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. Over the last decade an increasing number of people access news online (Amy Mitchell, 2016), 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 (Ribeiro et al., 2018), 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 (Gentzkow and Shapiro, 2011; Quat-trociocchi et al., 2016; Dubois and Blank, 2018; Garimella et al., 2018). An unfortunate consequence of this process is the rapid proliferation of fake news (Lazer et al., 2018), 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 (Vosoughi et al., 2018) 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 real-time 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 (Baly et al., 2020b, 2018) 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 (McPherson et al., 2001), 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 1363 engage with. 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) (Schlichtkrull et al., 2018), 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. We suggest an inference-based graph representation learning approach, which incrementally augments the graph with inferences about users' social information and content preferences. We perform extensive experiments in source-level (Baly et al., 2020a) and content-level (Nguyen et al., 2020) settings, demonstrating our inference-based graph representation approach leads to performance improvements in both cases, even in weakly supervised settings. Fake News Detection : Detecting fake news using social media has been a popular research topic recently. It's typically studied as a supervised learning task, in which a classifier is trained using representations of news and their social context to predict factuality of the content (Hassan et al., 2017; Shu et al., 2017; Shao et al., 2018; Prez-Rosas 1364 et al., 2017; Volkova and Jang, 2018; Shu et al., 2019a; Kim et al., 2019). Unfortunately, these methods cannot capture the interactions between the users and sources that share fake news on social media, which is necessary to better understand the way fake news propagates, and ultimately detect it. 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 user-article 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 source-source), 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 (Jiang et al., 2019; Chen et al., 2020). 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. 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. Our graph consists of the following nodes: (1) S , the news sources . Each sources' ( s i ) vec-1365 Figure 2: Factuality Prediction as Graph Reasoning tor consists of its Twitter and YouTube profiles embeddings (numerical + LM features details in App. A.2.1). Prior work (Baly et al., 2020b) showed that these features provide a strong signal. (2) A , the articles published by these sources. An article a i vector captures its contents using a SBERT (Reimers and Gurevych, 2019) RoBERTa (Liu et al., 2019) model, as it provides strong, meaningful sentence embeddings. (3) U , the Twitter users that interact with articles and sources, and provide the social context for them. The description is applicable to the source-level (Baly et al., 2020a) and content-level (Nguyen et al., 2020) settings, where elements in S or A , res., are our classification targets. The user vector is identical to the Twitter embedding mentioned above. The graph is formed by first adding the sources as individual nodes. Then, connecting each source with up to 300 articles ( e = { s i , a j } ). Next, we add social context to the graph via Twitter users that interact with sources: (1) Following Sources: We add up to 5000 users that follow sources, connecting each user to new sources they follow ( e = { s i , u j } ). These are likely to indicate a positive relationship. (2) Discussing Articles: We connect each article with users that tweet its title/link within a 3 month period of publication ( e = { a i , u j } ). These users provide the means for fake (and real) news spread, allowing us to model this process. Finally, social interactions, a crucial component for analyzing fake news propagation, are captured by scraping up to 5000 followers of each Twitter user, and connecting existing users with edges if they one follows another. Relational Graph Convolutional Networks (R-GCNs) (Schlichtkrull et al., 2018), that generalize traditional GCNs to handle different relationship types, thus allowing us to better capture their interactions and improve their representation. Intuitively, R-GCNs create contextualized node representations by considering the graph structure through graph convolutions and learn a composition function: h l +1 i = ReLU   (cid:80) r  R (cid:80) u  U r ( v i ) 1 z i,r W lr h lu   , where h li is the hidden representation for the i-th node at layer l and h 0 i = v i (output of the node encoder); U r ( v i ) represents v i 's neighboring nodes connected by the relation type r ; z i,r is for normalization; and W lr represents trainable parameters. To obtain meaningful node representation used for capturing factuality, we optimize the Node Classification (NC) objective of Fake News Detection. After obtaining the source representations from the R-GCN, we pass them through the softmax activation function and then train using categorical cross-entropy loss, where the labels are factuality. We define multiple inference operators that enable the creation of new edges based on learned information graph inferences. The different operators capture our intuition about how connecting node pairs of different types would contribute to trustworthiness propagation. For example, pairs of users that are not explicitly connected in the graph (i.e., do not follow each other) but share articles with similar factuality levels may have similar levels of non-trustworthiness. Connecting them would provide more information to the nodes they connect to. One of our inference operators adding user-user edges based on their node similarity in the embedding space captures this situation. For each inference operator type discussed below, we make edge connections based on the node representations we have learned, by computing similarity scores between all pairs of nodes (using the graph node embedding efficiently with FAISS (Johnson et al., 2017)), and connecting the nodes with the top k similarity scores based on our model. The first broad inference operator type adds edges between graph entities, in a similar way as a recommendation engine, suggesting entities to interact with each other, based on their graph relationships. User-Source : We add edges between users and sources ( e = { u i , s j } ), using the top k most similar source/user pairs in the embedding space. User-User : Pairs of users that interact with news in a similar way are connected ( e = { u i , u j } ). These users are likely to have the same beliefs and may even want to follow each other if they became aware of each others' profiles. User-Article : We add edges between articles and users likely to be interested them ( e = { u i , a j } )). This inference can be based on the target users' interactions with similar articles, or with other users sharing these articles. The second broad type connects entities based on content similarity. Unlike the previous set, these types of edges are not initially observed in the graph, which is one of the benefits of our setup, allowing us to add inferences about latent relationships that underlie how information propagates , such as coordination between different sources, information flooding by publishing similar content in multiple articles and bad influencers, consistently propagating low-quality content. Sources-Sources Sources likely to publish similar content at an equivalently factual level are connected ( e = { s i , s j } ). Articles-Articles Articles that could be similar to each other in content are connected ( e = { a i , a j } ). To do this effectively, we first identify articles pairs that discuss the same event, approximated using the publication date and entity mentions overlap (using Flair (Akbik et al., 2018)) in their title. Second, we use an entailment model (Parikh et al., 2016; Gardner et al., 2017) to only connect articles that entail each other, as they are more likely to be talking about similar content. Influencers Fake news is often spread by bad influencers that have a large following. Over the years, Twitter has launched campaigns intended to reduce fake news spread by suspending such users. This inference operator aims to do the same, by following these steps: (1) Using the training data, we label users by counting the paths to sources with a given factuality label. (2) Identify users without significant label variation in their followers group, as potential news influencers. We avoid users with mostly highly factuality followers. (3) At inference time, we connect new users to influencers in this initial set, with a special edge type, indicating similarity to an influencer. We add the top k Model Performance Acc MacroF1 #Edges M1 : Majority class 52.43 22.93 M2 : (Baly et al., 2018) 66.45 61.08 M3 : (Baly et al., 2020b) 71.52 67.25 M4 : Replication of (Baly et al., 2020b) 69.38 63.63 M5 : Node classification (NC) 68.90 63.72 M6 : InfOp Best Model 72.55 66.89 32K Table 1: Results on (Baly et al., 2020b). The inference operators we defined use the graph embedding function to identify new relationships that would potentially improve the embedding quality and allow for better information propagation during learning. The two steps are clearly interdependent. Now, we describe our iterative graph learning framework that builds on this dependency, and continually learns better social context representations in the graph by applying the inference operators, and then retraining the graph. It can be seen in Algorithm 1 and runs the following steps: (1) Initial Representation In this step, we train the graph G using the framework described in Sec 3.2 to get an initial graph representation. (2) Inference Step Apply inference operators (Sec 3.3) based on the learned representation. (3) Learning Step After, we continue the training process for the graph. We continually apply the two steps until convergence, based on development set performance. Additional details about the process are provided in Appendices A and C. When done, we retrain the model based on the final graph uncovered by applying the inference operators. Through this iterative approach, we continually improve our representation of the social media framework that enables fake news propagation, and reveal hidden relationships critical to understanding fake news spread. We evaluate our model's ability to predict fake news better on two challenging tasks: Fake News Source Classification, and Article Classification. To evaluate our model's ability to predict the factuality of news medium, we used the Media Bias/Fact Check dataset (Baly et al., 2018, 2020b). The public dataset consists of 859 sources, each labeled on a 3-point factuality scale: low , mixed , and high . Using the Twitter API 1 , we gather an average of 27 user engagements for each articles (Sec 3.1). Our final graph consists of 69,978 users, 93,191 articles, 164,034 nodes, and 7,196,808 edges. Details about the setup we used when training our graph (chosen using the dev set), and our scraping protocol are in Appendix A. Our code is available 2 . To evaluate fake news article detection, we used the dataset released by (Nguyen et al., 2020), put together from Twitter data using related work on ru-mor classification (Kochkina et al., 2018; Ma et al., 2016) and fake news detection (Shu et al., 2018). For each article, the dataset provides its source and a list of engaged users. We also collected the followers for each user, leading to a graph with 48,895 users, 442 sources, and 1,050 articles. Table 1 shows our results on source classification. We evaluate our models on the average of all 5 data splits released by (Baly et al., 2020b), using 20% of the training set sources as a development set. We report results on accuracy and Macro F1-score. We compare to (Baly et al., 2020b, 2018) ( M2 , 3 ), who to the best of our knowledge achieve the strongest performance on this dataset. As (Baly 1 https://developer.twitter.com/en/docs 2 https://github.com/hockeybro12/ FakeNews_Inference_Operators Model Split Performance AUC # New Edges FANG 90% 75.18 SVM 90% 75.89 NC 90% 83.48 InfOp 90% 85.89 10,000 FANG 70% 72.32 SVM 70% 59.18 NC 70% 73.15 InfOp 70% 77.76 10,000 FANG 50% 71.66 InfOp 50% 73.88 10,000 FANG 30% 70.36 InfOp 30% 72.63 10,000 FANG 10% 66.83 InfOp 10% 67.51 10,000 Table 2: On (Nguyen et al., 2020), we achieve the SOTA on all data splits (% of data used for training). et al., 2020b) did not release the article and social media data they used, we replicate their setup using the data we scraped (and their code), and compare to that as well ( M4 ). Despite us optimizing their model, our results are worse than their released performance, so we hypothesize that their data on our setup may lead to better overall performance. When training our initial graph with only observed data using the Node Classification (NC) fake news loss and the same data as our replication of (Baly et al., 2020b), we obtain similar performance to their approach ( M5 vs M4 ). When we apply our inference operators, and then train the graph identically (as in M5 ), we notice a 3 . 65% acc. improvement ( M5 vs M6 ), showing the clear benefit of our inference operator setup on this task , and answering our research question that the added information helps. Further, this setup achieves the state-of-the-art on (Baly et al., 2020b), exceeding both our replication with the same data (by 3.17% acc.) and their published results (by 1.03% acc.). Our results for article classification are in Tab 2. We compare to (Nguyen et al., 2020) (FANG), who to the best of our knowledge have the best performance, and compared to several competitive baselines in their work (Ruchansky et al., 2017). Nguyen et al. are also the most similar to us (as said in Sec 2, they also train GNN's), but they do not make unobserved interactions explicit, rather they modify the loss function they used when training to better capture them. Our setup is identical to the strong (Nguyen et al., 2020) setup (we use their 1368 released data and data splits) except we use different Twitter and Article representations, and we also consider Twitter follower edges. In addition, FANG (Nguyen et al., 2020) considered temporal aspects of how tweets propagate, which we do not, and we hypothesize that this may improve our performance. For this reason, we are using less data compared to FANG, apart from the fact that we consider Twitter user followers. For proper comparison, we also evaluate our representations by training a SVM, and in App. Tab 8, we evaluate our model with the same representations as FANG. We evaluate all of their data splits in Tab 2 (90% -> 90% of data for training, 10% for test, etc.). NC evaluates our model performance on the observed data. We show the best results (extended results and details in the App. C), and as can be seen, applying inference operators also improves performance on fake news article classification on all data splits (as much as 4.61% AUC), reinforcing that explicitly learning and creating unobserved relationships in the graph enables us to detect fake news content better. Also, we achieve SOTA by average 4.26%. In this section, we analyze our best model with inference operators (Table 1 M6 ) for fake news source detection (Baly et al., 2020b) by answering the following research questions: (1) Ablation study: What is the contribution of each inference operator? (2) Can our model learn on limited data? Does our inference-based representation help? (3) Can we learn meaningful user communities? (4) What type of inferences does our model make for each inference operator? (5) Can we detect the factuality of new content? (6) What embeddings do we learn? (App. B.1) (7) How many edges should we add for each inf. operator? (App. B.2) (8) How long does running inference operators take? (App. B.3) 5.1 Ablation Study In Table 3, we evaluate each of our inference operators, trained using our joint learning and inference algorithm for up to two iterations. To evaluate the accuracy of the edge connections we make when applying the inference operators (Inf. Acc), we compare the labels of the two nodes connected by an inferred edge (i.e., accurate decisions connect nodes with similar labels). Since labels are only associated in our data with sources, we define a heuristic for computing labels for article and user nodes based on the most common gold label in all the sources they were directly connected to in the initial graph (ex: a user that follows 3 high factuality sources is assigned a high factuality label). We also report the number of edges connected in each setup (dev set for all params). We note that almost all of our models with inference operators result in performance improvements over the baselines (Tab 1 M4 , 5 ), showing that capturing these hidden relations and making them explicit with new edges helps in fake news detection. Moreover, several of our inference operators (users-users/sources-sources) achieve high accuracy, while all perform better than random, showing that we can make useful edge connections after learning the initial information graph. Furthermore, applying multiple inference operators in multiple iterations through our setup ( InfOp Users-Users and Users-Articles) leads to the strongest performance on this task. To evaluate the potential of our approach, we also evaluate our performance if we had no inaccurate edge predictions (i.e. 100% Inf. Acc), and see significant improvements. Note that this is a potential of our setup, as it involves using all the data (including the training set) to determine the user labels and then filtering out inaccurate edge predictions. We suggest a first step, based on probabilistic inference (Pacheco and Goldwasser, 2021), described in the factor graph in Fig. 3, applied to the Users-Users operators. We define two decision variable types, F associated with a user's factuality prediction, and E associated with the inference operator outcome on a user pair. Each is associated with a scoring function,  1 scoring users factuality assignments, and  2 scoring user pairs based on embedding similarity. The assignments are con-1369 Model Performance Acc Macro F1 Std Acc. nected using two sets of constraints: C , ensuring factuality label consistency in users connected via a predicted edge, and T , ensuring transitivity across pairs of edges, sharing a node. We use MAP inference to identify the solution edge set. The results in Tab. 3, show a modest improvement (72.17) compared to local inference (71.97), obtained using significantly less edges (6.7K compared 30K). This experimentally shows a benefit of using global probabilistic inference to more intelligently determine edges to connect, rather than only using embedding similarity as we did before (here we also considered user factuality, and other decision vari-ables/scoring functions can be added in the future). Details and other potential benefits of this setup are provided in Appendix D. 5.2 Weakly Supervised Training Next, in Table 4 we evaluate our model on using limited training data for fake news source classification, by training on a smaller set of sources (still using the entire graph and full test set). Here, we see the ability of our inference operators to strongly improve performance (NC vs InfOp ), as they reveal relationships the model could not learn otherwise in the weakly supervised setting, which shows how our system could be useful for detecting recently published news. Now, we analyze how well our user-user inference operator allows us to learn information communities of users (Tab 5). To do this, we cluster (K-means, Tab 5 shows different values of K) users before ( B1 ) and after the inference operators are applied ( B2 ), and evaluate the cluster purity based on user labels. To compute purity, each cluster is assigned to the class which is most frequent in the cluster, and then the accuracy of this is measured. We assigned labels to users using the same heuristic described in Sec. 5.1. We see that the users cluster better after the inference operators are applied (even via bias labels), showing our ability to use them to form information communities. Here, we analyze the inference operators by analyzing specific edge connections that are made. We see that the model makes smart choices in connecting nodes that may be part of the same information community. For example (more in Appendix B.4), a low factuality article discussing Democrats as dangerous open border fanatics' was connected to a user with bio BuildtheWall ... DEMONRATS. 5.5 Incorporating New News Content Finally, we evaluate how well our model can incorporate unseen news content, by clustering (like before) 1500 fact-checked claims from PolitiFact 3 . In Table 5, we first cluster the initial RoBERTa em-3 https://www.politifact.com 1370 beddings of these claims ( C1 ) and then add them into the graph by connecting them to five graph articles that have similar embeddings to them ( C2 ). Next, we use our user-article inference operator to connect each of these articles to users ( C3 ). It's clear that the RoBERTa embeddings statements don't cluster well by factuality. However, once they are added into the graph ( C2 ) and after they are connected via inference operators ( C3 ), they do. This further shows how our framework, especially through inference operators, allows the better detection of unseen news content (in this case claims). Not only can we determine its factuality, but we can also determine what other users are likely to interact with it. 6 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 (Pujari and Goldwasser, 2021; Hovy and Yang, 2021; Pacheco and Goldwasser, 2021; Yang et al., 2016), a natural fit for many NLP tasks. Acknowledgements We thank the anonymous reviewers of this paper for all of their vital feedback. This work was partially supported by an NSF CAREER award IIS-2048001. 7 Ethics Statement To the best of our knowledge no code of ethics was violated throughout the experiments done in this paper. We reported all hyper-parameters and other technical details necessary to reproduce our results, and release the code and dataset we collected. We evaluated our model on two different datasets and tasks (source and article fake news classification), but it is possible that results may differ on other datasets. However, we feel our methodology is solid and applies to any social media fake news setting. For space constraint we moved some of the technical details and discussion to the Appendix section. The results we reported supports our claims in this paper and we believe it is reproducible. Any qualitative result we report is an outcome from a machine learning model that does not represent the authors' personal views. For any results that we discuss on the data we use, we will not include account information and all results will be anonymous. In our dataset release for (Baly et al., 2020b), we include sources, users, and articles. Sources are public information provided in (Baly et al., 2020b), and we map each to an ID. We scraped up to 300 articles for each source (as many as we could), which we map to an ID. We also scraped users that interact with articles, which we also release. Each user is given by their Twitter ID (which may be invalid or not provided if the user deleted their profile), and their graph ID. The Twitter ID of the Tweet the user propagates about the article is also given. We also scraped users that follow sources, and this information is released by providing the user ID's that interact with each source ID's. Finally, we provide the representations for each user, article, and source we used as our initial embedding in the graph. Our data is meant for academic research purposes and should not be used outside of academic research contexts. All our data is in English. Our framework in general does not create direct societal consequence and is intended to be used to defend against fake news. While our model could be used to build better fake news spreaders, our approach of identifying information communities through inference operators, could also prevent against that.
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