Source detection in graphs has demonstrated robust efficacy in the domain of
rumor source identification. Although recent solutions have enhanced
performance by leveraging deep neural networks, they often require complete
user data. In this paper, we address a more challenging task, rumor source
detection with incomplete user data, and propose a novel framework, i.e.,
Source Detection in Graphs with Incomplete Nodes via Positional Encoding and
Attentive Fusion (GIN-SD), to tackle this challenge. Specifically, our approach
utilizes a positional embedding module to distinguish nodes that are incomplete
and employs a self-attention mechanism to focus on nodes with greater
information transmission capacity. To mitigate the prediction bias caused by
the significant disparity between the numbers of source and non-source nodes,
we also introduce a class-balancing mechanism. Extensive experiments validate
the effectiveness of GIN-SD and its superiority to state-of-the-art methods.

通过位置编码和关注融合（GIN-SD）的图中不完整节点源检测方法，有效解决了在有限的用户数据条件下对谣言源的检测问题，并证实了它相对于现有方法的优越性。

GIN-SD: 通过位置编码和注意力融合的不完整节点图源检测

GIN-SD: Source Detection in Graphs with Incomplete Nodes via Positional  Encoding and Attentive Fusion

With the advent of social media, an increasing number of netizens are sharing
and reading posts and news online. However, the huge volumes of misinformation
(e.g., fake news and rumors) that flood the internet can adversely affect
people's lives, and have resulted in the emergence of rumor and fake news
detection as a hot research topic. The emotions and sentiments of netizens, as
expressed in social media posts and news, constitute important factors that can
help to distinguish fake news from genuine news and to understand the spread of
rumors. This article comprehensively reviews emotion-based methods for
misinformation detection. We begin by explaining the strong links between
emotions and misinformation. We subsequently provide a detailed analysis of a
range of misinformation detection methods that employ a variety of emotion,
sentiment and stance-based features, and describe their strengths and
weaknesses. Finally, we discuss a number of ongoing challenges in emotion-based
misinformation detection based on large language models and suggest future
research directions, including data collection (multi-platform, multilingual),
annotation, benchmark, multimodality, and interpretability.

社交媒体兴起以来，越来越多的网民在线上分享和阅读帖子和新闻。然而，泛滥于互联网的大量错误信息（例如虚假新闻和谣言）会对人们的生活产生负面影响，这引发了对谣言和虚假新闻检测的热门研究课题。网民在社交媒体帖子和新闻中表达的情绪和情感是帮助区分虚假新闻和真实新闻，以及理解谣言传播的重要因素。本文全面回顾基于情绪的误信息检测方法。我们首先解释情绪与误信息之间的紧密联系。接着，我们详细分析了多种基于情绪、情感和立场特征的误信息检测方法，描述它们的优势和劣势。最后，我们讨论了基于大型语言模型的基于情绪的误信息检测面临的一些挑战，并提出了未来的研究方向，包括数据收集（多平台、多语言）、标注、基准测试、多模态以及可解释性。

情感检测在虚假信息中的应用：一项综述

Emotion Detection for Misinformation: A Review

New events emerge over time influencing the topics of rumors in social media.
Current rumor detection benchmarks use random splits as training, development
and test sets which typically results in topical overlaps. Consequently, models
trained on random splits may not perform well on rumor classification on
previously unseen topics due to the temporal concept drift. In this paper, we
provide a re-evaluation of classification models on four popular rumor
detection benchmarks considering chronological instead of random splits. Our
experimental results show that the use of random splits can significantly
overestimate predictive performance across all datasets and models. Therefore,
we suggest that rumor detection models should always be evaluated using
chronological splits for minimizing topical overlaps.

本研究通过对四种常用谣言检测基准数据集进行实验，发现使用时间序列而非随机分割数据集可以有效降低主题重叠部分，降低对模型预测性能高估的影响，建议谣言检测模型均应使用时间序列分割数据集以提高准确性。