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公开(公告)号：US20210089579A1
公开(公告)日：2021-03-25
申请号：US17018877
申请日：2020-09-11
Inventor： Kai Shu , Deepak Mahudeswaran , Huan Liu
IPC: G06F16/906 , G06F16/9035 , G06F16/903 , G06F16/9032 , H04L12/58 , G06F16/951
Abstract: Detecting fake news involves analyzing a distribution of publishers who publish many news articles, analyzing a distribution of various topics relating to the published news articles, analyzing a social media context relating to the published news articles, and detecting fake news articles among the news articles based on the analysis of the distribution of publishers, the analysis of the distribution of the various topics, and the analysis of the social media context. Detecting fake news alternatively involves receiving online news articles including both fake online news articles and real online news articles, creating a hierarchical macro-level propagation network of the fake online news and real online news articles, the hierarchical macro-level propagation network comprising news nodes, social media post nodes, and social media repost nodes, creating a hierarchical micro-level propagation network of the fake online news and real online news articles, the hierarchical micro-level propagation network comprising reply nodes, analyzing structural and temporal features of the hierarchical macro-level propagation network, analyzing structural, temporal, and linguistic features of the hierarchical micro-level propagation network, and identifying fake news among the online news articles based on the analysis of the structural and temporal features of the hierarchical macro-level propagation network and the analysis of the structural, temporal, and linguistic features of the hierarchical micro-level propagation network.
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公开(公告)号：US20220182351A1
公开(公告)日：2022-06-09
申请号：US17546648
申请日：2021-12-09
Inventor： Lu Cheng , Kai Shu , Siqi Wu , Yasin Silva , Deborah Hall , Huan Liu
Abstract: A computer-implemented framework and/or system for cyberbullying detection is disclosed. The system includes two main components: (1) A representation learning network that encodes the social media session by exploiting multi-modal features, e.g., text, network, and time; and (2) a multi-task learning network that simultaneously fits the comment inter-arrival times and estimates the bullying likelihood based on a Gaussian Mixture Model. The system jointly optimizes the parameters of both components to overcome the shortcomings of decoupled training. The system includes an unsupervised cyberbullying detection model that not only experimentally outperforms the state-of-the-art unsupervised models, but also achieves competitive performance compared to supervised models.
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公开(公告)号：US11494446B2
公开(公告)日：2022-11-08
申请号：US17018877
申请日：2020-09-11
Inventor： Kai Shu , Deepak Mahudeswaran , Huan Liu
IPC: G06F16/906 , G06F16/9035 , G06F16/903 , G06F16/951 , H04L51/02 , G06F16/9032 , H04L51/52
Abstract: Detecting fake news involves analyzing a distribution of publishers who publish many news articles, analyzing a distribution of various topics relating to the published news articles, analyzing a social media context relating to the published news articles, and detecting fake news articles among the news articles based on the analysis of the distribution of publishers, the analysis of the distribution of the various topics, and the analysis of the social media context. Detecting fake news alternatively involves receiving online news articles including both fake online news articles and real online news articles, creating a hierarchical macro-level propagation network of the fake online news and real online news articles, the hierarchical macro-level propagation network comprising news nodes, social media post nodes, and social media repost nodes, creating a hierarchical micro-level propagation network of the fake online news and real online news articles, the hierarchical micro-level propagation network comprising reply nodes, analyzing structural and temporal features of the hierarchical macro-level propagation network, analyzing structural, temporal, and linguistic features of the hierarchical micro-level propagation network, and identifying fake news among the online news articles based on the analysis of the structural and temporal features of the hierarchical macro-level propagation network and the analysis of the structural, temporal, and linguistic features of the hierarchical micro-level propagation network.
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公开(公告)号：US11916866B2
公开(公告)日：2024-02-27
申请号：US17546648
申请日：2021-12-09
Inventor： Lu Cheng , Kai Shu , Siqi Wu , Yasin Silva , Deborah Hall , Huan Liu
IPC: H04L51/52 , G06N3/088 , G06F40/30 , H04L51/212 , H04L67/50
CPC classification number: H04L51/52 , G06F40/30 , G06N3/088 , H04L51/212 , H04L67/535
Abstract: A computer-implemented framework and/or system for cyberbullying detection is disclosed. The system includes two main components: (1) A representation learning network that encodes the social media session by exploiting multi-modal features, e.g., text, network, and time; and (2) a multi-task learning network that simultaneously fits the comment inter-arrival times and estimates the bullying likelihood based on a Gaussian Mixture Model. The system jointly optimizes the parameters of both components to overcome the shortcomings of decoupled training. The system includes an unsupervised cyberbullying detection model that not only experimentally outperforms the state-of-the-art unsupervised models, but also achieves competitive performance compared to supervised models.
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