Data fusion in cyber-physical-social systems: State-of-the-art and perspectives

Twitter, as a popular social network, has been targeted by different bot attacks. Detecting social bots is a challenging task, due to their evolving capacity to avoid detection. Extensive research efforts have proposed different techniques and approaches to solving this problem. Due to the scarcity of recently updated labeled data, the performance of detection systems degrades when exposed to a new dataset. Therefore, semi-supervised learning (SSL) techniques can improve performance, using both labeled and unlabeled examples. In this paper, we propose a framework based on the multi-view graph attention mechanism using a transfer learning (TL) approach, to predict social bots. We called the framework ‘Bot-MGAT’, which stands for bot multi-view graph attention network. The framework used both labeled and unlabeled data. We used profile features to reduce the overheads of the feature engineering. We executed our experiments on a recent benchmark dataset that included representative samples of social bots with graph structural information and profile features only. We applied cross-validation to avoid uncertainty in the model’s performance. Bot-MGAT was evaluated using graph SSL techniques: single graph attention networks (GAT), graph convolutional networks (GCN), and relational graph convolutional networks (RGCN). We compared Bot-MGAT to related work in the field of bot detection. The results of Bot-MGAT with TL outperformed, with an accuracy score of 97.8%, an F1 score of 0.9842, and an MCC score of 0.9481.

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Bot-MGAT: A Transfer Learning Model Based on a Multi-View Graph Attention Network to Detect Social Bots

Rumor detection on social media through mining the social circles with high homogeneity

Social networks occupy a ubiquitous and pervasive place in the life of their users. The substantial amount of content generated and shared by social networking users offers new research opportunities across a wide variety of disciplines, including media and communication studies, linguistics, sociology, psychology, information and computer sciences, or education. This situation, in combination with the continuous growth of social media data, creates an imperative need for content organisation. Thus, large-scale text learning tasks in social environments arise as one of the most relevant problems in machine learning and data mining. Interestingly, social media data pose several challenges due to its sparse, high-dimensional and large-volume characteristics. This survey reviews the field of social media data learning, focusing on classification and clustering techniques, as they are two of the most frequent learning tasks. It reviews not only new techniques that have been developed to tackle the new challenges posed by short-texts, but also how traditional techniques can be adapted to overcome such challenges. Then, open issues and research opportunities for social media data learning are discussed.

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/F2A5A1F47D512C94BD265A2D63AFF593/S0269888919000018a.pdf/div-class-title-short-text-learning-in-social-media-a-review-div.pdf

Short-text learning in social media: a review

• Studying the social media rumor refutation effectiveness lifecycle (SMRREL) from three important aspects (i.e., lifespan, peak value, and distribution). • Exploring possible factors affecting the lifespan and peak value of SMRREL through regression models in the machine learning field (e.g., XGBoostRegressor, LGBMRegressor, CatBoostRegressor, and other ensemble algorithms) and SHapley Additive Explanations method. • Summarizing distributions of SMRREL with the help of the K-shape clustering algorithm. • Giving relevant decision-making suggestions to enhance the persistence and intensity of rumor refutation effectiveness. Rumor refutation is a common method to control rumors to address potential risks. This paper studies the social media rumor refutation effectiveness lifecycle (SMRREL), focusing on three important characteristics (i.e., lifespan, peak value, and distribution) to provide support for (1) enhancing the persistence and intensity of rumor refutation effectiveness and (2) investigating the changing law of rumor refutation effectiveness. In total, 77,080 comment records, 55,847 forward records, and other pertinent data of 251 rumor refutation microblogs from an official rumor refutation platform are collected to perform analysis. To explore how the lifespan and peak value of SMRREL are influenced by the possible affecting factors, five regressors (i.e., RFRegressor, AdaBoostRegressor, XGBoostRegressor, LGBMRegressor, and CatBoostRegressor) are trained based on the collected data. The XGBoostRegressor shows the best performance, and the results are shown and explained using SHapley Additive exPlanations (SHAP). To investigate the distribution of SMRREL, lifecycle graphs of rumor refutation effectiveness are summarized and divided into three types, i.e., Outburst, Multiple Peaks, and Steep Slope. Finally, based on the results of the SMRREL analysis, corresponding decision-making recommendations are proposed to make better persistence and intensity of rumor refutation effectiveness. 

Lifecycle research of social media rumor refutation effectiveness based on machine learning and visualization technology

MGAT-ESM: Multi-channel graph attention neural network with event-sharing module for rumor detection

A novel semi-supervised classification algorithm for short text based on fusion similarity is presented via analyzing of existing defects of short text classification algorithm. First of all, some words with the ability of indication of the category are extracted from the labeled dataset to construct a strong category features set. A valid fusion similarity measurement method is designed by combining cosine theorem and strong category features based similarity. Secondly, computing the mean value of the supervised information, and determining the virtual class center point of each class, and then finding the real class center point. Finally, we search those texts which have the highest similarity with each real class center in the unlabeled dataset, and give it the same class label with the real class center point. At the same time, we add it to the labeled collection, update the strong category features set and the similarity matrix. Repeat this process until all short texts have been labeled. Ultimately, experiments show that our method can significantly improve the efficiency of short text classification. The text of the most similarity with the center of the class.

A Novel Semi-supervised Short Text Classification Algorithm Based on Fusion Similarity

A novel clustering method based on spectral clustering theory and spectral cut standard is proposed via analyzing the characteristics of short text and the defects of the existing clustering algorithms. First of all, a weighted undirected graph is created according to spectral clustering theory, similarity between node and node is calculated on graph, and a symmetrical documents similarity matrix is constructed, which provides all information for the clustering algorithm. Inspired by Greedy strategy, we utilize prim to develop PrimMAE algorithm for the purpose of partitioning graph into two parts, in which RMcut is termination condition of partitioning process, and then it is fed into CASC algorithm to cut the documents set iteratively. Ultimately, high quality clustering results demonstrate the effectiveness of the new clustering algorithm.

A Novel Graph Partitioning Criterion Based Short Text Clustering Method

Massive rumors usually appear along with breaking news or trending topics, seriously hindering the truth. Existing rumor detection methods are mostly focused on the same domain, thus have poor performance in cross-domain scenarios due to domain shift. In this work, we propose an end-to-end instance-wise and prototype-wise contrastive learning model with cross-attention mechanism for cross-domain rumor detection. The model not only performs cross-domain
feature alignment, but also enforces target samples to align with the corresponding prototypes of a given source domain. Since target labels in a target domain are unavailable, we use a clustering-based approach with carefully initialized centers
by a batch of source domain samples to produce pseudo labels. Moreover, we use a cross-attention mechanism on a pair of source data and target data with the same labels to learn domain-invariant representations. Because the samples in a
domain pair tend to express similar semantic patterns especially on the people’s attitudes (e.g., supporting or denying) towards the same category of rumors, the discrepancy between a pair of source domain and target domain will be decreased. We conduct experiments on four groups of cross-domain datasets and show that our proposed model achieves state-of-the-art performance.

Hongyan Ran

Papers

MGAT-ESM: Multi-channel graph attention neural network with event-sharing module for rumor detection

A Novel Semi-supervised Short Text Classification Algorithm Based on Fusion Similarity

A Novel Graph Partitioning Criterion Based Short Text Clustering Method

Unsupervised Cross-Domain Rumor Detection with Contrastive Learning and Cross-Attention

A metric-learning method for few-shot cross-event rumor detection