214 research outputs found
Fake News Detection with Heterogeneous Transformer
The dissemination of fake news on social networks has drawn public need for
effective and efficient fake news detection methods. Generally, fake news on
social networks is multi-modal and has various connections with other entities
such as users and posts. The heterogeneity in both news content and the
relationship with other entities in social networks brings challenges to
designing a model that comprehensively captures the local multi-modal semantics
of entities in social networks and the global structural representation of the
propagation patterns, so as to classify fake news effectively and accurately.
In this paper, we propose a novel Transformer-based model: HetTransformer to
solve the fake news detection problem on social networks, which utilises the
encoder-decoder structure of Transformer to capture the structural information
of news propagation patterns. We first capture the local heterogeneous
semantics of news, post, and user entities in social networks. Then, we apply
Transformer to capture the global structural representation of the propagation
patterns in social networks for fake news detection. Experiments on three
real-world datasets demonstrate that our model is able to outperform the
state-of-the-art baselines in fake news detection
Graph Learning for Anomaly Analytics: Algorithms, Applications, and Challenges
Anomaly analytics is a popular and vital task in various research contexts,
which has been studied for several decades. At the same time, deep learning has
shown its capacity in solving many graph-based tasks like, node classification,
link prediction, and graph classification. Recently, many studies are extending
graph learning models for solving anomaly analytics problems, resulting in
beneficial advances in graph-based anomaly analytics techniques. In this
survey, we provide a comprehensive overview of graph learning methods for
anomaly analytics tasks. We classify them into four categories based on their
model architectures, namely graph convolutional network (GCN), graph attention
network (GAT), graph autoencoder (GAE), and other graph learning models. The
differences between these methods are also compared in a systematic manner.
Furthermore, we outline several graph-based anomaly analytics applications
across various domains in the real world. Finally, we discuss five potential
future research directions in this rapidly growing field
Probing Spurious Correlations in Popular Event-Based Rumor Detection Benchmarks
As social media becomes a hotbed for the spread of misinformation, the
crucial task of rumor detection has witnessed promising advances fostered by
open-source benchmark datasets. Despite being widely used, we find that these
datasets suffer from spurious correlations, which are ignored by existing
studies and lead to severe overestimation of existing rumor detection
performance. The spurious correlations stem from three causes: (1) event-based
data collection and labeling schemes assign the same veracity label to multiple
highly similar posts from the same underlying event; (2) merging multiple data
sources spuriously relates source identities to veracity labels; and (3)
labeling bias. In this paper, we closely investigate three of the most popular
rumor detection benchmark datasets (i.e., Twitter15, Twitter16 and PHEME), and
propose event-separated rumor detection as a solution to eliminate spurious
cues. Under the event-separated setting, we observe that the accuracy of
existing state-of-the-art models drops significantly by over 40%, becoming only
comparable to a simple neural classifier. To better address this task, we
propose Publisher Style Aggregation (PSA), a generalizable approach that
aggregates publisher posting records to learn writing style and veracity
stance. Extensive experiments demonstrate that our method outperforms existing
baselines in terms of effectiveness, efficiency and generalizability.Comment: Accepted to ECML-PKDD 202
Graph learning for anomaly analytics : algorithms, applications, and challenges
Anomaly analytics is a popular and vital task in various research contexts that has been studied for several decades. At the same time, deep learning has shown its capacity in solving many graph-based tasks, like node classification, link prediction, and graph classification. Recently, many studies are extending graph learning models for solving anomaly analytics problems, resulting in beneficial advances in graph-based anomaly analytics techniques. In this survey, we provide a comprehensive overview of graph learning methods for anomaly analytics tasks. We classify them into four categories based on their model architectures, namely graph convolutional network, graph attention network, graph autoencoder, and other graph learning models. The differences between these methods are also compared in a systematic manner. Furthermore, we outline several graph-based anomaly analytics applications across various domains in the real world. Finally, we discuss five potential future research directions in this rapidly growing field. ยฉ 2023 Association for Computing Machinery
์์ ๋ฏธ๋์ด ์ ๋ฃจ๋จธ ํ์ง๋ฅผ ์ํ ๊ทธ๋ํ ํฉ์ฑ๊ณฑ ์ ๊ฒฝ๋ง๊ณผ ์ดํ ์ ๋ฉ์ปค๋์ฆ
ํ์๋
ผ๋ฌธ (์์ฌ) -- ์์ธ๋ํ๊ต ๋ํ์ : ๊ณต๊ณผ๋ํ ์ปดํจํฐ๊ณตํ๋ถ, 2020. 8. ๊น์ข
๊ถ.Social media has been a great disseminator for new information and thoughts. Due to its accessibility of sharing information, however, social media has also become an ideal platform for propagations of rumors, fake news, and misinformation. Rumors on social media not only mislead the users of online but also affects the real world immensely. Thus, detecting the rumors and preventing their spread became an essential task. Previous learning-based rumor detection methods adopted to use contents, users, or propagation features of rumors. However, the methods are limited to represent rumor propagation as static graphs, which arent optimal for capturing the dynamic information of the rumors.
In this study, we propose a novel graph convolutional networks with attention mechanism model named, Dynamic GCN, for rumor detection. We first represent rumor posts with their responsive posts as dynamic graphs. The temporal information is used to generate a sequence of graph snapshots. The representation learning on graph snapshots with attention mechanism captures both structural and temporal information of rumor spreads. The conducted experiments on two real-world datasets demonstrate that our model, Dynamic GCN, achieves superior results over the state-of-the-art models in the rumor detection task.์์
๋ฏธ๋์ด๋ ๊ฐ๋ ฅํ ์ ๋ณด ์ ๋ฌ๋ ฅ์ ๊ฐ์ง ๋งค์ฒด๋ก ์๋ก์ด ์ ๋ณด์ ์๊ฐ์ ์ ํ ์ฐฝ๊ตฌ์ด๋ค. ์์
๋ฏธ๋์ด์ ํน์ง์ธ ์ ๊ทผ์ฑ์ ๋๋ก ๋ฃจ๋จธ, ๊ฐ์ง ๋ด์ค, ์๋ชป๋ ์ ๋ณด์ ์ ํ์์๋ ์ด์์ ์ธ ํ๋ซํผ์ด ๋๋ค. ์์
๋ฏธ๋์ด ์ ๋ฃจ๋จธ๋ ์จ๋ผ์ธ ์ฌ์ฉ์๋ฅผ ์ค๋ํ ๋ฟ๋ง ์๋๋ผ ๋๋ก ํ์ค ์ธ๊ณ์๋ ํฐ ์ํฅ์ ๋ฏธ์น๋ค. ๋ฐ๋ผ์, ๋ฃจ๋จธ๋ฅผ ํ์งํ๊ณ ๊ทธ ์ ํ๋ฅผ ๋ง๋ ๋
ธ๋ ฅ์ด ์๊ตฌ๋๋ค. ๊ธฐ์กด์ ๋ฃจ๋จธ ํ์ง ๋ฐฉ๋ฒ์ ๋ฃจ๋จธ์ ๋ด์ฉ, ์ฌ์ฉ์, ๋๋ ์ ํ ๊ณผ์ ์ ์ ๋ณด๋ฅผ ํน์ฑ์ผ๋ก ์ด์ฉํ๋ค. ์ด๋ฌํ ๋ฐฉ๋ฒ์ ๋ฃจ๋จธ์ ์ ํ๋ฅผ ์ ์ ๊ทธ๋ํ๋ก ํํํ๋ฉฐ ๊ทธ ๊ตฌ์กฐ์ ํน์ฑ์ ์ด์ฉํ๋ค. ํ์ง๋ง ์ด๋ ๋ฃจ๋จธ์ ๋์ ํน์ฑ์ ํฌ์ฐฉํ์ง ๋ชปํ๋ค๋ ํ๊ณ๋ฅผ ๊ฐ์ง๊ณ ์๋ค. ๋ณธ ์ฐ๊ตฌ์์๋ ๊ทธ๋ํ ํฉ์ฑ๊ณฑ ์ ๊ฒฝ๋ง (graph convolutional networks: GCN)๊ณผ ์ดํ
์
๋ฉ์ปค๋์ฆ (attention mechanism)์ ํ์ฉํ ๋์ ๊ทธ๋ํ ํฉ์ฑ๊ณฑ ์ ๊ฒฝ๋ง (Dynamic GCN) ๋ฃจ๋จธ ํ์ง ๋ชจ๋ธ์ ์ ์ํ๋ค. ๋จผ์ , ์์
๋ฏธ๋์ด ์ ๋ฃจ๋จธ ๊ฒ์๊ธ๋ค (posts) ๊ณผ ๊ทธ์ ๋ต์ฅ์ด ๋๋ ๊ธ๋ค(responsive posts)์ ์ด์ฉํ์ฌ ๋ฃจ๋จธ์ ์ ํ ๊ณผ์ ์ ์ ์ ๊ทธ๋ํ๋ก ํํํ๋ค. ์๊ฐ ์ ๋ณด๋ฅผ ํตํด ์ ํ ๊ณผ์ ์ ํฌํจํ๊ณ ์๋ ์ ์ ๊ทธ๋ํ์ ์งํฉ์ธ ๊ทธ๋ํ ์ค๋
์ (graph snapshot) ์ํ์ค (sequence)๋ฅผ ๋ง๋ค๊ฒ ๋๋ค. ์ดํ
์
๋ฉ์ปค๋์ฆ์ ํ์ฉํ ๊ทธ๋ํ ์ค๋
์ ํํ ํ์ต์ ๋ฃจ๋จธ ์ ํ์ ๊ตฌ์กฐ์ ์๊ฐ์ ์ ๋ณด๋ฅผ ๋ชจ๋ ํจ๊ณผ์ ์ผ๋ก ๋ฐ์ํ๋ค. ์ค์ ํธ์ํฐ ๋ฐ์ดํฐ๋ฅผ ์ด์ฉํ ์คํ์ ํตํ์ฌ ์ ์๋ ๋ชจ๋ธ์ ์ฑ๋ฅ์ด ๋ค๋ฅธ ๋น๊ต ๋ชจ๋ธ๋ค๋ณด๋ค ๋์์ ํ์ธํ ์ ์์๋ค.Chapter I Introduction 1
Chapter II Related Work 5
2.1 Rumor Detection 5
2.2 Graph Convolutional Networks 6
2.3 Learning Sequences & Attention Mechanism 7
Chapter III Problem Definition 9
Chapter IV Dynamic GCN with Attention Mechanism 11
4.1 Snapshot Generation 13
4.2 Graph Convolutional Networks 14
4.3 Readout Layer 15
4.4 Attention Mechanism 16
4.5 Training & Prediction 17
Chapter V Experiments 18
5.1 Datasets 18
5.2 Baselines 20
5.3 Experimental Setup & Implementation Details 21
5.4 Performance Evaluations 24
5.5 Ablation Study 25
Chapter VI Conclusion 30
Bibliography 31
์ด ๋ก 40Maste
Rumor Detection with Diverse Counterfactual Evidence
The growth in social media has exacerbated the threat of fake news to
individuals and communities. This draws increasing attention to developing
efficient and timely rumor detection methods. The prevailing approaches resort
to graph neural networks (GNNs) to exploit the post-propagation patterns of the
rumor-spreading process. However, these methods lack inherent interpretation of
rumor detection due to the black-box nature of GNNs. Moreover, these methods
suffer from less robust results as they employ all the propagation patterns for
rumor detection. In this paper, we address the above issues with the proposed
Diverse Counterfactual Evidence framework for Rumor Detection (DCE-RD). Our
intuition is to exploit the diverse counterfactual evidence of an event graph
to serve as multi-view interpretations, which are further aggregated for robust
rumor detection results. Specifically, our method first designs a subgraph
generation strategy to efficiently generate different subgraphs of the event
graph. We constrain the removal of these subgraphs to cause the change in rumor
detection results. Thus, these subgraphs naturally serve as counterfactual
evidence for rumor detection. To achieve multi-view interpretation, we design a
diversity loss inspired by Determinantal Point Processes (DPP) to encourage
diversity among the counterfactual evidence. A GNN-based rumor detection model
further aggregates the diverse counterfactual evidence discovered by the
proposed DCE-RD to achieve interpretable and robust rumor detection results.
Extensive experiments on two real-world datasets show the superior performance
of our method. Our code is available at https://github.com/Vicinity111/DCE-RD
FANG: Leveraging Social Context for Fake News Detection Using Graph Representation
We propose Factual News Graph (FANG), a novel graphical social context
representation and learning framework for fake news detection. Unlike previous
contextual models that have targeted performance, our focus is on
representation learning. Compared to transductive models, FANG is scalable in
training as it does not have to maintain all nodes, and it is efficient at
inference time, without the need to re-process the entire graph. Our
experimental results show that FANG is better at capturing the social context
into a high fidelity representation, compared to recent graphical and
non-graphical models. In particular, FANG yields significant improvements for
the task of fake news detection, and it is robust in the case of limited
training data. We further demonstrate that the representations learned by FANG
generalize to related tasks, such as predicting the factuality of reporting of
a news medium.Comment: To appear in CIKM 202
Fake News Detection Through Graph-based Neural Networks: A Survey
The popularity of online social networks has enabled rapid dissemination of
information. People now can share and consume information much more rapidly
than ever before. However, low-quality and/or accidentally/deliberately fake
information can also spread rapidly. This can lead to considerable and negative
impacts on society. Identifying, labelling and debunking online misinformation
as early as possible has become an increasingly urgent problem. Many methods
have been proposed to detect fake news including many deep learning and
graph-based approaches. In recent years, graph-based methods have yielded
strong results, as they can closely model the social context and propagation
process of online news. In this paper, we present a systematic review of fake
news detection studies based on graph-based and deep learning-based techniques.
We classify existing graph-based methods into knowledge-driven methods,
propagation-based methods, and heterogeneous social context-based methods,
depending on how a graph structure is constructed to model news related
information flows. We further discuss the challenges and open problems in
graph-based fake news detection and identify future research directions.Comment: 18 pages, 3 tables, 7 figure
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