You too Brutus! Trapping Hateful Users in Social Media: Challenges, Solutions & Insights

• URL: http://arxiv.org/abs/2108.00524v1
• Date: Sun, 1 Aug 2021 19:13:58 GMT
• Title: You too Brutus! Trapping Hateful Users in Social Media: Challenges, Solutions & Insights
• Authors: Mithun Das, Punyajoy Saha, Ritam Dutt, Pawan Goyal, Animesh Mukherjee and Binny Mathew
• Abstract summary: Hate speech is one of the crucial issues plaguing the online social media. We run a detailed exploration of the problem space and investigate an array of models using Graph Neural Networks (GNN) We perform detailed error analysis on the best performing text and graph based models and observe that hateful users have unique network neighborhood signatures.
• Score: 11.266863752167678
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Hate speech is regarded as one of the crucial issues plaguing the online social media. The current literature on hate speech detection leverages primarily the textual content to find hateful posts and subsequently identify hateful users. However, this methodology disregards the social connections between users. In this paper, we run a detailed exploration of the problem space and investigate an array of models ranging from purely textual to graph based to finally semi-supervised techniques using Graph Neural Networks (GNN) that utilize both textual and graph-based features. We run exhaustive experiments on two datasets -- Gab, which is loosely moderated and Twitter, which is strictly moderated. Overall the AGNN model achieves 0.791 macro F1-score on the Gab dataset and 0.780 macro F1-score on the Twitter dataset using only 5% of the labeled instances, considerably outperforming all the other models including the fully supervised ones. We perform detailed error analysis on the best performing text and graph based models and observe that hateful users have unique network neighborhood signatures and the AGNN model benefits by paying attention to these signatures. This property, as we observe, also allows the model to generalize well across platforms in a zero-shot setting. Lastly, we utilize the best performing GNN model to analyze the evolution of hateful users and their targets over time in Gab.

Related papers

• 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)
• Understanding writing style in social media with a supervised contrastively pre-trained transformer [57.48690310135374]
  Online Social Networks serve as fertile ground for harmful behavior, ranging from hate speech to the dissemination of disinformation. We introduce the Style Transformer for Authorship Representations (STAR), trained on a large corpus derived from public sources of 4.5 x 106 authored texts. Using a support base of 8 documents of 512 tokens, we can discern authors from sets of up to 1616 authors with at least 80% accuracy.
  arXiv  Detail & Related papers  (2023-10-17T09:01:17Z)
• Challenging the Myth of Graph Collaborative Filtering: a Reasoned and Reproducibility-driven Analysis [50.972595036856035]
  We present a code that successfully replicates results from six popular and recent graph recommendation models. We compare these graph models with traditional collaborative filtering models that historically performed well in offline evaluations. By investigating the information flow from users' neighborhoods, we aim to identify which models are influenced by intrinsic features in the dataset structure.
  arXiv  Detail & Related papers  (2023-08-01T09:31:44Z)
• Revisiting Hate Speech Benchmarks: From Data Curation to System Deployment [26.504056750529124]
  We present GOTHate, a large-scale code-mixed crowdsourced dataset of around 51k posts for hate speech detection from Twitter. We benchmark it with 10 recent baselines and investigate how adding endogenous signals enhances the hate speech detection task. Our solution HEN-mBERT is a modular, multilingual, mixture-of-experts model that enriches the linguistic subspace with latent endogenous signals.
  arXiv  Detail & Related papers  (2023-06-01T19:36:52Z)
• DoubleH: Twitter User Stance Detection via Bipartite Graph Neural Networks [9.350629400940493]
  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.
  arXiv  Detail & Related papers  (2023-01-20T19:20:10Z)
• Model Inversion Attacks against Graph Neural Networks [65.35955643325038]
  We study model inversion attacks against Graph Neural Networks (GNNs) In this paper, we present GraphMI to infer the private training graph data. Our experimental results show that such defenses are not sufficiently effective and call for more advanced defenses against privacy attacks.
  arXiv  Detail & Related papers  (2022-09-16T09:13:43Z)
• Graph Generative Model for Benchmarking Graph Neural Networks [73.11514658000547]
  We introduce a novel graph generative model that learns and reproduces the distribution of real-world graphs in a privacy-controlled way. Our model can successfully generate privacy-controlled, synthetic substitutes of large-scale real-world graphs that can be effectively used to benchmark GNN models.
  arXiv  Detail & Related papers  (2022-07-10T06:42:02Z)
• 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)
• Detecting Online Hate Speech: Approaches Using Weak Supervision and Network Embedding Models [2.3322477552758234]
  We propose a weak supervision deep learning model that quantitatively uncover hateful users and (ii) present a novel qualitative analysis to uncover indirect hateful conversations. We evaluate our model on 19.2M posts and show that our weak supervision model outperforms the baseline models in identifying indirect hateful interactions. We also analyze a multilayer network, constructed from two types of user interactions in Gab(quote and reply) and interaction scores from the weak supervision model as edge weights, to predict hateful users.
  arXiv  Detail & Related papers  (2020-07-24T18:13:52Z)
• Stealing Links from Graph Neural Networks [72.85344230133248]
  Recently, neural networks were extended to graph data, which are known as graph neural networks (GNNs) Due to their superior performance, GNNs have many applications, such as healthcare analytics, recommender systems, and fraud detection. We propose the first attacks to steal a graph from the outputs of a GNN model that is trained on the graph.
  arXiv  Detail & Related papers  (2020-05-05T13:22:35Z)

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.