Grounded Vocabulary for Image Retrieval Using a Modified Multi-Generator Generative Adversarial Network

Kim, Kuekyeng; Park, Chanjun; Seo, Jaehyung; Lim, Heuiseok

doi:10.1109/access.2021.3122547

Cited by 1 publication

(2 citation statements)

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“…K-FANG embedded the body of the article and user information projected in 100 dimensions to construct node features with KoBERT, KcBERT, 12 KoELECTRA, 13 and KLUE-RoBERTa-large. 14 In addition, K = 1 was set for the node embedding model GraphSAGE and the embeddings were updated based solely on the information of neighboring nodes.…”

Section: Model Detailsmentioning

confidence: 99%

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Utilization Strategy of User Engagements in Korean Fake News Detection

et al. 2022

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Fake News (disinformation with malicious intent) has emerged as a major social problem. To address this issue, previous studies mainly utilized single information, the news content, to detect fake news. However, using only news content in training is insufficient. Moreover, most studies did not consider the propagation aspect of fake news as a training feature. Thus, in an attempt to incorporate the ability to learn representation based on textual information and social context, this study proposed a fake news detection algorithm that thoroughly utilizes user graph in Korean fake news and dataset construction methods. In addition, a training strategy was proposed for utilizing user graph in Korean fake news detection through comparative and ablation studies. The experimental results showed that K-FANG outperformed the baseline in detecting fake news. Moreover, user engagements were found to be useful for detecting fake news even if the data contained hate speech. Finally, the validity of using stance information by expanding its class and controlling the class imbalance issues was also verified. This study provided useful implications for utilizing user information in fake news detection.

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Section: Model Detailsmentioning

confidence: 99%

“…Certain recent studies undertook another approach to utilize auxiliary information to overcome the limitation of the content-based model [14], [19], [20]. In particular, user relationship information on social media and the news consuming behavior of users are represented as auxiliary information that surrounds news.…”

Section: Introductionmentioning

confidence: 99%