MARINE: Multi-relational Network Embeddings with Relational Proximity and Node Attributes

Feng, Ming-Han; Hsu, Chin-Chi; Li, Cheng-Te; Yeh, Mi-Yen; Lin, Shou-De

doi:10.1145/3308558.3313715

Cited by 21 publications

(14 citation statements)

References 20 publications

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“…develop a tree-structured recursive neural networks to learn the embedding of rumor propagation structure. Although multi-relational graph embedding methods (Feng et al, 2019;Wang and Li, 2019) are able to effectively learn how different types of entities (related to source news ar- ticles) interact with each other in a heterogeneous information network for classification tasks, they cannot be applied for the inductive setting, i.e., detecting the truthfulness of new-coming tweets.…”

Section: Related Workmentioning

confidence: 99%

GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media

Lü

Li²

2020

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

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297

127

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This paper solves the fake news detection problem under a more realistic scenario on social media. Given the source short-text tweet and the corresponding sequence of retweet users without text comments, we aim at predicting whether the source tweet is fake or not, and generating explanation by highlighting the evidences on suspicious retweeters and the words they concern. We develop a novel neural network-based model, Graph-aware Co-Attention Networks (GCAN), to achieve the goal. Extensive experiments conducted on real tweet datasets exhibit that GCAN can significantly outperform state-of-the-art methods by 16% in accuracy on average. In addition, the case studies also show that GCAN can produce reasonable explanations.

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Section: Related Workmentioning

confidence: 99%

GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media

Lü

Li²

2020

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Self Cite

297

127

View full text Add to dashboard Cite

show abstract

“…[29] developed tree-structured recursive neural networks to learn the embedding of rumor propagation structure. Multi-relational graph embedding methods [28,31] can effectively learn how different types of entities (related to source news articles) interact with each other in a heterogeneous information network for classification tasks.…”

Section: Fake News Detectionmentioning

confidence: 99%

“…(2) user-based: relying on involved users' characteristics to detect fake news [24][25][26][27]. (3) network-based: encoding the propagation structure and feeding it into neural network as features [21,[28][29][30][31]. (4) hybrid-based: integrating some of the abovementioned features to perform news classification.…”

Section: Introductionmentioning

confidence: 99%

MUFFLE: Multi-Modal Fake News Influence Estimator on Twitter

Hsieh

Jiang

et al. 2022

Applied Sciences

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To alleviate the impact of fake news on our society, predicting the popularity of fake news posts on social media is a crucial problem worthy of study. However, most related studies on fake news emphasize detection only. In this paper, we focus on the issue of fake news influence prediction, i.e., inferring how popular a fake news post might become on social platforms. To achieve our goal, we propose a comprehensive framework, MUFFLE, which captures multi-modal dynamics by encoding the representation of news-related social networks, user characteristics, and content in text. The attention mechanism developed in the model can provide explainability for social or psychological analysis. To examine the effectiveness of MUFFLE, we conducted extensive experiments on real-world datasets. The experimental results show that our proposed method outperforms both state-of-the-art methods of popularity prediction and machine-based baselines in top-k NDCG and hit rate. Through the experiments, we also analyze the feature importance for predicting fake news influence via the explainability provided by MUFFLE.

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“…KGs are leveraged in various ways, including propagating user preferences over knowledge entities by RippleNet [33], multi-task learning with KG Embedding by MKR [34], applying graph attention on a user-item-attribute graph by KGAT [36], adopting LSTM to model sequential dependencies of entities and relations [39], and integrating induction of explainable rules from KG by RuleRec [23]. MARINE [11] combines homogeneous and heterogeneous graph embedding learning mechanisms to recommend links between entities. Furthermore, KGPL [31] assigns pseudo-positive labels to unobserved samples through knowledge graph neural network-based predictions so that the recommendation model can better deal with the cold-start issues.…”

Section: Related Workmentioning

confidence: 99%

FairSR: Fairness-aware Sequential Recommendation through Multi-Task Learning with Preference Graph Embeddings

Hsu

Zhang

2022

ACM Trans. Intell. Syst. Technol.

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Sequential recommendation (SR) learns from the temporal dynamics of user-item interactions to predict the next ones. Fairness-aware recommendation mitigates a variety of algorithmic biases in the learning of user preferences. This article aims at bringing a marriage between SR and algorithmic fairness. We propose a novel fairness-aware sequential recommendation task, in which a new metric, interaction fairness , is defined to estimate how recommended items are fairly interacted by users with different protected attribute groups. We propose a multi-task learning-based deep end-to-end model, FairSR, which consists of two parts. One is to learn and distill personalized sequential features from the given user and her item sequence for SR. The other is fairness-aware preference graph embedding (FPGE). The aim of FPGE is two-fold: incorporating the knowledge of users’ and items’ attributes and their correlation into entity representations, and alleviating the unfair distributions of user attributes on items. Extensive experiments conducted on three datasets show FairSR can outperform state-of-the-art SR models in recommendation performance. In addition, the recommended items by FairSR also exhibit promising interaction fairness.

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MARINE: Multi-relational Network Embeddings with Relational Proximity and Node Attributes

Cited by 21 publications

References 20 publications

GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media

GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media

MUFFLE: Multi-Modal Fake News Influence Estimator on Twitter

FairSR: Fairness-aware Sequential Recommendation through Multi-Task Learning with Preference Graph Embeddings

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