True or False? Detecting False Information on Social Media Using Graph Neural Networks (2022)

Abstract: In recent years, false information such as fake news, rumors and conspiracy theories on many relevant issues in society have proliferated. This phenomenon has been significantly amplified by the fast and inexorable spread of misinformation on social media and instant messaging platforms. With this work, we contribute to containing the negative impact on society caused by fake news. We propose a graph neural network approach for detecting false information on Twitter. We leverage the inherent structure of graph-based social media data aggregating information from short text messages (tweets), user profiles and social interactions. We use knowledge from pre-trained language models efficiently, and show that user-defined descriptions of profiles provide useful information for improved prediction performance. The empirical results indicate that our proposed framework significantly outperforms text- and user-based methods on misinformation datasets from two different domains, even in a difficult multilingual setting.

## introduction the spread of misinformation on social media is a growing problem that can hardly be tackled without the help of ai-based detection methods due to the large amount of data and its complexity. this is evident in crisis situations such as the covid-19 pandemic (naeem and boulos, 2021) or russia's attack on ukraine where a sheer flood of fake news has exacerbated the situation causing great insecurity and harm among the people. previous work has focused primarily on the verification of news content, taking into account user profiles and propagation patterns in social networks. however, in real life scenario, news articles are not always freely available, and matching usergenerated content from social media to published articles is often hard to accomplish @xcite . therefore, we propose a method for au-tomatic fake news detection that is based only on data available on social media. we introduce a unified framework with graph neural networks (gnns) that leverages short text messages, user profile information and social network properties. as a case study, we train and evaluate our model on monoand multilingual social media content from twitter. to this end, we jointly model the heterogeneous graph structure of the data formed by users, retweeters and their tweets, and cast the verification task as a node classification problem. we exploit self-defined profile descriptions from twitter users and retweeters as well as the tweets' text to create initial user and tweet node features. unlike previous approaches which use pre-trained word embeddings to encode text features @xcite or learn word-level features during training @xcite , we utilize state-of-the-art context-aware multilingual representations from sentence-bert @xcite . since we avoid expensive fine-tuning of the text encoders, we make our model efficient and easily applicable. finally, we train our system in an inductive setting, boosting its capability to reliably predict new unseen instances without the need of re-training. ## related work text-or content-based fake news detection models have been greatly enhanced by the advancement of pre-trained language models @xcite @xcite . since gnns leverage news propagation patterns and user network information, they are particularly suitable for social media data. however, gnns have only recently been introduced for the detection of false information in social networks. @xcite collect news stories and twitter content, and are the first to employ a gnn architecture to model text and user features together with the social network properties for fake news detection. @xcite extract user and tweet features, and model user propagation paths with gru-and cnn-based models. a graph convolutional network (gcn) is used to learn interactions among users who share the same content. @xcite independently train a text encoder for news content and a graph encoder for modelling the follower-following network of users spreading a new articles. @xcite cast fake news detection as a graph classification task, extract features from twitter's user objects only and use gnns to compute the dissemination of news content among multiple users. the authors tackle the problem of new, unseen data by using techniques from continual learning. finally, @xcite propose a gnn-based method for user preference-aware fake news detection which exploits historical user posts for node generation and models propagation patterns of news articles among respective retweeters. except for lu and li (2020) and @xcite , the above-mentioned approaches incorporate extensive text content from news articles. we follow the approach of lu and li (2020) by addressing the challenge of classifying short and noisy text documents, but instead of applying gnns to user networks only, we propose a unified way of modelling user interactions and user-created content with a graph-based approach. moreover, none of the previous works address multilingual aspects of social media messages and user origins. we show that our approach significantly outperforms textand user-based baselines even in a multilingual setting. ## results and analysis for each model architecture, we report the mean f 1 of the positive class ('fake') of five runs with different random seeds. the results are listed in as for fakenewsnet, distiluse-sage outperforms all baseline architectures, mpnet-sage and our proposed gnns with the gat operator. among the baseline models, additional user information (tweet-user) only helps with 'distiluse' embeddings. however, the best results are achieved with 'mpnet' representations. since 'mpnet' embeddings have a larger dimension, i.e., d @xmath0. d @xmath1, they have the ability to capture more information within shallower networks. among the gnn architectures initial 'distiluse' nodes outperform their 'mpnet' counterparts. regarding covid-19-disinfo, all 'mpnet' models outperform the 'distiluse' models. the larger embedding size seems to be useful in scenarios where little training data is available. for our proposed approach, 'mpnet' embeddings outperform 'distiluse' representations by roughly 4 percentage points in both setups, i.e., 'gat' and 'sage'. the strongest model, mpnet-sage, is more than 7 percentage points better than the strongest baseline, mpnet-tweet-user. in general, the results suggest that the covid-19-disinfo classification task is much harder than the fakenewsnet task. most likely this is due to the lack of sufficient training and validation data, since we use regularization methods to mitigate overfitting. other reasons could be the multilingual character of the content and the domain-specific vocabulary which is difficult to capture for the pretrained language models. however, the final representations of 'fake' and 'real' tweets generated by our proposed gnn detection framework are more distinct than the baseline features, and, therefore, help to improve detection performance on both datasets (see figs. 1 and 2 ). although we use publicly available data in a purely observational manner, we point out that our model may learn a 'semantic bias' @xcite towards user-defined descriptions. under certain conditions, this bias could lead to questionable results that are not intended. ## conclusion in this work, we present a simple, yet efficient gnn approach for the detection of fake news on social media. our model employs pre-trained language models to encode text features of social media messages and user profile descriptions. by jointly modelling the relations between users and their tweets and between users who shared similar content, our gnn architecture outperforms text-based models as well as models which combine text and user features from pre-trained language models. in addition, our model is able to apply its knowledge to unseen data without the need of re-training. we show that our approach has limitations in settings with insufficient training data. but with the right choice of initial node representations, the model still outperforms all baselines. in future work, we will investigate domain-adapted language models for initializing graph nodes. further, we plan to evaluate our model on similar social media content, such as reddit @xcite .