Tackling Fake News Detection by Continually Improving Social Context Representations using Graph Neural Networks (2022)

Abstract: Easy access, variety of content, and fast widespread interactions are some of the reasons making social media increasingly popular. However, this rise has also enabled the propagation of fake news, text published by news sources with an intent to spread misinformation and sway beliefs. Detecting it is an important and challenging problem to prevent large scale misinformation and maintain a healthy society. We view fake news detection as reasoning over the relations between sources, articles they publish, and engaging users on social media in a graph framework. After embedding this information, we formulate inference operators which augment the graph edges by revealing unobserved interactions between its elements, such as similarity between documents’ contents and users’ engagement patterns. Our experiments over two challenging fake news detection tasks show that using inference operators leads to a better understanding of the social media framework enabling fake news spread, resulting in improved performance.

## introduction over the last decade an increasing number of people access news online @xcite , and use social networking platforms to engage, consume and propagate this content in their social circles. social networks provide easy means to distribute news and commentary, resulting in a sharp increase in the number of media outlets @xcite , representing a wide range of perspectives and ideologies. however, despite this diversity, content is often shared only among people that hold similar beliefs and ideologies, leading to the formation of highly segregated information communities, often referred to as "echo chambers" @xcite @xcite ). an unfortunate consequence of this process is the rapid proliferation of "fake news" @xcite , content which resembles news in form but lacks the journalistic standards ensuring its quality. social media platforms are now flooded with inaccurate, incomplete, and intentionally misleading information, which propagates at lightning speed between users sharing an echo-chamber. according to a recent study @xcite false stories spread six times faster compared to real news stories. given the volume and speed of fake news spread, manual fact checking organizations cannot be used in realtime to stop it. an alternative, which is arguably easier to scale, is to jointly model the claims and their source and ask who and what can you trust? answering this question requires modeling the complex information landscape on social media, consisting of news sources, the articles they release and their social context, corresponding to social media users engaging and sharing information in their networks. similar to previous work @xcite we formulate the problem as associating a discrete factuality level (high, low, or mixed) with news content and news sources. these works treat news factuality level assessment as a traditional classification problem, using features based on social media data. our goal in this paper is to explore a different approach, driven by the principal of social homophily @xcite , referring to the tendency of individuals to form social ties with others who share their views and preferences. we follow the observation that the political perspectives and biases expressed in the text will be reflected in the behavior of users engaging with it. together they form information communities, connecting users with each other based on their content preferences, and with sources that provide that content. in this highly connected structure, even a little evidence connecting users' preferences to false narratives can be propagated and help inform the judgements about the sources they follow and engage with. fig. 1 demonstrates the interactions between users, articles and their sources. unfortunately, much of this rich social information is not directly observed, or due to the volume of these interactions, cannot be fully sampled. to help alleviate this problem and capture this knowledge, we propose a set of inference operators, each augmenting the information graph with different relationships beyond what was initially seen. by iteratively applying these inference operators, we are able to capture more of the hidden relationships that enable the spread of fake news through social media, and are crucial for detecting it. from a technical perspective, we view fake news detection as a reasoning problem over information graphs. we use the evidence provided by our existing knowledge of high vs. low factuality content (i.e., the training data), to assess the factuality of unknown content based on observed and predicted links capturing their connections. this transductive process is done using a relational graph neural net (r-gcn) @xcite , which creates distributed representations of nodes contextualized by the graph structure, allowing us to transfer information from observed evidence nodes to unknown source nodes using graph embedding tasks. we use inference operators, which build on the similarity metric defined by the learned graph embedding, to increase the number of edges connecting the two node types. these two interdependent steps are done iteratively. based on the observed data in the information graph, we can create an initial graph-contextualized representation for each node via graph embedding training. we can see that based on the current trained model, there are three articles that are similar in content and embedding, and are represented in the figure by sharing a gray background. two are "fake news" articles, published by red background low-factuality news sources ("fakeisus" and "in-fowars"), while the right most one is published by a high-factuality source. assuming the model is not familiar with their source factuality level, then based on the observed graph information, it may not be able to distinguish between them. thus, in this work, we propose to augment the graph based on learned knowledge, via inference operators. intuitively, the goal of our inference operators is to provide additional graph edges (shown as dashed red lines), such that the graph-contextualized embeddings would capture the similarity between the two low-factuality articles and the difference compared to the high-factuality one. for example, users engaging with the left two articles follow the same social influencer, who is a high activity user. in the initial graph training, this observed relationship would increase these users' learned node similarity (yellow background) allowing our inference operators to connect them into a strong information community of like-minded users, that was not initially observed, and thus not easily represented by the graph embedding. this newly inferred relationship can be propagated through the information graph, allowing us to have more strong information about other articles/sources/users these newly connected users interact with, thus detecting fake news better. in summary, we make the following contributions: • we formulate fake news detection as a reasoning problem over an information graph. due to the above mentioned limitations, researchers have recently started using graph neural networks (gnns) (to model graphs), for this task. as they contain social media entities as nodes and link them through edges based on their observed interactions, graphs are able to better capture social context. more specifically, through edge interactions, nodes in graphs can reinforce other nodes' representations, strengthening the overall information quality. shu et al. was one of the early works, and more recently, han et al. utilized continual learning with gnns to capture the propagation cascade of fake news on twitter. however, unlike our work, these and other graph models do not uncover or model hidden relationships in the data. most similar to us, nguyen et al. proposed the factual news graph (fang) also modeling the relationship between sources, articles, and users in a graph framework, by training the model to better capture social context. however, rather than iteratively adding new explicit edges to uncover hidden interactions in the graph as we do by using inference operators, fang (nguyen et al., 2020) modified the loss function they used when training the graph to better capture user-user and userarticle interactions that already exist. despite this being effective, it does not model graph interactions that were not observed in the original data, while our approach can uncover these hidden relationships as well, and thus more strongly capture the fake news propagation landscape on social media (the information communities we make explicit can help model other content better). moreover, our framework allows the graph to be continually enhanced, so we can capture more relationships than were built into the original graph (like sourcesource), and this leads to us achieving performance improvements over their work. iterative graph learning: recently, there has also been work on learning to augment graphs, such as by using end-to-end neural models optimized for the final task @xcite . while these works do iteratively augment the graph similar to us, by doing it end-to-end they can be prone to be task specific (edges may be created solely for achieving higher classification accuracy), rather than learning a high quality social media representation. this may lead to issues at test time or in inductive settings. in our case, as we are adding edges based on learned graph similarities, we are strengthening the information communities that already exist, while uncovering hidden ones. further, we can easily control for the relations and amounts of them that are added. ## model we view fake news detection as reasoning over the relations between sources, articles, and engaging users in an information graph. we hypothesize that due to the principle of homophily, social ties leading to the formation of online communities will capture similarities and differences in content preferences within and across communities. we capture the interaction between social information and news content using a heterogeneous graph defined in sec. 3.1, and use a relational graph convolutional network (r-gcn), to create vectorized node representations for factuality prediction. the r-gcn defined in sec. 3.2 allows our model to capture the different social communities, by creating contextualized node representations. for example, an article node is represented using its contents, source, and relationships with users engaging with it (which are also represented using their relationships to other nodes). the success of us capturing the social communities through the r-gcn hinges on having strong social information (i.e., graph edges) to characterize them. providing this information might not be straight-forward, as collecting social information at scale can be costly and noisy. instead, we propose inference operators, defined in sec. 3.3, which augment the graph with new edges, using the similarity between learned nodes representation to assess their compatibility. this allows the r-gcn to enrich each newly connected nodes' contextualized representation, improving factuality classification. in sec. 3.4 we describe a reasoning framework, which iteratively enriches the graph using inference operators and computes the updated node representations based on the updated graph. the framework is depicted in fig. 2 . ## experiments we evaluate our model's ability to predict fake news better on two challenging tasks: fake news ## discussion in this section, we analyze our best model with inference operators ( ## summary and future work we propose an approach for tackling fake news detection by continually improving social context representations. to achieve this, we developed an iterative representation learning and inference framework that learns an initial graph embedding, and then applies different inference operators to reveal hidden relationships in the graph. we continually capture more knowledge about the social dynamics that allow fake news to propagate. we showed strong performance on fake news detection, across several datasets and settings. our current work looks at increasing the accuracy of the inference operators by adding external knowledge. we began exploring this direction by using an entailment model to infer article relationships using content similarity. we also explore additional ways to jointly model inference operators and capture the dependencies between them. we believe this work helps pave the way for further research connecting text analysis along with its social context @xcite @xcite , a natural fit for many nlp tasks.