Rumor Detection on Social Media via Fused Semantic Information and a Propagation Heterogeneous Graph

Social media had a revolutionary impact because it provides an ideal platform for share information; however, it also leads to the publication and spreading of rumors. Existing rumor detection methods have relied on finding cues from only user-generated content, user profiles, or the structures of wide propagation. However, the previous works have ignored the organic combination of wide dispersion structures in rumor detection and text semantics. To this end, we propose KZWANG, a framework for rumor detection that provides sufficient domain knowledge to classify rumors accurately, and semantic information and a propagation heterogeneous graph are symmetry fused together. We utilize an attention mechanism to learn a semantic representation of text and introduce a GCN to capture the global and local relationships among all the source microblogs, reposts, and users. An organic combination of text semantics and propagating heterogeneous graphs is then used to train a rumor detection classifier. Experiments on Sina Weibo, Twitter15, and Twitter16 rumor detection datasets demonstrate the proposed model’s superiority over baseline methods. We also conduct an ablation study to understand the relative contributions of the various aspects of the method we proposed.

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An Adaptive Deep Transfer Learning Model for Rumor Detection without Sufficient Identified Rumors

Mathematical Problems in Engineering ◽

10.1155/2020/7562567 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12

Author(s):

Meicheng Guo ◽

Zhiwei Xu ◽

Limin Liu ◽

Mengjie Guo ◽

Yujun Zhang

Keyword(s):

Social Media ◽

Parameter Tuning ◽

Training Data ◽

Detection Methods ◽

Transfer Model ◽

Detection Model ◽

Adaptive Parameter ◽

Social Media Platforms ◽

Real World Datasets ◽

Rumor Detection

With the extensive usage of social media platforms, spam information, especially rumors, has become a serious problem of social network platforms. The rumors make it difficult for people to get credible information from Internet and cause social panic. Existing detection methods always rely on a large amount of training data. However, the number of the identified rumors is always insufficient for developing a stable detection model. To handle this problem, we proposed a deep transfer model to achieve accurate rumor detection in social media platforms. In detail, an adaptive parameter tuning method is proposed to solve the negative transferring problem in the parameter transferring process. Experiments based on real-world datasets demonstrate that the proposed model achieves more accurate rumor detection and significantly outperforms state-of-the-art rumor detection models.

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Dynamic graph convolutional networks with attention mechanism for rumor detection on social media

PLoS ONE ◽

10.1371/journal.pone.0256039 ◽

2021 ◽

Vol 16 (8) ◽

pp. e0256039

Author(s):

Jiho Choi ◽

Taewook Ko ◽

Younhyuk Choi ◽

Hyungho Byun ◽

Chong-kwon Kim

Keyword(s):

Social Media ◽

Real World ◽

Structural Information ◽

Attention Mechanism ◽

Temporal Information ◽

Detection Methods ◽

Dynamic Graph ◽

Convolutional Networks ◽

Real World Datasets ◽

Rumor Detection

Social media has become an ideal platform for the propagation of rumors, fake news, and misinformation. Rumors on social media not only mislead online users but also affect the real world immensely. 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. However, these methods are limited to represent rumor propagation as a static graph, which isn’t optimal for capturing the dynamic information of the rumors. In this study, we propose novel graph convolutional networks with attention mechanisms, 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 three real-world datasets demonstrate the superiority of Dynamic GCN over the state-of-the-art methods in the rumor detection task.

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MANIN: Multi-Attention Neural Interaction Networks for Social Rumor Detection

10.21203/rs.3.rs-888834/v1 ◽

2021 ◽

Author(s):

Sun Jiehu ◽

Wu Yue

Keyword(s):

Social Networks ◽

Semantic Information ◽

Interaction Network ◽

Detection Methods ◽

Accuracy Rate ◽

User Interactions ◽

Neural Interaction ◽

Public Networks ◽

Rumor Detection ◽

Textual Content

Abstract With the fast-changing development of emerging online media, it has be-come apparent that information on social networks is characterized by extensive, fast and timely spreading. The absence of effective detection methods and moni-toring means has led to a massive outbreak of rumors. Therefore, accurate detection and timely suppression of rumors in social networks is a vital task in maintaining social security and purifying public networks. Most existing work relies only on monotonous textual content and shallow semantic information, and lacks critical at-tention to and potential mining of user relationships. Such being the case, we can better improve these problems by employing attention mechanisms. In this paper, we proposea Multi-Attention Neural Interaction Network (MANIN) for rumor detection, which consists mainly of a self-attention-based BERT encoder, a post-comment co-attention mechanism, and a graph attention neural network for mining potential user interactions. We have conducted numerous experiments on real datasets and verified their validity, and the results show that the model proposed by us outperforms existing models with an accuracy rate of 81.6%.

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Rumor Detection on Social Media with Graph Structured Adversarial Learning

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2020/197 ◽

2020 ◽

Cited By ~ 1

Author(s):

Xiaoyu Yang ◽

Yuefei Lyu ◽

Tian Tian ◽

Yifei Liu ◽

Yudong Liu ◽

...

Keyword(s):

Social Media ◽

Detection Methods ◽

Graph Structure ◽

Adversarial Learning ◽

User Comments ◽

Text Information ◽

The Rich ◽

Real World Datasets ◽

Rumor Detection ◽

Propagation Network

The wide spread of rumors on social media has caused tremendous effects in both the online and offline world. In addition to text information, recent detection methods began to exploit the graph structure in the propagation network. However, without a rigorous design, rumors may evade such graph models using various camouflage strategies by perturbing the structured data. Our focus in this work is to develop a robust graph-based detector to identify rumors on social media from an adversarial perspective. We first build a heterogeneous information network to model the rich information among users, posts, and user comments for detection. We then propose a graph adversarial learning framework, where the attacker tries to dynamically add intentional perturbations on the graph structure to fool the detector, while the detector would learn more distinctive structure features to resist such perturbations. In this way, our model would be enhanced in both robustness and generalization. Experiments on real-world datasets demonstrate that our model achieves better results than the state-of-the-art methods.

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From rumor to genetic mutation detection with explanations: a GAN approach

Scientific Reports ◽

10.1038/s41598-021-84993-1 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Mingxi Cheng ◽

Yizhi Li ◽

Shahin Nazarian ◽

Paul Bogdan

Keyword(s):

Social Media ◽

Mutation Detection ◽

Classification Problem ◽

Genetic Mutation ◽

Detection Methods ◽

Generative Adversarial Networks ◽

Discriminative Models ◽

Vigorous Growth ◽

Rumor Detection

AbstractSocial media have emerged as increasingly popular means and environments for information gathering and propagation. This vigorous growth of social media contributed not only to a pandemic (fast-spreading and far-reaching) of rumors and misinformation, but also to an urgent need for text-based rumor detection strategies. To speed up the detection of misinformation, traditional rumor detection methods based on hand-crafted feature selection need to be replaced by automatic artificial intelligence (AI) approaches. AI decision making systems require to provide explanations in order to assure users of their trustworthiness. Inspired by the thriving development of generative adversarial networks (GANs) on text applications, we propose a GAN-based layered model for rumor detection with explanations. To demonstrate the universality of the proposed approach, we demonstrate its benefits on a gene classification with mutation detection case study. Similarly to the rumor detection, the gene classification can also be formulated as a text-based classification problem. Unlike fake news detection that needs a previously collected verified news database, our model provides explanations in rumor detection based on tweet-level texts only without referring to a verified news database. The layered structure of both generative and discriminative models contributes to the outstanding performance. The layered generators produce rumors by intelligently inserting controversial information in non-rumors, and force the layered discriminators to detect detailed glitches and deduce exactly which parts in the sentence are problematic. On average, in the rumor detection task, our proposed model outperforms state-of-the-art baselines on PHEME dataset by $$26.85\%$$ 26.85 % in terms of macro-f1. The excellent performance of our model for textural sequences is also demonstrated by the gene mutation case study on which it achieves $$72.69\%$$ 72.69 % macro-f1 score.

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A guided latent Dirichlet allocation approach to investigate real-time latent topics of Twitter data during Hurricane Laura

Journal of Information Science ◽

10.1177/01655515211007724 ◽

2021 ◽

pp. 016555152110077

Author(s):

Sulong Zhou ◽

Pengyu Kan ◽

Qunying Huang ◽

Janet Silbernagel

Keyword(s):

Social Media ◽

Real Time ◽

Language Processing ◽

Disaster Response ◽

Domain Knowledge ◽

Latent Dirichlet Allocation ◽

Situational Awareness ◽

High Performing ◽

Latent Topics ◽

Dirichlet Allocation

Natural disasters cause significant damage, casualties and economical losses. Twitter has been used to support prompt disaster response and management because people tend to communicate and spread information on public social media platforms during disaster events. To retrieve real-time situational awareness (SA) information from tweets, the most effective way to mine text is using natural language processing (NLP). Among the advanced NLP models, the supervised approach can classify tweets into different categories to gain insight and leverage useful SA information from social media data. However, high-performing supervised models require domain knowledge to specify categories and involve costly labelling tasks. This research proposes a guided latent Dirichlet allocation (LDA) workflow to investigate temporal latent topics from tweets during a recent disaster event, the 2020 Hurricane Laura. With integration of prior knowledge, a coherence model, LDA topics visualisation and validation from official reports, our guided approach reveals that most tweets contain several latent topics during the 10-day period of Hurricane Laura. This result indicates that state-of-the-art supervised models have not fully utilised tweet information because they only assign each tweet a single label. In contrast, our model can not only identify emerging topics during different disaster events but also provides multilabel references to the classification schema. In addition, our results can help to quickly identify and extract SA information to responders, stakeholders and the general public so that they can adopt timely responsive strategies and wisely allocate resource during Hurricane events.

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Embed2Detect: temporally clustered embedded words for event detection in social media

Machine Learning ◽

10.1007/s10994-021-05988-7 ◽

2021 ◽

Author(s):

Hansi Hettiarachchi ◽

Mariam Adedoyin-Olowe ◽

Jagdev Bhogal ◽

Mohamed Medhat Gaber

Keyword(s):

Social Media ◽

Event Detection ◽

High Volume ◽

Detection Methods ◽

Word Embeddings ◽

Agglomerative Clustering ◽

Data Set ◽

Social Media Data ◽

Social Media Platforms ◽

Media Data

AbstractSocial media is becoming a primary medium to discuss what is happening around the world. Therefore, the data generated by social media platforms contain rich information which describes the ongoing events. Further, the timeliness associated with these data is capable of facilitating immediate insights. However, considering the dynamic nature and high volume of data production in social media data streams, it is impractical to filter the events manually and therefore, automated event detection mechanisms are invaluable to the community. Apart from a few notable exceptions, most previous research on automated event detection have focused only on statistical and syntactical features in data and lacked the involvement of underlying semantics which are important for effective information retrieval from text since they represent the connections between words and their meanings. In this paper, we propose a novel method termed Embed2Detect for event detection in social media by combining the characteristics in word embeddings and hierarchical agglomerative clustering. The adoption of word embeddings gives Embed2Detect the capability to incorporate powerful semantical features into event detection and overcome a major limitation inherent in previous approaches. We experimented our method on two recent real social media data sets which represent the sports and political domain and also compared the results to several state-of-the-art methods. The obtained results show that Embed2Detect is capable of effective and efficient event detection and it outperforms the recent event detection methods. For the sports data set, Embed2Detect achieved 27% higher F-measure than the best-performed baseline and for the political data set, it was an increase of 29%.

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A Normative Framework for Sharing Information Online

10.1093/oxfordhb/9780198857815.013.5 ◽

2021 ◽

Author(s):

Emily Sullivan ◽

Mark Alfano

Keyword(s):

Social Media ◽

The Internet ◽

Normative Framework ◽

Current State ◽

Shared Information ◽

Technological Developments ◽

State Of Affairs ◽

Share Information

People have always shared information through chains and networks of testimony. It is arguably part of what makes us human and enables us to live in cooperative communities with populations greater than 150 or so. The invention of the internet and the rise of social media have turbocharged our ability to share information. This chapter develops a normative epistemic framework for sharing information online. This framework takes into account both ethical and epistemic considerations that are intertwined in typical cases of online testimony. The authors argue that, while the current state of affairs is not entirely novel, recent technological developments call for a rethinking of the norms of testimony, as well as the articulation of a set of virtuous dispositions that people would do well to cultivate in their capacity as conduits (not just sources or receivers) of information.

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Composite Event Processing for Data Streams and Domain Knowledge

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.219-220.927 ◽

2011 ◽

Vol 219-220 ◽

pp. 927-931

Author(s):

Jun Qiang Liu ◽

Xiao Ling Guan

Keyword(s):

Query Optimization ◽

Data Streams ◽

Domain Knowledge ◽

Semantic Information ◽

Query Language ◽

Processing System ◽

Optimization Techniques ◽

Research Attention ◽

Composite Event ◽

Solid Foundation

In recent years the processing of composite event queries over data streams has attracted a lot of research attention. Traditional database techniques were not designed for stream processing system. Furthermore, example continuous queries are often formulated in declarative query language without specifying the semantics. To overcome these deficiencies, this article presents the design, implementation, and evaluation of a system that executes data streams with semantic information. Then, a set of optimization techniques are proposed for handling query. So, our approach not only makes it possible to express queries with a sound semantics, but also provides a solid foundation for query optimization. Experiment results show that our approach is effective and efficient for data streams and domain knowledge.

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Fault Detection Method Based on Global-Local Marginal Discriminant Preserving Projection for Chemical Process

Processes ◽

10.3390/pr10010122 ◽

2022 ◽

Vol 10 (1) ◽

pp. 122

Author(s):

Yang Li ◽

Fangyuan Ma ◽

Cheng Ji ◽

Jingde Wang ◽

Wei Sun

Keyword(s):

Feature Extraction ◽

Fault Detection ◽

Chemical Process ◽

Detection Method ◽

Detection Performance ◽

Detection Methods ◽

Normal Operation ◽

Discriminative Feature ◽

Inherent Feature ◽

Global And Local

Feature extraction plays a key role in fault detection methods. Most existing methods focus on comprehensive and accurate feature extraction of normal operation data to achieve better detection performance. However, discriminative features based on historical fault data are usually ignored. Aiming at this point, a global-local marginal discriminant preserving projection (GLMDPP) method is proposed for feature extraction. Considering its comprehensive consideration of global and local features, global-local preserving projection (GLPP) is used to extract the inherent feature of the data. Then, multiple marginal fisher analysis (MMFA) is introduced to extract the discriminative feature, which can better separate normal data from fault data. On the basis of fisher framework, GLPP and MMFA are integrated to extract inherent and discriminative features of the data simultaneously. Furthermore, fault detection methods based on GLMDPP are constructed and applied to the Tennessee Eastman (TE) process. Compared with the PCA and GLPP method, the effectiveness of the proposed method in fault detection is validated with the result of TE process.

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