DoubleH: Twitter User Stance Detection via Bipartite Graph Neural Networks

• URL: http://arxiv.org/abs/2301.08774v1
• Date: Fri, 20 Jan 2023 19:20:10 GMT
• Title: DoubleH: Twitter User Stance Detection via Bipartite Graph Neural Networks
• Authors: Chong Zhang, Zhenkun Zhou, Xingyu Peng, Ke Xu
• Abstract summary: We crawl a large-scale dataset of the 2020 US presidential election and automatically label all users by manually tagged hashtags. We propose a bipartite graph neural network model, DoubleH, which aims to better utilize homogeneous and heterogeneous information in user stance detection tasks.
• Score: 9.350629400940493
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Given the development and abundance of social media, studying the stance of social media users is a challenging and pressing issue. Social media users express their stance by posting tweets and retweeting. Therefore, the homogeneous relationship between users and the heterogeneous relationship between users and tweets are relevant for the stance detection task. Recently, graph neural networks (GNNs) have developed rapidly and have been applied to social media research. In this paper, we crawl a large-scale dataset of the 2020 US presidential election and automatically label all users by manually tagged hashtags. Subsequently, we propose a bipartite graph neural network model, DoubleH, which aims to better utilize homogeneous and heterogeneous information in user stance detection tasks. Specifically, we first construct a bipartite graph based on posting and retweeting relations for two kinds of nodes, including users and tweets. We then iteratively update the node's representation by extracting and separately processing heterogeneous and homogeneous information in the node's neighbors. Finally, the representations of user nodes are used for user stance classification. Experimental results show that DoubleH outperforms the state-of-the-art methods on popular benchmarks. Further analysis illustrates the model's utilization of information and demonstrates stability and efficiency at different numbers of layers.

Related papers

• The Heterophilic Snowflake Hypothesis: Training and Empowering GNNs for Heterophilic Graphs [59.03660013787925]
  We introduce the Heterophily Snowflake Hypothesis and provide an effective solution to guide and facilitate research on heterophilic graphs. Our observations show that our framework acts as a versatile operator for diverse tasks. It can be integrated into various GNN frameworks, boosting performance in-depth and offering an explainable approach to choosing the optimal network depth.
  arXiv  Detail & Related papers  (2024-06-18T12:16:00Z)
• Enhancing Fake News Detection in Social Media via Label Propagation on Cross-modal Tweet Graph [19.409935976725446]
  We present a novel method for detecting fake news in social media. Our method densifies the graph's connectivity to capture denser interaction better. We use three publicly available fake news datasets, Twitter, PHEME, and Weibo, for evaluation.
  arXiv  Detail & Related papers  (2024-06-14T09:55:54Z)
• Two-Stage Stance Labeling: User-Hashtag Heuristics with Graph Neural Networks [2.474908349649168]
  We develop a two stage stance labeling method that utilizes the user-hashtag bipartite graph and the user-user interaction graph. In the first stage, a simple and efficient for stance labeling uses the user-hashtag bipartite graph to update the stance association of user and hashtag nodes. This set of soft labels is then integrated with the user-user interaction graph to train a graph neural network (GNN) model.
  arXiv  Detail & Related papers  (2024-04-16T02:18:30Z)
• Analyzing Trendy Twitter Hashtags in the 2022 French Election [0.0]
  We propose a method for using semantic networks as user-level features for machine learning tasks. We conducted an experiment using a semantic network of 1037 Twitter hashtags from a corpus of 3.7 million tweets related to the 2022 French presidential election. Our semantic feature performs well with the regression with most emotions having $R2$ above 0.5.
  arXiv  Detail & Related papers  (2023-10-11T15:17:55Z)
• Seq-HGNN: Learning Sequential Node Representation on Heterogeneous Graph [57.2953563124339]
  We propose a novel heterogeneous graph neural network with sequential node representation, namely Seq-HGNN. We conduct extensive experiments on four widely used datasets from Heterogeneous Graph Benchmark (HGB) and Open Graph Benchmark (OGB)
  arXiv  Detail & Related papers  (2023-05-18T07:27:18Z)
• Detecting Political Opinions in Tweets through Bipartite Graph Analysis: A Skip Aggregation Graph Convolution Approach [9.350629400940493]
  We focus on the 2020 US presidential election and create a large-scale dataset from Twitter. To detect political opinions in tweets, we build a user-tweet bipartite graph based on users' posting and retweeting behaviors. We introduce a novel skip aggregation mechanism that makes tweet nodes aggregate information from second-order neighbors.
  arXiv  Detail & Related papers  (2023-04-22T10:38:35Z)
• Evidential Temporal-aware Graph-based Social Event Detection via Dempster-Shafer Theory [76.4580340399321]
  We propose ETGNN, a novel Evidential Temporal-aware Graph Neural Network. We construct view-specific graphs whose nodes are the texts and edges are determined by several types of shared elements respectively. Considering the view-specific uncertainty, the representations of all views are converted into mass functions through evidential deep learning (EDL) neural networks.
  arXiv  Detail & Related papers  (2022-05-24T16:22:40Z)
• HL-Net: Heterophily Learning Network for Scene Graph Generation [90.2766568914452]
  We propose a novel Heterophily Learning Network (HL-Net) to explore the homophily and heterophily between objects/relationships in scene graphs. HL-Net comprises the following 1) an adaptive reweighting transformer module, which adaptively integrates the information from different layers to exploit both the heterophily and homophily in objects. We conducted extensive experiments on two public datasets: Visual Genome (VG) and Open Images (OI)
  arXiv  Detail & Related papers  (2022-05-03T06:00:29Z)
• Interpretable Signed Link Prediction with Signed Infomax Hyperbolic Graph [54.03786611989613]
  signed link prediction in social networks aims to reveal the underlying relationships (i.e. links) among users (i.e. nodes) We develop a unified framework, termed as Signed Infomax Hyperbolic Graph (textbfSIHG) In order to model high-order user relations and complex hierarchies, the node embeddings are projected and measured in a hyperbolic space with a lower distortion.
  arXiv  Detail & Related papers  (2020-11-25T05:09:03Z)
• Exploiting Heterogeneous Graph Neural Networks with Latent Worker/Task Correlation Information for Label Aggregation in Crowdsourcing [72.34616482076572]
  Crowdsourcing has attracted much attention for its convenience to collect labels from non-expert workers instead of experts. We propose a novel framework based on graph neural networks for aggregating crowd labels.
  arXiv  Detail & Related papers  (2020-10-25T10:12:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.