Dynamic features based rumor detection method

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August undefined, 2024

WebApr 4, 2024 · The dynamic rumor influence minimization (DRIM) problem is introduced, a step-by-step discrete time optimization method for controlling rumors and a dynamic rumor-blocking approach, namely RLDB, based on deep reinforcement learning is provided. Spreading malicious rumors on social networks such as Facebook, Twitter, … WebMay 1, 2024 · Therefore, some researchers study rumor detection methods based on the semantic information of posts and their dissemination structure. For example, Ma et al. [16] develope a tree-structured neural network to capture the semantic information and propagation thread. ... [28] integrate the static features such as basic user information …

Dynamic graph convolutional networks with attention mechanism for rumor ...

Web2 days ago · The conducted experiments on three real-world datasets demonstrate the superiority of Dynamic GCN over the state-of-the-art methods in the rumor detection task. View WebOct 6, 2024 · Rumor detection methods are mainly divided into two categories—content feature-based methods and propagation structure-based methods. The methods based on content features mainly use … north isobelshire

An End-to-End Rumor Detection Model Based on Feature …

WebJan 17, 2024 · Social media has been developing rapidly in public due to its nature of spreading new information, which leads to rumors being circulated. Meanwhile, detecting rumors from such massive information in social media is becoming an arduous challenge. Therefore, some deep learning methods are applied to discover rumors through the … WebOct 12, 2024 · Rumor detection methods based on propagation structure usually analyze the propagation paths or networks formed by retweets and comments of blog posts to … WebSocial media features will an ideal platform for the propagation of rumors, fake news, press misinformation. Rumors on socializing media not only trick online users not also manipulate and real our immensely. Thus, discern the rumors and preventing their propagation became an essential task. Any of the recent deep learning-based rumor detection methods, … how to say in conclusion in german

Heterogeneous Graph Convolutional Network-Based Dynamic Rumo…

DGTR: Dynamic graph transformer for rumor detection

Webconsider the event-level rumor detection task. There is a set of posts in each event and the objective is to identify whether the event is a rumor by leverage the posts in it. Below we summarize the related work on rumor detection based on the information they utilize. Most content-based methods leverage the characteristics WebNov 23, 2024 · This work proposes a novel framework for unsupervised rumor detection that relies on an online post's content and social features using state-of-the-art clustering techniques. The proposed architecture outperforms several existing baselines and performs better than several supervised techniques. The proposed method, being lightweight, … north is northWebAug 1, 2024 · Traditional feature-based approaches extract features from the false rumor message, its author, as well as the statistics of its responses to form a flat feature vector. north israel and south judah

"WebApr 13, 2024 · Rumors may bring a negative impact on social life, and compared with pure textual rumors, online rumors with multiple modalities at the same time are more likely to mislead users and spread, so multimodal rumor detection cannot be ignored. Current detection methods for multimodal rumors do not focus on the fusion of text and picture … " - Dynamic features based rumor detection method

Dynamic features based rumor detection method

Enhancing Rumor Detection in Social Media Using Dynamic Propagation ...

WebMay 6, 2024 · Most existing methods learn event-specific features that can not be transferred to unseen events. This paper proposed an end-to-end framework named Event Adversarial Neural Network (EANN), which can derive event-invariant features with adversarial learning and thus benefit the detection of fake news on newly arrived … WebApr 20, 2024 · A novel two-layer GRU model for rumor events detection based on a Sentiment Dictionary and a dynamic time series (DTS) algorithm, named as SD-DTS-GRU, which learns continuous representations of microblog events in a better manner by making use of the SD to identify fine-grained human emotional expressions of each event and …

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WebSep 30, 2024 · 3.1 Problem Definition. In general, rumor detection in social media could be formulated as a binary classification problem, which will be defined as follow: Given a set of Weibo (or Twitter) events E = {e 1, e 2, e 3,…}, where e i represents an event containing a number of microblogs (or tweets). For computational efficiency, we follow previous work … WebAug 18, 2024 · In Fig 3, we illustrated the two different methods of snapshot generations. Here on the index i for the claim ci will be omitted. S(t) is the graph snapshot at the time step t. Each graph snapshot in S will have separate adjacency matrices A = { A(1), A(2), , A(T) } with S(t) = V(t), E(t). Fig 3.

WebJul 1, 2016 · Based on the classical SI epidemic model, in this work, the users in an online social network can be divided into two classes depending on their different states: … WebSince deep learning- based methods offer promising solutions in this area, we majorly discuss the baseline methods related to deep-based unimodal and multimodal fake news detection. 2.1 Unimodal fake news detection Jae-Seung Shim et al. [13] proposed a context-based approach that utilizes the network information of the user and vectorizes it …

Webunified framework for effective rumor detection. Experimental results on two real-world social media datasets demonstrate the salience of dynamic propagation structure … WebThe ODE-based dynamic module leverages a GCN integrated with an ordinary differential system to explore dynamic features of heterogeneous graphs. To evaluate the …

WebApr 5, 2024 · The lexicon-based sentiment classification method classifies the sentiment of text by using the statistical features of sentiment from researchers’ experience or experts’ opinions etc. This kind of method needs to continuously expand the lexicon and some new words, and its accuracy rate of text sentiment analysis is not high enough.

WebExisting work on rumor detection concentrates more on the utilization of textual features, but diffusion structure itself can provide critical propagating information in identifying … north is southWebMay 12, 2024 · In this paper, a deep neural network- (DNN-) based feature aggregation modeling method is proposed, which makes full use of the knowledge of propagation pattern feature and text content feature of … how to say in conclusion in filipinoWebAug 18, 2024 · Rumor detection on social media is a task of classifying messages or posts with their veracity labels. Traditional approaches in rumor detection and other … how to say income in italianWebAug 18, 2024 · Thus, detecting the rumors and preventing their spread became an essential task. Some of the recent deep learning-based rumor detection methods, such as Bi-Directional Graph Convolutional Networks (Bi-GCN), represent rumor using the completed stage of the rumor diffusion and try to learn the structural information from it. north is still north clarence thomas how to say incompetent in spanishWebHence, the selection and extraction of features are signiﬁcant to rumor identi-ﬁcation. Takahashi et al. [25] found the diﬀerences in vocabulary distribution between rumors and non-rumors and use this feature for detection. Sun et al. [24] extracted 15 features related to content, users proﬁles, and multimedia to identify event rumors. how to say in conclusion in other wordsWebsentiment features into rumor detection. Wu et al. [10] proposed to capture the high-order propagation patterns to improve rumor detection. Most of these feature-based methods are biased, time-consuming and limited. They are usually designed for speciﬁc scenarios and hence cannot be easily generalized for other appli-cations. how to say incontinence