Fatima,

Zardari

et al. 2023

Applied Sciences

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This review article comprehensively delves into the rapidly evolving field of domain adaptation in computer and robotic vision. It offers a detailed technical analysis of the opportunities and challenges associated with this topic. Domain adaptation methods play a pivotal role in facilitating seamless knowledge transfer and enhancing the generalization capabilities of computer and robotic vision systems. Our methodology involves systematic data collection and preparation, followed by the application of diverse assessment metrics to evaluate the efficacy of domain adaptation strategies. This study assesses the effectiveness and versatility of conventional, deep learning-based, and hybrid domain adaptation techniques within the domains of computer and robotic vision. Through a cross-domain analysis, we scrutinize the performance of these approaches in different contexts, shedding light on their strengths and limitations. The findings gleaned from our evaluation of specific domains and models offer valuable insights for practical applications while reinforcing the validity of the proposed methodologies.

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“…Another possible solution to the entity-bias problem is to train models continuously by techniques such as active learning, i.e., keeping the models updated with fresh knowledge from real-time news sources. An initial study [89] was done on this idea and demonstrated its feasibility. However, the study only experimented with NLP models, while graph-based methods remain under-explored.…”

Section: B Cross-domain Detectionmentioning

confidence: 99%