To defend against neural system-generated fake news, an effective mechanism is urgently needed. We contribute a novel benchmark for fake news detection at the knowledge element level, as well as a solution for this task which incorporates cross-media consistency checking to detect the fine-grained knowledge elements making news articles misinformative. Due to training data scarcity, we also formulate a novel data synthesis method by manipulating knowledge elements within the knowledge graph to generate noisy training data with specific, hard to detect, known inconsistencies. Our detection approach outperforms the state-of-the-art (up to 16.8% absolute accuracy gain), and more critically, yields fine-grained explanations. 1 1 Introduction In recent years, generative neural network models in natural language processing (Zellers et al., 2019) and computer vision (Choi et al., 2018) have become the frontier for malicious actors to controllably generate misinformation at scale. These realistic-looking AI-generated fake news have been shown to easily deceive humans, and it is, thus, critical for us to develop robust verification techniques against machine-generated fake news (Tan et al., 2020; Zellers et al., 2019; Kaliyar et al., 2020). Current misinformation detection approaches mainly focus on document-level fake news detection using lexical features and semantic embedding representations (Wang, 2017; Karimi et al., 2018; Tan et al., 2020). However, fake news is often generated based on manipulating (misus-ing, exaggerating, or falsifying) only a small part of the true information, namely the knowledge 1 The code, data and resources related to the misinformation detector are made publicly available at https://github. com/yrf1/InfoSurgeon for research purposes. elements (KEs, including entities, relations and events). Moreover, recent news oftentimes makes claims that do not have verified evidence yet, and evaluating the truthfulness of these real-time claims depends more on their consistency with other information conveyed in other data modalities. In this paper, we propose a new task: fine-grained, knowledge element-level cross-media information consistency checking . The task involves treating the entire multimedia news article as one whole interconnected claim, where the goal is to detect misinformative KEs across the image, caption, and body text, as revealed by inconsistencies with respect to itself, or to background knowledge. This KE-level detection approach directly points out the fake pieces of information in the news, allowing for better explainability. Figure 1 shows an example where both the text and image provide complementary information about key argument roles of an event. We present the Info rmation Surgeon (InfoSurgeon) model, which takes full advantage of state-of-the-art multimedia joint knowledge extraction techniques to analyze fine-grained event, entity, and relation elements, as well as whether these extracted KEs align consistently across modalities and background knowledge. We propose a novel probabilistic graphical neural network model to fuse the outputs from these indicators. A major challenge to performing KE level misinformation detection is the lack of training data. Hence, we additionally propose a novel approach to generate noisy training data automatically since existing fake news generators (Zellers et al., 2019) do not track the specific pieces of information generated that are fake. We take a real news article, extract a multimedia knowledge graph, and replace or insert salient nodes or edges in the graph. We track the specific manipulation operations, and regenerate the manipulated version of the news article Figure 1: The architecture of our fine-grained, KE-level fake news detection system, InfoSurgeon . using a graph-to-text approach (Ribeiro et al.,2020), while filtering out poor quality unconvincing generations through a neural adversarial filter. Experiment results show that our approach achieves 92%-95% detection accuracy, 16.8% absolute higher than the state-of-the-art approach (Tan et al., 2020). Ablation tests demonstrate the effectiveness of our new detection method. The major contributions of this paper are:  We propose a novel approach to perform fake news detection at the KE level, representing the claims in the news article as a multimedia knowledge graph and detecting the mis-informative pieces in the form of KEs for a strong explainability. We contribute InfoSurgeon , a unified framework for detecting misinformation in news articles that comprehensively incorporates source context, semantic representation, multimedia information elements, and background knowledge in a reasoning framework. Finally, we present a novel benchmark, KE level fake news detection, with a silver standard annotation dataset (15,000 multimedia document pairs) automatically generated by KG conditioned natural language generation. Given a multimedia news article, X , which consists of its body text bt , list of images im 1 ..i , list of accompanying captions c 1 ..i , and metadata m = ( domain, date, author, headline ) , our study aims to detect the presence of misinformation at two levels. In document-level detection, we classify each news article as either real or fake , overall. In knowledge element-level detection, we predict the specific set of knowledge elements in the news article conveying misinformation. Here, we refer to knowledge elements (KEs) comprehensively as the entities, relations, events, and sub-graphs/metapaths (Fu et al., 2020) in an information network. To detect the misinformative KEs, we treat each news article as one ultimate claim represented by a multimedia knowledge graph KG = ( N, E ) capturing the important information conveyed. The nodes ( N ) in the KG consist of entities ( t ), while the edges ( E ) in the KG consist of relations ( r ) or event argument roles ( a ) connecting the entities. Detecting the KEs causing a news article to be fake boils down to extracting < subject entity, predicate, object entity > triplets from the multimedia input data, and labeling all of the triplets in which the relation or event between a head entity and tail entity holds false as evidence of misinformation through binary edge classification. Figure 2 shows exam-Relation: Event: Events/Relations:  HK police   Hidden Corner   HK police   protesters  Physical.LocatedNear arrest Justice.Arrest.Detainee Justice.Arrest.Jailer  HK police   people   shoot  Conflict.Attack.Victim Conflict.Attack.Attacker  real bullets  Conflict.Attack.Instrument T = True, F = False, T F F F F Life.Die.Victim F Life.Die.Killer died = entity Figure 2: In the case of events, we ignore the event trigger denoted by 4 and connect entities by their event argument roles and event types combined e.g. , < HK police, Justice.Arrest.Jailer-Justice.Arrest.Detainee, (un-cooperative) protesters > . We evaluate document-level fake news detection based on the established metric of prediction accuracy. To evaluate fine-grained KE level fake news detection, we compute the F-score: the harmonic mean of precision and recall across KEs. This is an appropriate metric due to the imbalanced nature of fake KEs, which usually constitute the minority. As shown in Figure 1, our fine-grained multimedia fake news detection system, InfoSurgeon , extracts features from both global context and local KG. The global context nodes capture the semantic representations of the body text, images, captions, and metadata in the news article. The local KG provides an explicit representation of the key information pieces and their interactions. As a clarifying example, the entire image in a news article constitutes a global context node, while the specific objects detected in the image make up the node entities in the local KG. InfoSurgeon combines these two complementary components by connecting the global context nodes to the entity nodes in the KG extracted from the news article, thereby propagating context signals into the knowledge elements. To incorporate general context information and take advantage of cross-media inconsistencies that are more likely to exist in fake news, we compute semantic representations for each news article component to initialize the node features. We feed the body text and each caption through the summarization-based BERT encoder from Liu and Lapata (2019), which averages the encoded token embeddings across sentences through a weighted mechanism. For metadata, we run the text encoder on a string containing the article domain, publication date, author, and headline. For images, we concatenate object-based (Anderson et al., 2018) and event-based (Pratt et al., 2020) visual features. Features for the edges between global context nodes are initialized by the attention-based semantic similarity between the node features (Tan et al., 2020). Constructing a KG from each Multimedia News Article: We leverage a publicly available multimedia Information Extraction (IE) system (Li et al., 2020; Lin et al., 2020) to construct a within-document knowledge graph KG = ( N t , E r | a ) for each multimedia article. The IE system can extract 197 types of entities, 61 types of relations, and 144 types of events from text and images. Then, it performs entity linking (Pan et al., 2017) to map entities extracted from both text and images to a background knowledge base e.g. Freebase (Bol-lacker et al., 2008) and NIL (unlinkable) entity clustering for name mentions that cannot be linked, followed by cross-media event and entity coreference resolution and grounding (Lai et al., 2021; Wen et al., 2021; Pratt et al., 2020). Initializing the KG Embeddings: We define an attribute function, A : N t , E r | a ) F , that transforms each of the nodes and edges to its initial representation by concatenating the following features:  Background Embeddings For the entity nodes N t that can be linked to Freebase, we use data dump from Google Developers resources 2 to map them to their respective Wikipedia pages, which serve as a rich source of established background knowledge. Background node embedding features are initialized from passing a Long Short Term Memory networks (LSTM) based architecture (Gers et al., 2000) through the word embeddings (Pennington et al., 2014) of the first paragraph in the Wikipedia page, which usually starts with a mention of the Wiki page's title. Background edge embedding features are initialized from passing the LSTM through the paragraphs that contain the mentions of both the head and tail nodes. These embeddings are set to a default zero vector for unlinkable nodes. 2 https://developers.google.com/freebase/  News Embeddings These are the surface-level features circumstantial to the entities, relations, and events extracted. News-based node features are initialized from passing an LSTM through the word embeddings of the canonical entity mention extracted. News-based edge features are initialized from passing the LSTM through the word embeddings of the relation type or event argument role infused with head and tail entity information, in the triplet format e.g.  < HK police, Physical.LocatedNear, visible crowd > . Source Attribution This is a 4-dimensional binary vector indicating whether the KE came from the body text, image, caption, or metadata. A central idea to our misinformation detector is that edge embeddings are naturally more closely aligned to the node embeddings they are connected to for the non-fake triplets. Therefore, we learn a neural network layer to extract the hidden representations of credibility between node connections. The graphical representation of the global context and local KG network is heterogeneous in nature though, so we propagate features as follows. For the global context subgraph, potential misinformation lies in whether the images, captions, or metadata align with the overall news article. Given a global context node, u , we compute the hidden representations of credibility with all other global context node neighbors v 2 nbr ( u ) (1), and aggregate the information back to node u itself (2). h e uv = relu ( W t  [ h n u , h e uv , h n v ]) (1) h n u = relu 0 @ 1 | nbr( u ) | X v 2 nbr( u ) h e uv 1 A (2) For the local KG, potential misinformation lies in the relations or event argument roles connecting entity nodes. Given two local KG nodes u and v that are connected by an edge, we compute the hidden representation of triplet credibility as in eq (1). To further take advantage of neighborhood information, we propagate features across the global context and local KG network with graph attention and message passing. merge the extracted graph features together through AVG or MAX pooling. To strengthen signals, we further add primitive indicator values before the document level linear classifier. Tan et al. (2020) use a single binary indicator for the existence of overlap between entities in the caption and entities in the article body. We use a broader set of indicators reflecting the number of overlapping entities and events across the caption, body, and image. Knowledge Element Level Fake News Detection: Detecting misinformative knowledge elements in the KG can be treated as a binary edge classification problem, in which each edge represents the entire triplet in which it serves as the predicate. We run a linear classifier on each of the learned edge embeddings that are not directly connected to the semantic nodes, to detect if the relation or event argument role connecting two entities is normal or not. Currently, there exists no annotated dataset for KE level misinformation detection. A primary reason may be due to explicitly fake (as opposed to subtly biased) news being edited or taken down by online platforms after initial posting. Because manually labeling the misinformative KEs in a real-world news corpus is expensive, we aim to create a novel dataset with controlled synthesis of news articles and automatically generated labels for fine-grained KE level explainability in fake news detection. In this section, we propose two novel approaches that generate fake news, and at the same time, automatically label the misinformative knowledge elements. Given a set of real news articles, X real , we perform deliberate edit operations on certain salient KEs in the new articles' KG to derive a manipulated representation, KG 0 . Hence, we can generate a new article conditioned on KG 0 . The corresponding KE level label can then be automatically derived, with the manipulated elements as fake and the unaltered elements as real, while the document-level label for the new generated article is fake. Given a pristine, real news article, we aim to perform controlled fake new synthesis by altering certain entities, relations, and events, while keeping the rest of the story largely intact. We observe that, in general, the entity nodes with the strongest degree of connection are the centerpiece of a news <team,Manufacture.Artifact_Artifact.Artifact_Manufacturer, Zambia Fiji > <Zambia Fiji ,Manufacture.Artifa ct_Manufacturer, men> ... < bicycle > <hospital> ... article, while the entity nodes with the smallest degree of connection are less salient. Thus, we randomly select entity nodes occurring at mid range frequency to manipulate. We vary the type of KG manipulation, as follows: (1) Entity swapping we swap the original entity with an alternative entity that belongs to the same entity type. (2) Addition of a new relation or event we take an existing entity, randomly select a relation or event argument role that connects to this entity type, and append a new entity at the other end of the relation or event. (3) Subgraph replacement we select a subgraph of the news article that branches off the randomly selected entity nodes above, and replace it with a subgraph from another news article. Although we also considered the removal of node and edges, we found it intuitively too challenging to detect because lack of information can exist at various points across the article in reality but the silver standard annotation from selective removal would not cover enough of these for supervised training. Next, we generate a fake news article that aligns with this manipulated KG 0 by finetuning a BART-large language model (Lewis et al., 2020) on our training set. To better enforce that manipulated entities actually appear in the generated article, we use a copy mechanism which re-purposes entities from the input KG when generating the output article (Post and Vilar, 2018). After training, we manipulate KGs as described above and feed the manipulated KGs into our model to generate synthetic data (see the example in Figure 3). The manipulated knowledge elements serve as silver-standard annotations for the generated fake news articles. Tan et al. (2020) observe captions to be very significant in detecting fake articles, with performance dropping from 85.6% (when trained on articles generated using GROVER-Mega (Zellers et al., 2019)) to 56.9% when captions are excluded. Hence, we aim to further manipulate existing captions by generating subtle variations in the relations between entities. We leverage Abstract Meaning Representation (AMR) (Banarescu et al., 2013) graphs extracted from these captions since they capture rich fine-grained sentence-level semantic relations expressing who does what to whom . AMR semantic representation includes PropBank (Palmer et al., 2005) frames, non-core semantic roles, coreference, entity typing and linking, modality, and negation. True Caption : In Afghanistan, the Taliban released to the media this picture, which it said shows the suicide bombers who attacked the army base in Mazar-i-Sharif, April 21, 2017 Fake caption: On 21 April 2017 the Taliban released this picture to the army in Afghanistan which they said was a suicide bomber hiding at a media base in the city of Mazar-i-Sharif Figure 4: Example of AMR-to-text fake caption generation. To obtain the AMR graphs, we use the stack-transformer based AMR parser from Astudillo et al. (2020) and train it on AMR 3.0 3 . Given the AMR graph, we vary the manipulation as follows: (1) Role switching we randomly select two entity mentions that are present in different argument subgraphs of the AMR root node and interchange their positions in the AMR graph. (2) Predicate negation we randomly pick predicates in the AMR graph corresponding to event triggers and other verbs, and replace them with their antonyms, which we obtain from WordNet (Fellbaum, 1998). This manipulation also includes reverting nodes with negative polarity, thereby negating the sentence. 3 https://catalog.ldc.upenn.edu/LDC2020T02 After manipulating the AMR graphs, we convert them into text using the pretrained models 4 provided by Ribeiro et al. (2020). Specifically, we use a BART-large model that was fine-tuned to generate the sentence from its corresponding linearized AMR graph. We use topp topk sampling (Holtz-man et al., 2019), with k = 10 and p = 0 . 95 , to promote diversity in the generated text. Figure 4 shows an example of generated fake caption. We run experiments on two datasets: (1) The NYTimes-NeuralNews , an established benchmark for multi-media fake news detection with pristine news articles collected by Biten et al. (2019) and fake news generated by Grover in Tan et al. (2020). Following Tan et al. (2020), we use a subset of 32k real news articles from New York Times and 32k Grover-generated (Zellers et al., 2019) fake articles. (2) Our new VOA-KG2txt dataset, which consists of 15k real news article scraped from Voice of America and 15k machine-generated fake news articles using the KG-to-text approach in Section 4. We compare against two recent baselines: (1) (Tan et al., 2020) is most similar to InfoSurgeon as it performs multi-media fake news detection, but does not use KGs, perform fine-grained prediction, or leverage KG-driven data synthesis; and (2) (Zellers et al., 2019) which uses an adversarial discriminator to detect fake news articles based on the article text while disregarding the information from images and captions. Note that in the NYTimes experiment, a Grover-medium discriminator is used for the Zellers et al. (2019) baseline since fake news in the dataset is created using a Grover-mega generator and model leakage would be unfair. In the VOA experiment, the Grover-mega discriminator is used because fake news in the dataset is generated by a separate model, BART (Lewis et al., 2020). Additional implementation details can be found in the appendix. In Table 1, we report our accuracy at distinguishing real news articles from those generated by Grover in the NYTimes-NeuralNews dataset. We observe a large gain in performance (16.9%) over Tan et al. (2020). We believe there are several reasons for 4 https://github.com/UKPLab/plms-graph2text this gain. The main reason is due to the use of multimedia structured reasoning in our approach. (Tan et al., 2020) trains on articles and images and relies on the model itself to learn which statements in text to focus on for inference. In contrast, our approach explicitly extracts relations between entities (e.g. X LocatedNear Y) and in events (e.g. X-Attacker, Attack, Y-Target). This structure captured by the KG allows the model to easily zero-in on the semantics of assertions made in the text. By doing so, the model can more easily discover self-contradictions within articles (as well as between articles and captions). Moreover, our approach integrates external knowledge from Wikipedia into our knowledge graph, which enables our model to detect factual statements in generated articles which conflict with background knowledge. For example, if a generated article states that a country shares borders with another but it actually does not, we can detect the article's inconsistency with background knowledge. Table 1 also presents the results on the VOA-KG2txt dataset we assembled. We observe that our model continues to outperform Tan et al. (2020) on this dataset. Importantly, the synthetic data is created by our novel KG-to-text fake news synthesis approach (Section 4). This dataset poses unique challenges to our approach, as much of the knowledge graph (from real news articles) is preserved in the input to the generator. This means many claims made within the article are actually true (in contrast to NYTimes-NeuralNews , where the generator is not conditioned on specific claims). One novel aspect of our approach for fake news detection is we manipulate knowledge graphs to generate training data for our detector. While this enables us to generate more realistic training data, it also allows us to know precisely what elements of the generated knowledge graphs are manipulated. This enables us to make fine-grained, knowledge element level predictions to better understand how a given article is faked. Thus, we also evaluate our detector's performance at predicting real vs. fake at the knowledge element level. These annotations are only available on the VOA-KG2txt dataset we synthesize and not on NYTimes-NeuralNews . We present our results in Table 2. We see that our approach achieves 31% -37% accuracy at this task, significantly outperforming the random baseline. We note that this is an extremely challenging task, as we manipulate KGs subject to constraints which make their manipulations difficult to detect (Section 4). Determining which elements are misleading requires higher-level reasoning, both across modalities and with background knowledge. We next test the importance of each component in the detector. Specifically, we present results showing performance when the model is used with only the knowledge graph, semantic features (from the text, image, and captions), and primitive indicator values. As expected, we observe the best performance when all components are used, as this provides the most information to the model, as well as more opportunities for detecting inconsistencies. Semantic features constitute the most powerful component for the detector, but KG offers complementary information based on fine-grained knowledge elements, together making InfoSurgeon more robust and effective. In Table 4, we show an example document where InfoSurgeon is able to correctly predict real vs. fake, but the baseline (Tan et al., 2020) is not. The image and caption show Fort McHenry, while the article discusses the Fort's role in the Battle of 1814. The article mentions how the World Trade Center was destroyed in the battle. As there is no obvious cross-media inconsistency, Tan et al. (2020) predicts the document as real. In contrast, InfoSurgeon leverages background knowledge about the date of construction and destruction of the World Trade Center to determine the document is fake and predicts the knowledge element which is falsified, including the falsely generated entity twin towers which does not appear in the image nor caption. Our appendix contains additional results, including surgery where manipulated KEs are suppressed and a new article is then generated. In order to assess the quality of the synthesized text from our KG-to-text generator, we conduct a Turing Test by 16 human subjects who read news on a daily basis and are not authors of this paper. We randomly select a subset of 100 documents from the test set, half real and half fake, and present them to the human judges. Each human judge assesses all of these documents, without knowing the distribution of real and fake news. The average overall detection accuracy achieved by human judges is 61.6%, with 81.2% accuracy on real documents and only 41.9% accuracy on fake documents. A third of the fake news documents were predicted incorrectly by over half of the human subjects. This indicates that our automatically generated fake documents are also very hard for humans to detect. The most common clues humans used to detect fake news include linguistic style, topic coherence, specific event details and novel entities. Fake News Detection. Traditional approaches to fake news detection are largely based on fact-checking, text-style, or context from a single modality (Ciampaglia et al., 2015; Shi and Weninger, 2016; Pan et al., 2018; Angeli et al., 2015). Other approaches include detecting previously fact-checked claims (Shaar et al., 2020), retrieving sentences that explain fact-checking (Nadeem et al., 2019; Atanasova et al., 2020), and leveraging context and discourse information (Nakov et al., 2019). While style-based (Perez-Rosas et al., 2018; Karimi et al., 2018; De Sarkar et al., 2018) approaches have been effective in the past, they fall short against stylistically consistent, machine generated text (Schuster et al., 2020). However, Zellers et al. (2019) demonstrate that a text generator, such as Grover, can serve as a good detector against its own generations, picking up data artifacts such as exposure bias and sampling variance. Compared to Zellers et al. (2019), our fake news detection approach doesn't rely on access to the generator and is more robust against unseen generators. Recent approaches focus on using the multimedia information in news articles, as opposed to using only a single modality such as text (Baly et al., 2018; Ma et al., 2018; Hanselowski et al., 2018; Karimi and Tang, 2019) or images (Huh et al., 2018; Wang et al., 2019). Tan et al. (2020); Wang et al. (2018) extract multi-media features across the article body, images, and captions to detect inconsistencies. In comparison, we contribute a more comprehensive approach to fake news detection, by unifying source bias, semantic features, knowledge elements, cross-document cross-media consistency checking, and background knowledge reasoning, each of which offers complementary information, while previous attempts focus on only one or a few of these aspects (see Table 5). Fake News Generation. Zellers et al. (2019) finetune GPT-2 (Radford et al., 2019) on a large-scale news corpus to generate propaganda that can fool humans well. Biten et al. (2019) introduce an approach to generate image captions based on contextual information derived from news articles. In contrast, we leverage graph-to-text based approaches such as KG-to-text (Ribeiro et al., 2020; Chen et al., 2020) and AMR-to-text (Song et al., 2018; Ribeiro et al., 2020) to get more direct control in manipulation. We modify the knowledge elements in the structured input to produce more subtle variations in the generated text. Existing Benchmarks. The FEVER (Thorne et al., 2018) dataset seeks to retrieve supporting evidence for single-sentence claims and classify the claims as Supported, Refuted or NotEnoughInfo. PolitiFact 5 is a website that manually assigns fact-check label to claims, along with the background information. Zlatkova et al. (2019) propose a dataset for fact-checking claims about images. TabFact (Chen et al., 2019) presents semi-structural tables for fact verification. The SemEval-2020 shared task (Da San Martino et al., 2020) centers on the detection of propaganda techniques in news articles, which is more linguistically oriented. We create a new benchmark which will open up a new research direction towards explainable misinformation detection at the knowledge element level. We have demonstrated a novel method for multimedia misinformation detection that can achieve 92%-95% detection accuracy using cross-media information consistency checking and adversarial fake information generation by knowledge graph manipulation. Our framework can be used to ingest and assess news articles, while providing fine-grained knowledge element-level explanations. As future work, we plan to extend the problem such that any combination of body text, image, video, audio and caption can be fake. We will also incorporate consistency reasoning across multiple documents and from commonsense knowledge, and extend our approach to open-domain documents from multiple sources, languages and cultures. In the long term, we aim to collect more human-generated data with different types of intent that cause different levels of acceptance by readers, study more types of human manipulations to design additional criteria (e.g., entity novelty, newsworthiness, etc.), jointly detect misinformation and intent, correct detected misinformation, and generate authentic narratives. Our goal in developing fine-grained information consistency checking techniques is to advance the state-of-the-art and enhance the field's ability to detect fake news on the knowledge-element level. A general approach to ensure proper, rather than malicious, application of dual-use technology should incorporate ethical considerations as the first-order principles in every step of the system design, as well as maintain a high degree of transparency and interpretability of data, algorithms, models, and functionality throughout the system. In this paper, we focus on creating an interpretable approach so that users of the system can understand which parts of the article have been falsified. We intend to make our misinformation detector software available as open source and share docker containers for public verification and auditing so it can be used to combat fake news. But it's also important to note that, in order to avoid anyone using our frameworks to deliberately generate and spread misinformation, we will not share our misinformation generators. We acknowledge the pros and cons of releasing methodological details on the generator. Details on the generator raise awareness of the threat landscape and what is potentially being developed by malicious agents, which in turn help advance more robust countermeasures against adversarial attacks on fake news detectors. In addition, it reinforces another important principle scientific reproducibility. The flip side is that unethical parties may apply the new generator approach in their misconducts. To achieve a balance between such opposed considerations, we leave out ideas on how to improve the generator. We will also omit small details that make the generator successful without masking out the backbone to the scientific community. The proper composition of news content depends ultimately, in part, on regulations and standards that provide a legal framework and professional editorial review practice safeguarding against misinformation with deceitful intents. Whether InfoSurgeon is beneficial depends on who uses it. Here are some example scenarios where InfoSurgeon should and should not be used:  Should-Do : Anyone who wants to stay informed uses InfoSurgeon as an assistant to understand news events. Should-Do : Journalists use InfoSurgeon to verify facts and select authentic information to generate news summaries, timelines, and perspectives. Should-Do : Analysts use InfoSurgeon to monitor disaster and assist situation understanding, emergency response and resource allocation. Should-Not-Do : Anyone using InfoSurgeon to create and spread misinformation. Should-Not-Do : The detection results of InfoSurgeon should not be considered as definite determination about a news article being real or fake. It is intended only as an advisory and appropriate verification processes should not be dispensed. Finally, the types of misinformation we have detected are limited to the general news domain, and hence, they are not applicable to other domains. The performance of our system components as reported in the experiment section is based on the specific benchmark datasets, which could be affected by such data biases. Therefore, questions concerning generalizability and fairness should be carefully considered in future work. This research is based upon work supported by U.S. DARPA SemaFor Program No. HR001120C0123 and DARPA AIDA Program No. FA8750-18-2-0014. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of DARPA, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copyright annotation therein.
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