Original: Cross-document Misinformation Detection based on Event Graph Reasoning
Revised: Cross-document Misinformation Detection based on Event Graph Reasoning

Original: Abstract
Revised: Abstract

Original: For emerging events, human readers are often exposed to both real news and fake news.
Revised: For emerging events, human readers are often exposed to both real news and fake news.

Original: Multiple news articles may contain complementary or contradictory information that readers can leverage to help detect fake news.
Revised: Multiple news articles may contain complementary or contradictory information that readers can leverage to help detect fake news.

Original: Inspired by this process, we propose a novel task of cross-document misinformation detection .
Revised: Inspired by this process, we propose a novel task of cross-document misinformation detection .

Original: Given a cluster of topically related news documents, we aim to detect misinformation at both document level and a more ﬁnegrained level, event level .
Revised: Given a cluster of topically related news documents, we aim to detect misinformation at both document level and a more ﬁnegrained level, event level .

Original: Due to the lack of data, we generate fake news by manipulating real news, and construct 3 new datasets with 422 , 276 , and 1 , 413 clusters of topically related documents, respectively.
Revised: Due to the lack of data, we generate fake news by manipulating real news, and construct 3 new datasets with 422 , 276 , and 1 , 413 clusters of topically related documents, respectively.

Original: We further propose a graph-based detector that constructs a cross-document knowledge graph using cross-document event coreference resolution and employs a heterogeneous graph neural network to conduct detection at two levels.
Revised: We further propose a graph-based detector that constructs a cross-document knowledge graph using cross-document event coreference resolution and employs a heterogeneous graph neural network to conduct detection at two levels.

Original: We then feed the event-level detection results into the document-level detector.
Revised: We then feed the event-level detection results into the document-level detector.

Original: Experimental results show that our proposed method signiﬁcantly outperforms existing methods by up to 7 F1 points on this new task.
Revised: Experimental results show that our proposed method signiﬁcantly outperforms existing methods by up to 7 F1 points on this new task.

Original:  Introduction
Revised:  Introduction

Original: The dissemination of fake news has become an important social issue.
Revised: The dissemination of fake news has become an important social issue.

Original: For emergent complex events, human readers are usually exposed to multiple news documents, where some are real and others are fake.
Revised: For emergent complex events, human readers are usually exposed to multiple news documents, where some are real and others are fake.

Original: News documents from different sources naturally form a cluster of topically related documents.
Revised: News documents from different sources naturally form a cluster of topically related documents.

Original: We notice that articles about the same topic may contain conﬂicting or complementary information, which can beneﬁt the task of misinformation detection.
Revised: We notice that articles about the same topic may contain conﬂicting or complementary information, which can beneﬁt the task of misinformation detection.

Original: An example is shown in Figure 1.
Revised: An example is shown in Figure 1.

Original: As shown in the knowledge graph, the death of Rosanne Boyland in 2021 US Capitol attack is a shared event across all four documents.
Revised: As shown in the knowledge graph, the death of Rosanne Boyland in 2021 US Capitol attack is a shared event across all four documents.

Original: Each document is internally consistent, which makes it difﬁcult to identify misinformation when judging each news separately.
Revised: Each document is internally consistent, making it difﬁcult to identify misinformation when judging each news separately.

Original: However, the three real news documents complement each other’s statements regarding the death of Boyland, while the fake news document contradicts the other stories.
Revised: However, the three real news documents complement each other’s statements regarding the death of Boyland, while the fake news document contradicts the other stories.

Original: Such crossdocument connections can be leveraged to help detect misinformation.
Revised: Such cross-document connections can be leveraged to help detect misinformation.

Original: Most  existing work in fake news detection is limited to judging each document in isolation.
Revised: Most of the existing work on fake news detection is limited to judging each document in isolation.

Original: In contrast, we propose a novel task of cross-document misinformation detection that aims to detect fake information from a cluster of topically related news documents.
Revised: In contrast, we propose a novel task of crossdocument misinformation detection that aims to detect fake information from a cluster of topically related news documents.

Original: We conduct the task at both  document level and event level.
Revised: We perform the task at both the document level and event level.

Original: Each event describes a speciﬁc type of real-world event mentioned in the text (e.g., the death of Boyland in Figure 1), and usually involves certain participants to represent different aspects of the event (e.g., thedeath cause  and the victim of the death event).
Revised: Each event describes a speciﬁc type of real-world event mentioned in the text (e.g., the death of Boyland in Figure 1), and usually involves certain participants to represent different aspects of the event (e.g., the cause of death and the victim of the death event).

Original: Document-level detection aims to detect fake news documents.
Revised: Document-level detection aims to detect fake news documents.

Original: Eventlevel detection is a more ﬁne-grained task that aims to detect fake events, thereby pinpointing speciﬁc fake information in news documents.
Revised: Event-level detection is a more ﬁne-grained task that aims to detect fake events, thereby pinpointing speciﬁc fake information in news documents.

Original: Existing work on ﬁne-grained misinformation detection detectsfake knowledge triplets  (Fung et al., 2021).
Revised: Existing work on ﬁne-grained misinformation detection detects triplets of false knowledge (Fung et al., 2021).

Original: However, we focus on identifying false events instead of relations or entities, because events are more important for storytelling, and easier to compare across multiple documents through cross-document coreference resolution.
Revised: However, we focus on identifying false events instead of relations or entities, because events are more important for storytelling and easier to compare across multiple documents through cross-document coreference resolution.

Original: To the best of our knowledge, there are no fake news detection datasets with clusters of topically related documents.
Revised: To the best of our knowledge, there are no fake news detection datasets with clusters of topically related documents.

Original: Therefore, we construct 3 new benchmark datasets based on existing real news corpus with such clusters.
Revised: Therefore, we construct 3 new benchmark datasets based on existing real news corpus with such clusters.

Original: Following Fung et al.
Revised: Following Fung et al.

Original: (2021), we train a generator that generates a document from a knowledge graph (KG), and feed manipulated KGs into the generator to generate fake news documents.
Revised: (2021), we train a generator that generates a document from a knowledge graph (KG), and feed manipulated KGs into the generator to generate fake news documents.

Original: By tracking the manipulation operations, we also obtain supervision for 
Revised: Tracking the manipulation operations, we also obtain supervision for event- level detection.

Original: We further propose a detection approach as shown in Figure 2.
Revised: We further propose a detection approach as shown in Figure 2.

Original: Given a cluster of documents, we ﬁrst use an information extraction (IE) system (Lin et al., 2020) to construct a within-document
Revised: Given a cluster of documents, we ﬁrst use an information extraction (IE) system (Lin et al., 2020) to construct a within-document

Original: KG for each document.
Revised: KG for each document.

Original: Then, we connect the within-document KGs to form a cross-document
Revised: Then, we connect the within-document KGs to form a cross-document

Original: KG using cross-document event coreference resolution (Lai et al., 2021; Wen et al., 2021).
Revised: KG using cross-document event coreference resolution (Lai et al., 2021; Wen et al., 2021).

Original: Eventually, we use a heterogeneous graph neural network (GNN) to encode the cross-document KG and conduct detection at two levels.
Revised: Eventually, we use a heterogeneous graph neural network (GNN) to encode the cross-document KG and conduct detection at two levels.

Original: Our contributions are summarized as follows:
Revised: Our contributions are summarized as follows:

Original: We propose the novel task of cross-document misinformation detection, and conduct the task at two levels, document level and the more ﬁne-grained event level.
Revised: We propose the novel task of cross-document misinformation detection, and conduct the task at two levels, document level and the more ﬁne-grained event level.

Original: We construct 3 new datasets for our proposed task based on existing document clusters categorized by topics.
Revised: We construct 3 new datasets for our proposed task based on existing document clusters categorized by topics.

Original: We propose a detector that leverages crossdocument information and improve documentlevel detection by utilizing features produced by the event-level detector.
Revised: We propose a detector that leverages crossdocument information and improve documentlevel detection by utilizing features produced by the event-level detector.

Original: Experiments on three datasets demonstrate that our method signiﬁcantly outperforms existing methods.
Revised: Experiments on three datasets demonstrate that our method signiﬁcantly outperforms existing methods.

Original:  Related Work
Revised:  Related Work

Original: Fake News Detection: Early work for fake news detection uses hand-crafted features to conduct document classiﬁcation (Rubin et al., 2016; Wang, 2017; Rashkin et al., 2017; Pérez-Rosas et al., 2018;
Revised: Fake News Detection: Early work on fake news detection uses hand-crafted features to perform document classiﬁcation (Rubin et al., 2016; Wang, 2017; Rashkin et al., 2017; Pérez-Rosas et al., 2018;

Original: Sarkar et al., 2018; Atanasova et al., 2019).
Revised: Sarkar et al., 2018; Atanasova et al., 2019).

Original: Recent work uses neural networks such as recurrent neural network (Karimi et al., 2018; Nasir et al., 2021) and Transformer (Zellers et al., 2019) to encode the news document.
Revised: Recent work uses neural networks such as recurrent neural networks (Karimi et al., 2018; Nasir et al., 2021) and Transformer (Zellers et al., 2019) to encode the news document.

Original: To model the internal structure of a news document, Karimi and Tang (2019) model the inter-sentence dependency tree, Vaibhav et al.
Revised: To model the internal structure of a news document, Karimi and Tang (2019) model the inter-sentence dependency tree, Vaibhav et al.

Original: (2019) and Hu et al.
Revised: ; Hu et al.

Original: (2021) model the interactions between sentences, and Pan et al.
Revised: (2021) model the interactions between sentences; and Pan et al.

Original: (2018) and Fung et al.
Revised: (2018) and Fung et al.

Original: (2021) model the knowledge graph extracted by IE systems.
Revised: (2021) model the knowledge graph extracted by IE systems.

Original: Similar to our work, Hu et al.
Revised: Similar to our work, Hu et al.

Original: (2021) compare each news with external knowledge base (KB) to check for inconsistencies.
Revised: (2021) compare each news with external knowledge base (KB) to check for inconsistencies.

Original: However, the correlation between news and KB is not as close as the correlation between related news documents due to the incompleteness of these KBs.
Revised: However, the correlation between news and KB is not as close as the correlation between related news documents due to the incompleteness of these KBs.

Original: Other work utilizes additional information such as user engagements and behaviors on social media (Shu et al., 2019; Nguyen et al., 2020) and multimodal features (Khattar et al., 2019; Tan et al., 2020; Fung et al., 2021).
Revised: Other work utilizes additional information such as user engagements and behaviors on social media (Shu et al., 2019; Nguyen et al., 2020) and multi-modal features (Khattar et al., 2019; Tan et al., 2020; Fung et al., 2021).

Original: However, to the best of our knowledge, no published work has considered using cross-document inference for misinformation detection.
Revised: However, to the best of our knowledge, no published work has considered using cross-document inference for misinformation detection.

Original: In addition to document-level detection, the task of ﬁne-grained detection is also important but rarely explored.
Revised: In addition to document-level detection, the task of ﬁne-grained detection is also important but rarely explored.

Original: The most relevant work detects fake knowledge triplets extracted from each individual news article (Fung et al., 2021).
Revised: The most relevant work detects fake knowledge triplets extracted from each individual news article (Fung et al., 2021).

Original: Another related task is fact veriﬁcation which aims to verify a statement based on retrieved evidence.
Revised: Another related task is fact veriﬁcation which aims to verify a statement based on retrieved evidence.

Original: Fact veriﬁcation has been explored in multiple domains such as general domain (Thorne et al., 2018), climate change (Diggelmann et al., 2020) and COVID-19 (Wadden et al., 2020; Saakyan et al., 2021).
Revised: Fact veriﬁcation has been explored in multiple domains such as general domain (Thorne et al., 2018), climate change (Diggelmann et al., 2020) and COVID-19 (Wadden et al., 2020; Saakyan et al., 2021).

Original: However, fact veriﬁcation focuses on short single-sentence statements, and cannot model the complicated internal structure of a news document.
Revised: However, fact veriﬁcation focuses on short single-sentence statements and cannot model the complicated internal structure of a news document.

Original: Fake News Datasets: The main difﬁculty in constructing a fake news dataset is to obtain annotations.
Revised: Fake News Datasets: The main difﬁculty in constructing a fake news dataset is to obtain annotations.

Original: Rashkin et al.
Revised: Rashkin et al.

Original: (2017) and Rubin et al.
Revised: (2017) and Rubin et al.

Original: (2016) obtain labels from the source information, and consider news from reliable sources as real news, and unreliable sources as fake news.
Revised: (2016) obtain labels from the source information and consider news from reliable sources as real news, and unreliable sources as fake news.

Original: A potential issue is that the detector may only learn to distinguish the style of different news sources, rather than the authenticity of the content.
Revised: A potential issue is that the detector may only learn to distinguish the style of different news sources, rather than the authenticity of the content.

Original: Shu et al.
Revised: Shu et al.

Original: (2020) collect annotations from fact-checking websites, and Pérez-Rosas et al.
Revised: (2020) collect annotations from fact-checking websites, and Pérez-Rosas et al.

Original: (2018) collect annotations via crowd-sourcing.
Revised: (2018) collect annotations via crowd-sourcing.

Original: These approaches produce datasets of higher quality, but require extensive manual efforts.
Revised: These approaches produce datasets of higher quality, but require extensive manual efforts.

Original: With the development of powerful generative models capable of mimicking humanwritten news (Zellers et al., 2019), recent work has constructed datasets by using generative models to generate fake news (Tan et al., 2020; Fung et al., 2021).
Revised: With the development of powerful generative models capable of mimicking humanwritten news (Zellers et al., 2019), recent work has constructed datasets by using generative models to generate fake news (Tan et al., 2020; Fung et al., 2021).

Original: Fung et al.
Revised: Fung et al.

Original: (2021) further generate fake news from manipulated KG, which we follow to construct our dataset.
Revised: (2021) further generate fake news from manipulated KG, which we follow to construct our dataset.

Original:  Task Formulation
Revised:  Task Formulation

Original: Given a cluster of documents about the same story, the task of cross-document misinformation detection aims to detect the fake information included in the cluster.
Revised: Given a cluster of documents about the same story, the task of cross-document misinformation detection aims to detect the fake information included in the cluster.

Original: Formally, let S = { d 1 , · · · , d N } be the document cluster, and N = | S | be the size of the cluster.
Revised: Formally, let S = { d 1 , · · · , d N } be the document cluster, and N = | S | be the size of the cluster.

Original: Some documents in S are real, while others are fake.
Revised: Some documents in S are real, while others are fake.

Original: From each document d ∈ S , we extract events E ( d ) =
Revised: From each document d ∈ S , we extract events E ( d ) =

Original: { e 1 , · · · , e m } , where m =
Revised: { e 1 , · · · , e m } , where m =

Original: | E ( d ) | is the number of events in document d .
Revised: | E ( d ) | is the number of events in document d .

Original: In an extracted event set E ( d ) , some events are real and others are fake.
Revised: In an extracted event set E ( d ) , some events are real and others are fake.

Original: We conduct the task of misinformation detection at two levels, document level and event level.
Revised: We conduct the task of misinformation detection at two levels, document level and event level.

Original: Document-level detection aims to predict whether each document d ∈ S is real or fake.
Revised: Document-level detection aims to predict whether each document d ∈ S is real or fake.

Original: Event-level detection is a more ﬁne-grained task which aims to predict whether each event e ∈ E ( d ) , d ∈ S is real or fake.
Revised: Event-level detection is a more ﬁne-grained task that aims to predict whether each event e ∈ E ( d ) , d ∈ S is real or fake.

Original: In the example in Figure 1, the die event in the fake news is fake, since it falsely describes
Revised: In the example in Figure 1, the die event in the fake news is fake, since it falsely describes

Original: Boyland being killed by the police but she actually died of drug overdose.
Revised: Boyland being killed by the police, but she actually died of drug overdose.

Original:  Approach
Revised:  Approach

Original: An overview of our approach is shown in Figure 2.
Revised: An overview of our approach is shown in Figure 2.

Original: Given a cluster of documents, we ﬁrst construct a within-document KG for each document using an IE system (Lin et al., 2020), and then connect the within-document KGs into a cross-document
Revised: Given a cluster of documents, we ﬁrst construct a within-document KG for each document using an IE system (Lin et al., 2020), and then connect the within-document KGs into a cross-document

Original: KG using cross-document event coreference resolution.
Revised: KG using cross-document event coreference resolution.

Original: Based on the cross-document KG, we use a hetereogeneous GNN (Schlichtkrull et al., 2018;
Revised: Based on the cross-document KG, we use a hetereogeneous GNN (Schlichtkrull et al., 2018;

Original: Hu et al., 2019) to conduct detection.
Revised: Hu et al., 2019) to perform detection.

Original: We further incorporate the results of event-level detection to help the document-level detector.
Revised: We further incorporate the results of event-level detection to help the document-level detector.

Original: 4. Knowledge Graph Construction
Revised: 4. Knowledge Graph Construction

Original: Within-document KG: We ﬁrst construct a within-document IE-based knowledge graph for each document.
Revised: Within-document KG: We ﬁrst construct a within-document IE-based knowledge graph for each document.

Original: We leverage OneIE (Lin et al., 2020), a joint IE system, to extract the entities, relations and events contained in a given document.
Revised: We leverage OneIE (Lin et al., 2020), a joint IE system, to extract the entities, relations, and events contained in a given document.

Original: Then, we conduct entity linking (Pan et al., 2017) and entity coreference resolution (Lee et al., 2017) to merge multiple mentions of the same entities together.
Revised: Then, we conduct entity linking (Pan et al., 2017) and entity coreference resolution (Lee et al., 2017) to merge multiple mentions of the same entities together.

Original: Eventually, we obtain a withindocument KG where entities and events are nodes, relations are edges between entities, and arguments are edges between events and entities.
Revised: Eventually, we obtain a withindocument KG where entities and events are nodes, relations are edges between entities, and arguments are edges between events and entities.

Original: Cross-document
Revised: Cross-document

Original: KG: We leverage crossdocument event coreference resolution to connect the within-document KGs into a cross-document
Revised: KG: We leverage crossdocument event coreference resolution to connect the within-document KGs into a cross-document

Original: KG as illustrated in Figure 2.
Revised: KG as illustrated in Figure 2.

Original: We employ a crossdocument event coreference resolution system (Lai et al., 2021; Wen et al., 2021) to identify clusters of events from multiple documents that refer to the same real-world events.
Revised: We employ a crossdocument event coreference resolution system (Lai et al., 2021; Wen et al., 2021) to identify clusters of events from multiple documents that refer to the same real-world events.

Original: The system utilizes both textual contexts of the event mentions andthe symbolic features such as the event type information.
Revised: The system utilizes both textual contexts of the event mentions and symbolic features such as the event type information.

Original: An example of the detected event cluster is shown in Table 1, where the four events from four documents all refer to the same explosion attack on Venezuela President Nicolas Marduro.
Revised: An example of the detected event cluster is shown in Table 1, where the four events of four documents all refer to the same explosion attack on Venezuela’s President Nicolas Marduro.

Original: These four events contain complementary or contradictory details, which can be used for misinformation detection.
Revised: These four events contain complementary or contradictory details, which can be used for misinformation detection.

Original: For each event cluster, we add a node to represent the overall information of the real-world complex event corresponding to the cluster.
Revised: For each event cluster, we add a node to represent the overall information of the real-world complex event corresponding to the cluster.

Original: Then, an edge is added between each event node and corresponding cluster node to allow reasoning among cross-document coreferential events.
Revised: Then, an edge is added between each event node and corresponding cluster node to allow reasoning among cross-document coreferential events.

Original: To indicate which document each entity or event belongs to and capture the global information of each document, we further introduce a document node and connect it to the associated entity and event nodes for each document.
Revised: To indicate which document each entity or event belongs to and capture the global information of each document, we further introduce a document node and connect it to the associated entity and event nodes for each document.

Original: The resulting KG contains 4 types of nodes (i.e. entity nodes, event nodes, document nodes, and event cluster nodes) and 5 types of edges (i.e. relation edges, event argument edges, document-toentity edges, document-to-event edges, and edges connecting event nodes to event cluster nodes).Since all edges are directional, we add an inverse
Revised: The resulting KG contains 4 types of nodes (i.e. entity nodes, event nodes, document nodes, and event cluster nodes) and 5 types of edges (i.e. relation edges, event argument edges, document-toentity edges, document-to-event edges, and edges connecting event nodes to event cluster nodes).

Original: Real · · · Venezuela’ s president, Nicolás Maduro, has survived an apparent assassination attempt after what of- ﬁcials described as drones armed with explosives arg detonated trig overhead during a speech he was mak- ing at a military event.
Revised: Real · · · Venezuela’ s president, Nicolás Maduro, has survived an apparent assassination attempt after what of- ﬁcials described as drones armed with explosives arg detonated trig overhead during a speech he was mak- ing at a military event.

Original: Real · · · The BBC quotes anonymous ﬁreﬁghters at the scene who say “the incident was actually a gas tank explosion trig inside an apartment arg2 , but did not pro- vide further details.”
Revised: Real · · · The BBC quotes anonymous ﬁreﬁghters at the scene who say “the incident was actually a gas tank explosion trig inside an apartment arg2 , but did not pro- vide further details.”

Original: Fake · · · Maduro was not targeted by the drones, the prime minister said, but state security services reported that the drones were meant for him.
Revised: Fake · · · Maduro was not targeted by the drones, the prime minister said, but state security services reported that the drones were meant for him.

Original: “The explosion trig was caused by two machine guns arg1 ,” Maduro said, adding that there were no injuries.
Revised: “The explosion trig was caused by two machine guns arg1 ,” Maduro said, adding that there were no injuries.

Original: Fake · · · Two drones armed with explosives detonated trig near PuntoDeCorte arg2 , where the Venezuelan Foreign Minister, Jorge Rodríguez, was performing, and near the stage where he was giving a speech.
Revised: Fake · · · Two drones armed with explosives detonated trig near PuntoDeCorte arg2 , where the Venezuelan Foreign Minister, Jorge Rodríguez, was performing, and near the stage where he was giving a speech.

Original: · · · edge for each edge to propagate features along both directions, and the ﬁnal KG contains 10 edge types, accounting for the inverse of existing edge types.
Revised: Since all edges are directional, we add an inverse edge for each edge to propagate features along both directions, and the ﬁnal KG contains 10 edge types, accounting for the inverse of existing edge types.

Original: KG representation: We use BERT (Devlin et al., 2019) to initialize the node and edge embeddings in the KG.
Revised: KG representation: We use BERT (Devlin et al., 2019) to initialize the node and edge embeddings in the KG.

Original: For a document node, we use BERT to encode the entire document and take the embeddings of [CLS] tokens.
Revised: For a document node, we use BERT to encode the entire document and take the embeddings of [CLS] tokens.

Original: Similarly, for an entity node, we encode its canonical mention.
Revised: Similarly, for an entity node, we encode its canonical mention.

Original: For an event node, we encode the sentence where the event trigger occurs.
Revised: For an event node, we encode the sentence where the event trigger occurs.

Original: For an event cluster node, we take the average of the embeddings of all events in the cluster.
Revised: For an event cluster node, we take the average of the embeddings of all events in the cluster.

Original: For a relation edge or an event argument role edge, we encode the linearized representation of the relation tuple.
Revised: For a relation edge or an event argument role edge, we encode the linearized representation of the relation tuple.

Original: For example, the Leadership relation between “Nicolas Maduro” and “Venezuelan” is represented by “ Nicolas Maduro, Leadership, Venezuelan ” , and “guns” as the ExplosiveDevice argument of the
Revised: For example, the Leadership relation between “Nicolas Maduro” and “Venezuelan” is represented by “ Nicolas Maduro, Leadership, Venezuelan ” , and “guns” as the ExplosiveDevice argument of the

Original: DetonateExplode event is represented by “ DetonateExplode, ExplosiveDevice, guns ”.
Revised: DetonateExplode event is represented by “ DetonateExplode, ExplosiveDevice, guns ”.

Original: 4. Knowledge Graph Encoder
Revised: 4. Knowledge Graph Encoder

Original: Heterogeneous GNN: Given the heterogeneous nature of the cross-document KG, we adopt a hetereogeneous GNN to encode the KG.
Revised: Heterogeneous GNN: Given the heterogeneous nature of the cross-document KG, we adopt a het- erogeneous GNN to encode the KG.

Original: Formally, let G denotethe KG and V denote the nodes in G .
Revised: Formally, let G denote KG and V denote the nodes in G .

Original: We use R to denote the 10 types of edges as discussed in the previous section, and for each edge type r ∈ R , we use G r to denote the subgraph of G that only contains edges of type r .
Revised: We use R to denote the 10 types of edges as discussed in the previous section, and for each edge type r ∈ R , we use G r to denote the subgraph of G that only contains edges of type r .

Original: At the l -th layer, the inputs are output features produced by the previous layer denoted as h ( l − 1) i , i ∈ V .
Revised: At the l -th layer, the inputs are output features produced by the previous layer denoted as h ( l − 1) i , i ∈ V .

Original: For each edge type r ∈ R , we apply a separate GNN to encode G r and produce a set of features denoted as h ( l ) i,r .
Revised: For each edge type r ∈ R , we apply a separate GNN to encode G r and produce a set of features denoted as h ( l ) i,r .

Original: Then, we aggregate the outputs for all edge types into the ﬁnal output as follows:
Revised: Then, we aggregate the outputs for all edge types into the ﬁnal output as follows:

Original: [Equation]
Revised: [Equation]

Original: For document-to-entity edges, document-toevent edges, and edges connecting event nodes to event cluster nodes, we use standard graph attention network (GAT).
Revised: For document-to-entity edges, document-toevent edges, and edges connecting event nodes to event cluster nodes, we use standard graph attention network (GAT).

Original: For relation edges and event argument edges, we apply edge-aware GAT to leverage the edge features.
Revised: For relation edges and event argument edges, we apply edge-aware GAT to leverage the edge features.

Original: Here, the edge features refer to the BERT embeddings of text descriptions such as “ Nicolas Maduro, Leadership, Venezuelan ” or “ DetonateExplode, ExplosiveDevice, guns ” as described in Section 4.1.
Revised: Here, the edge features refer to the BERT embeddings of text descriptions such as “ Nicolas Maduro, Leadership, Venezuelan ” or “ DetonateExplode, ExplosiveDevice, guns ” as described in Section 4.1.

Original: The remainder of Section 4.2 presents details of GAT and edge-aware GAT, i.e., how to produce h ( l ) i,r based on h ( l − 1) i .
Revised: The remainder of Section 4.2 presents details of GAT and edge-aware GAT, i.e., how to produce h ( l ) i,r based on h ( l − 1) i .

Original: Graph attention network:
Revised: Graph attention network:

Original: For each given node,
Revised: For each given node,

Original: GAT aggregates the node features of its neighbors via attention mechanism (Velickovic et al., 2018).
Revised: GAT aggregates the node features of its neighbors via attention mechanism (Velickovic et al., 2018).

Original: For a given edge type r ∈ R , let N i,r denote the neighbors of node i in G r .
Revised: For a given edge type r ∈ R , let N i,r denote the neighbors of node i in G r .

Original: At the l -th layer, the attention weights α ij are calculated as follows:
Revised: At the l -th layer, the attention weights α ij are calculated as follows:

Original: [Equation]
Revised: [Equation]

Original: where a and W are trainable parameters, and (cid:107) denotes the feature concatenation.
Revised: where a and W are trainable parameters, and (cid:107) denotes the feature concatenation.

Original: The output features h ( l ) i,r for node i in G r are calculated as follows:
Revised: The output features h ( l ) i,r for node i in G r are calculated as follows:

Original: [Equation]
Revised: [Equation]

Original: Edge-aware graph attention network: Edgeaware GAT is an extension of GAT that considers edge features in addition to node features (Huang et al., 2020; Yasunaga et al., 2021).
Revised: Edge-aware graph attention network: Edgeaware GAT is an extension of GAT that considers edge features in addition to node features (Huang et al., 2020; Yasunaga et al., 2021).

Original: Let r ij denote the features of the edge between node i and j .
Revised: Let r ij denote the features of the edge between nodes i and j .

Original: For a given edge type r ∈ R , at the l -th layer, the attention weights α ij are computed as follows:
Revised: For a given edge type r ∈ R , at the l -th layer, the attention weights α ij are computed as follows:

Original: [Equation]
Revised: [Equation]

Original: where W r , W Q and W K are trainable parameters.
Revised: where W r , W Q and W K are trainable parameters.

Original: The output features h ( l ) i,r for node i in G r are computed as follows:
Revised: The output features h ( l ) i,r for node i in G r are computed as follows:

Original: [Equation]
Revised: [Equation]

Original: where W V is a learnable matrix.
Revised: where W V is a learnable matrix.

Original: 4. Misinformation Detector
Revised: 4. Misinformation Detector

Original: Using the previously described graph encoder, we are able to obtain representations of the document and event nodes.
Revised: Using the previously described graph encoder, we are able to obtain representations of the document and event nodes.

Original: We conduct document-level detection using the document node representations, and event-level detection using the event node representations.
Revised: We conduct document-level detection using the document node representations, and event-level detection using the event node representations.

Original: We separately train two detectors for these two levels of tasks.
Revised: We separately train two detectors for these two levels of tasks.

Original: However, these two tasks are not mutually independent.
Revised: However, these two tasks are not mutually independent.

Original: Intuitively, document-level detection can beneﬁt from the results of event-level detection, because the presence of a large number of false events indicates that the document is more likely to be fake.
Revised: Intuitively, document-level detection can beneﬁt from the results of event-level detection, because the presence of a large number of false events indicates that the document is more likely to be fake.

Original: Therefore, we feed the results produced by a well-trained event-level detector into each layer of the document-level detector.
Revised: Therefore, we feed the results produced by a well-trained event-level detector into each layer of the document-level detector.

Original: Let e i denote the representations of node i produced by the event-level detector.
Revised: Let e i denote the representations of node i produced by the event-level detector.

Original: At the l -th layer of the document-level detector, instead of using the output features of the previous layer h ( l − 1) i as input features, we use a linear projection of the concatenation of e i and h ( l − 1) i calculated as follows:
Revised: At the l -th layer of the document-level detector, instead of using the output features of the previous layer h ( l − 1) i as input features, we use a linear projection of the concatenation of e i and h ( l − 1) i calculated as follows:

Original: [Equation]
Revised: [Equation]

Original: where W ( l ) proj is a learnable matrix.
Revised: where W ( l ) proj is a learnable matrix.

Original:  Dataset Construction
Revised:  Dataset Construction

Original: Currently, there are no existing resources for crossdocument misinformation detection.
Revised: Currently, there are no existing resources for crossdocument misinformation detection.

Original: We propose to construct datasets based on real news datasets with clustering information.
Revised: We propose to construct datasets based on real news datasets with clustering information.

Original: For each cluster, we randomly sample 50% real news and replace them with manipulated fake news.
Revised: For each cluster, we randomly sample 50% real news and replace them with manipulated fake news.

Original: Figure 3 shows an overview of  fake news generation process, and more examples are presented in Appendix D.
Revised: Figure 3 shows an overview of the fake news generation process, and more examples are presented in Appendix D.

Original: Following Fung et al.
Revised: Following Fung et al.

Original: (2021), we train a KG-totext generator from the real news in our datasets, and generate fake news from manipulated KGs.
Revised: (2021), we train a KG-totext generator from the real news in our datasets, and generate fake news from manipulated KGs.

Original: The main differences between Fung et al.
Revised: The main differences between Fung et al.

Original: (2021)’s method and ours in terms of manipulating KG are: (1) we only conduct entity swapping, and do not adopt other types of manipulation including adding relations or events and subgraph replacement; (2) since we focus on events, we select entities to be replaced that are arguments of high-frequency events, instead of based on entity node degree; (3) we select entities from other documents in the same document cluster to replace the original entities, so that the entities before and after replacement are more similar.
Revised: (2021)’s method and ours in terms of manipulating KG are: (1) we only conduct entity swapping, and do not adopt other types of manipulation including adding relations or events and subgraph replacement; (2) since we focus on events, we select entities to be replaced that are arguments of high-frequency events, instead of based on entity node degree; (3) we select entities from other documents in the same document cluster to replace the original entities, so that the entities before and after replacement are more similar.

Original: We record the manipulation operations, and use a heuristic rule to obtain supervision for event-level detection as explained below.
Revised: We record the manipulation operations, and use a heuristic rule to obtain supervision for event-level detection as explained below.

Original: In a fake document, if an event involves manipulated entities as arguments, we consider this event as fake.
Revised: In a fake document, if an event involves manipulated entities as arguments, we consider this event as fake.

Original:  Experiments 6. Data
Revised:  Experiments 6. Data

Original: We construct three new benchmark datasets based on three datasets that naturally have clusters of topically related documents.
Revised: We constructed three new benchmark datasets based on three datasets that naturally have clusters of topically related documents.

Original: IED is a complex event corpus, where each complex event refers to a realworld story (e.g., Boston bombing) and is described by multiple documents (Li et al., 2021).
Revised: IED is a complex event corpus, where each complex event refers to a real-world story (e.g., Boston bombing) and is described by multiple documents (Li et al., 2021).

Original: Therefore, a complex event can be considered as a document cluster.
Revised: Therefore, a complex event can be considered as a document cluster.

Original: TL17 and Crisis are two timeline summarization datasets containing multiplenews timelines. Each timeline contains multiple documents describing an evolving long-term event such as In- # Cluster # Doc # Fake [Figure] per doc 
Revised: TL17 and Crisis are two timeline summarization datasets containing multiple # Cluster # Doc # Fake event per doc (%)

Original: [Figure]

Original: [Figure]

Original: IED
Revised: IED

Original: Train
Revised: Train

Original: [Table]
Revised: [Table]

Original: ﬂuenza H1N1 and Egypt Revolution (Tran et al., 2013, 2015), and thus can be regarded as a document cluster.
Revised: Dev [Table] news timelines. Each timeline contains multiple documents describing an evolving long-term event such as Inﬂuenza H1N1 and Egypt Revolution (Tran et al., 2013, 2015), and thus can be regarded as a document cluster.

Original: The detailed statistics of the original datasets are shown in Appendix A.
Revised: The detailed statistics of the original datasets are shown in Appendix A.

Original: However,the documents within the same cluster may not be closely related as the story described by a cluster can span up to three years.
Revised: However, documents within the same cluster may not be closely related as the story described by a cluster can span up to three years.

Original: To obtain smaller and more closely related clusters, we split each timeline into smaller clusters of approximately sizeof 10 based on publication dates 2 .
Revised: To obtain smaller and more closely related clusters, we split each timeline into smaller clusters of approximately size 10 based on publication dates 2 .

Original: Then, we employ the methods described in Section 5 to generate fake documents.
Revised: Then, we employ the methods described in Section 5 to generate fake documents.

Original: The statistics of the constructed datasets are in Table 2.
Revised: The statistics of the constructed datasets are in Table 2.

Original: 6. Experimental Settings
Revised: 6. Experimental Settings

Original: For our proposed method, we use a 4 -layer heterogeneous GAT and use bert-base-uncased to initialize the node and edge embeddings.
Revised: For our proposed method, we use a 4 -layered heterogeneous GAT and use bert-base-uncased to initialize the node and edge embeddings.

Original: For comparison, on the document-level detection task, we compare our method against two baselines:
Revised: For comparison, on the document-level detection task, we compare our method against two baselines:

Original: HDSF that models  inter-sentence dependency tree (Karimi and Tang, 2019), and GROVER (Zellers et al., 2019), a Transformer-based detector.
Revised: HDSF that models the inter-sentence dependency tree (Karimi and Tang, 2019), and GROVER (Zellers et al., 2019), a Transformer-based detector.

Original: On the event-level detection task, we compare our method against random guessing , logistic regression and BERT .
Revised: On the event-level detection task, we compare our method against random guessing , logistic regression and BERT .

Original: For logistic regression, we use hand-crafted features to represent the event including the event type, the number of arguments, and the size of the event cluster.
Revised: For logistic regression, we use hand-crafted features to represent the event including the event type, the number of arguments, and the size of the event cluster.

Original: The detailed settings are presented in
Revised: The detailed settings are presented in

Original: Appendix C.
Revised: Appendix C.

Original: For evaluation, we use F1 to evaluate documentlevel detection.
Revised: For evaluation, we use F1 to evaluate documentlevel detection.

Original: Considering the label imbalance of event-level detection, we use F1 and the area under the ROC curve (AUC) to evaluate event-level detection.
Revised: Considering the label imbalance of event-level detection, we use F1 and the area under the ROC curve (AUC) to evaluate event-level [Table] [Table]

Original:  For  F1 metric, we select the optimal threshold on the validation set.[Table] [Table]
Revised: detection. For the F1 metric, we select the optimal threshold on the validation set.

Original: 6. Document-level Detection Results
Revised: 6. Document-level Detection Results

Original: Table 3 shows the results of document-level detection.
Revised: Table 3 shows the results of document-level detection.

Original: Our method yields consistent improvements on all three datasets, and signiﬁcantly outperforms the baselines that judge the authenticity for each document in isolation.
Revised: Our method yields consistent improvements on all three datasets and signiﬁcantly outperforms the baselines that judge the authenticity for each document in isolation.

Original: To understand the effectiveness of each component, we conduct an ablation study and show the results in Table 4.
Revised: To understand the effectiveness of each component, we conducted an ablation study and show the results in Table 4.

Original: We have the following ﬁndings:
Revised: We have the following ﬁndings:

Original: (1) We remove the edges between event nodes and event center nodes to analyze the impact of cross document event coreference resolution, and ﬁnd that such information signiﬁcantly improves the performance on IED and TL17.
Revised: (1) We remove the edges between event nodes and event center nodes to analyze the impact of cross-document event coreference resolution, and ﬁnd that such information signiﬁcantly improves the performance on IED and TL17.

Original: We also train our detector with smaller clusters on TL17 and get worse performance ( 84 .
Revised: We also train our detector with smaller clusters on TL17 and get worse performance ( 84 .

Original:  and 87 .
Revised: 53% and 87 .

Original:  on clus - ters with size 1 and respectively
Revised: 37% on clusters with size 1 and 2 respectively), which veriﬁes

Original: ), which veriﬁes that our model beneﬁts from more cross-document information.
Revised: that our model beneﬁts from more cross-document information

Original: The beneﬁt of cross-document event coreference resolution is less signiﬁcant on the large-scale Crisis dataset containing 1.7 k documents.
Revised: The beneﬁt of cross-document event coreference resolution is less signiﬁcant on the large-scale Crisis dataset containing 1.7 k documents.

Original: This may imply that cross-document misinformation detection is more useful for emerging new events where large-scale training data is not available.
Revised: This may imply that cross-document misinformation detection is more useful for emerging new events where large-scale training data is not available.

Original: (2) Using the event-level detection results consistently improves the performance by 1 - 3 points on all datasets. Since the projection modules introduce additional parameters, we further train a
Revised: (2) Using the event-level detection results con-

Original: [Table]
Revised: [Table]

Original: Crisis
Revised: We further conduct an ablation study and report the results of our method without cross-document event coreference information, denoted as Ours ( sistently improves the performance by 1 - 3 points on all datasets.

Original: [Table] detector utilizing random features and ﬁnd that using random features reduces the performance.
Revised: Since the projection modules introduce additional parameters, we further train a detector utilizing random features and ﬁnd that using random features reduces the performance.

Original: This veriﬁes that the improvement is brought by utilizing the knowledge learnt by the event-level detector rather than additional parameters.
Revised: This veriﬁes that the improvement is brought by utilizing the knowledge learnt by the event-level detector rather than additional parameters.

Original: 6. Event-level Detection Results
Revised: 6. Event-level Detection Results

Original: We track the manipulation operations during the dataset construction process, which allows us to obtain supervision for event-level detection.
Revised: We track the manipulation operations during the dataset construction process, which allows us to obtain supervision for event-level detection.

Original: The results are shown in Table 5.
Revised: The results are shown in Table 5.

Original: We compare our method with random guessing, logistic regression with hand-crafted event features, and BERT.
Revised: We compare our method with random guessing, logistic regression with hand-crafted event features, and BERT.

Original: We ﬁnd that random guessing performs the worst, logistic regression and BERT achieve satisfactory performance, and our method signiﬁcantly outperforms all baselines by a large margin.
Revised: We ﬁnd that random guessing performs the worst, logistic regression and BERT achieve satisfactory performance, and our method signiﬁcantly outperforms all baselines by a large margin.

Original: As in documentlevel detection, we conduct an ablation study on the use of cross-document event coreference resolution by removing edges between event nodes and event cluster nodes, and ﬁnd that such information brings slight improvements over AUC metric.
Revised: As in documentlevel detection, we conduct an ablation study on the use of cross-document event coreference resolution by removing edges between event nodes and event cluster nodes, and ﬁnd that such information brings slight improvements in the AUC metric.

Original: 6. Analysis and Discussion
Revised: 6. Analysis and Discussion

Original: To demonstrate the beneﬁts of using crossdocument event coreference resolution, we show an example in Figure 4, with 4 documents from the same cluster.
Revised: To demonstrate the beneﬁts of using crossdocument event coreference resolution, we show an example in Figure 4, with 4 documents from the same cluster.

Original: As shown in Figure 4, by performing cross-document reasoning on events in the same event cluster, our model achieves better performance compared to Ours (ABLATION) , i.e., our model without edges between event nodes and event cluster nodes.
Revised: As shown in Figure 4, by performing cross-document reasoning on events in the same event cluster, our model achieves better performance compared to Ours (ABLATION) , i.e., our model without edges between event nodes and event cluster nodes.

Original: We further analyze the remaining errors from our model.
Revised: We further analyze the remaining errors in our model.

Original: Figure 5 shows two representative cases where both document-level and event-level detectors fail to detect misinformation.
Revised: Figure 5 shows two representative cases where both document-level and event-level detectors fail to detect misinformation.

Original: In the ﬁrst example, the manipulated entity is not captured by the
Revised: In the ﬁrst example, the manipulated entity is not captured by the

Original: IE system, and the error of IE system is propagated into the detector.
Revised: IE system and the error of IE system is propagated into the detector.

Original: A potential solution is to use an
Revised: A potential solution is to use an

Original: OpenIE system (Stanovsky et al., 2018) that is able to cover more event and entity types.
Revised: OpenIE system (Stanovsky et al., 2018) that is able to cover more event and entity types.

Original: The second example is a more challenging case where the event containing fake information is not mentioned by any other documents.
Revised: The second example is a more challenging case where the event containing fake information is not mentioned in any other document.

Original: This makes it difﬁcult to either verify or disprove via cross-document reasoning and may require the detector to actively search for external information related to the event.
Revised: This makes it difﬁcult to either verify or disprove via cross-document reasoning, and may require the detector to actively search for external information related to the event.

Original: There are some remaining challenges and limitations in our proposed methodology.
Revised: There are some remaining challenges and limitations in our proposed methodology.

Original: First, some cross-document contradictions are difﬁcult to capture by coreference resolution only.
Revised: First, some cross-document contradictions are difﬁcult to capture by coreference resolution only.

Original: In the example in Figure 1, knowing that the police is unlikely to help and attack Boyland at the same time requires commonsenseknowledge reasoning, which we leave as our future work.
Revised: In the example in Figure 1, knowing that the police are unlikely to help and attack Boyland at the same time requires commonsense reasoning, which we leave as our future work.

Original: Second, an underlying assumption of our framework is that real news articles are consistent and complementary with each other, while fake news often contradicts each other.
Revised: Second, an underlying assumption of our framework is that real news articles are consistent and complementary with each other, while fake news often contradicts each other.

Original: This assumption is true for our constructed datasets because we manipulate the KGs via random entity swapping.
Revised: This assumption is true for our constructed datasets because we manipulate the KGs via random entity swapping.

Original: However, certain types of humanwritten fake newsdocuments, such as conspiracy theories, tend to be closely related to each other and convey highly similar information because they visit of
Revised: However, certain types of human-written fake news be detected. In the second example, the visit of

Original: Vajpayee to Mumbai is fake information but not mentioned by any other documents, and no coreference is detected for the Transportation event.
Revised: Vajpayee to Mumbai is fake information but not mentioned by any other documents, and no coreference is detected for the Transportation event.

Original: Therefore, our detector does not have enough information to detect the fake information.
Revised: Therefore, our detector does not have enough information to detect the fake information.

Original:  share the same biases or aim to manipulate readers in the same way.
Revised: documents, such as conspiracy theories, tend to be closely related to each other and convey highly similar information because they share the same biases or aim to manipulate readers in the same way.

Original: This may limit the performance of our proposed system in real-world scenarios.
Revised: This may limit the performance of our proposed system in real-world scenarios.

Original: 6. Human Evaluation on Fake News Generation
Revised: 6. Human Evaluation on Fake News Generation

Original: To evaluate the quality of the generated fake news, we conduct a Turing Test by 13 human readers as in Fung et al.
Revised: To evaluate the quality of the generated fake news, we conducted a Turing Test by 13 human readers as in Fung et al.

Original: We randomly select 100 documents from the IED dataset, half real and half fake, and ask the human readers to assess the authenticity for each document.
Revised: We randomly selectdocuments from the IED dataset, half real and half fake, and ask the human readers to assess the authenticity for each document.

Original: The overall accuracy achieved by human readers is 66.88%, with 77.44% accuracy on real documents but only 56.32% accuracy on fake documents.
Revised: The overall accuracy achieved by human readers is 66.88%, with 77.44% accuracy on real documents but only 56.32% accuracy on fake documents.

Original: This shows that it is difﬁcult for human readers to detect the generated fake news.
Revised: This shows that it is difﬁcult for human readers to detect the generated fake news.

Original:  Conclusions and Future Work
Revised:  Conclusions and Future Work

Original: We are the ﬁrst to study the new task of crossdocument misinformation detection.
Revised: We are the ﬁrst to study the new task of crossdocument misinformation detection.

Original: We conduct the task at two levels, document level and the more ﬁne-grained event level, and construct three new datasets to handle the lack of training data.
Revised: We conduct the task at two levels, document level and the more ﬁne-grained event level, and construct three new datasets to handle the lack of training data.

Original: We further propose a graph-based cross-document detector that conducts reasoning over a cross-document knowledge graph and feed the event-level detection results into document-level detector.
Revised: We further propose a graph-based cross-document detector that conducts reasoning over a cross-document knowledge graph and feed the event-level detection results into document-level detector.

Original: Experimental results show that our proposed method signiﬁcantly outperforms existing methods.
Revised: The ex- perimental results show that our proposed method signiﬁcantly outperforms existing methods.

Original: For future work, we intend to extend our method to conduct cross-document reasoning over more types of information (e.g., entities and relations) in addition to events.
Revised: For future work, we intend to extend our method to conduct cross-document reasoning over more types of information (e.g., entities and relations) in addition to events.

Original: We also plan to extend our method to multi-media news including texts, images, audios and videos, which requires the construction of cross-document multi-modal knowledge graphs.
Revised: We also plan to extend our method to multi-media news including texts, images, audios and videos, which requires the construction of cross-document multi-modal knowledge graphs.

Original: Finally, a challenging but important task is to construct a large-scale fake news detection corpus with human-written fake news containing document clusters and study our method in this scenario.
Revised: Finally, a challenging but important task is to construct a large-scale fake news detection corpus with human-written fake news containing document clusters and study our method in this scenario.

Original:  Ethical Considerations
Revised:  Ethical Considerations

Original: The goal of this work is to advance state-of-theart research in the ﬁeld of misinformation detection, by analyzing multiple documents on the same topic.
Revised: The goal of this work is to advance state-of-theart research in the ﬁeld of misinformation detection by analyzing multiple documents on the same topic.

Original: We build new benchmark datasets using a fake news generator, and propose a detector that achieves high performance in such scenarios.
Revised: We build new benchmark datasets using a fake news generator, and propose a detector that achieves high performance in such scenarios.

Original: We have released the constructed datasets and detector codes in this submission as a useful reference for future research.
Revised: We have released the constructed datasets and detector codes in this submission as a useful reference for future research.

Original: We hope  our work will encourage more efforts in this direction and beneﬁt the community.
Revised: We hope that our work will encourage more efforts in this direction and beneﬁt the community.

Original: However, as with any work that utilizes text generation, our work involves the risk of being applied to produce false information to mislead or manipulate readers.
Revised: However, as with any work that utilizes text generation, our work involves the risk of being applied to produce false information to mislead or manipulate readers.

Original: Therefore, we promise not to share codes or checkpoints of our generator to avoid potential negative consequences.
Revised: Therefore, we promise not to share codes or checkpoints of our generator to avoid potential negative consequences.

Original: To improve reproducibility, we describe the general idea and a few crucial details of the fake news generator.
Revised: To improve reproducibility, we describe the general idea and a few crucial details of the fake news generator.

Original: Acknowledgement
Revised: Acknowledgement

Original: This research is based upon work supported by U.S.
Revised: This research is based upon work supported by U.S.

Original: DARPA SemaFor Program No. HR001120C and DARPA KAIROS
Revised: DARPA SemaFor Program No. HR001120C and DARPA KAIROS

Original: Program No. FA8750-19-21004.
Revised: Program No. FA8750-19-21004.

Original: 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.
Revised: 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.

Original: The U.S. Government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copyright annotation therein.
Revised: The U.S. Government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copy- annotation therein.

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Original: A Statistics of Original Datasets
Revised: A Statistics of Original Datasets

Original: Statistics of the original IED, TL17 and Crisis dataset are presented in Table 6.
Revised: Statistics of the original IED, TL17 and Crisis dataset are presented in Table 6.

Original: # Cluster # Doc # Doc per cluster IED TL
Revised: [Table]

Original: Crisis
Revised: Crisis

Original: B Method Details B. Information Extraction
Revised: B Method Details B. Information Extraction

Original: We use OneIE (Lin et al., 2020), a BERT-based end-to-end IE system to extract entities, relations and events.
Revised: We use OneIE (Lin et al., 2020), a BERT-based end-to-end IE system to extract entities, relations, and events.

Original: OneIE conducts IE in four steps: (1) encode a sentence with a pre-trained BERT encoder, (2) identify entity mentions and event triggers using a conditional random ﬁelds layer, (3) classify types of entity mentions, events, entity relations and event arguments using feed-forward networks, and (4) search for a globally optimal IE graph via beam search.
Revised: OneIE conducts IE in four steps: (1) encode a sentence with a pre-trained BERT encoder, (2) identify entity mentions and event triggers using a conditional random ﬁelds layer, (3) classify types of entity mentions, events, entity relations, and event arguments using feed-forward networks, and (4) search for a globally optimal IE graph via beam search.

Original: In this work, we use the model released by Wen et al.
Revised: In this work, we use the model released by Wen et al.

Original: The model achieves 64.1, 49.7, and 49.5 F1 on trigger extraction, argument extraction and relation extraction respectively on ACE 2005 and ERE (Song et al., 2015).
Revised: The model achieves 64.1, 49.7, and 49.5 F1 on trigger extraction, argument extraction and relation extraction respectively on ACE 2005 and ERE (Song et al., 2015).

Original: In addition, we use entity linking and entity coreference resolution to identify coreferential entity mentions.
Revised: In addition, we use entity linking and entity coreference resolution to identify coreferential entity mentions.

Original: For entity linking , we use an LSTMbased entity linker to link (Pan et al., 2017) to link entity mentions to WikiData entries.
Revised: For entity linking , we use an LSTMbased entity linker to link (Pan et al., 2017) to link entity mentions to WikiData entries.

Original: The entity linker achieves 91.8 F1 and 84.3 accuracy.
Revised: The entity linker achieves 91.8 F1 and 84.3 accuracy.

Original: For entity coreference resolution , we use an extension of the e2e-coref model (Lee et al., 2017) based on
Revised: For entity coreference resolution , we use an extension of the e2e-coref model (Lee et al., 2017) based on

Original: XLM-RoBERTa (Conneau et al., 2020).
Revised: XLM-RoBERTa (Conneau et al., 2020).

Original: The model is released by Wen et al.
Revised: The model is released by Wen et al.

Original: and achieves  92.
Revised: and achieves a 92.

Original: CoNLL score on OntoNotes (Pradhan et al., 2012).
Revised: CoNLL score on OntoNotes (Pradhan et al., 2012).

Original: Eventually, entity mentions that are linked to the same WikiData entry or identiﬁed as coreferences will be considered as the same entity, and their entity nodes in the KG will be merged.
Revised: Eventually, entity mentions that are linked to the same WikiData entry or identiﬁed as coreferences will be considered as the same entity, and their entity nodes in the KG will be merged.

Original: B. Event Coreference Resolution
Revised: B. Event Coreference Resolution

Original: For event coreference resolution, we use Lai et al.
Revised: For event coreference resolution, we use Lai et al.

Original: (2021), a within-document coreference resolution model.
Revised: (2021), a within-document coreference resolution model.

Original: We extend it to the cross-document scenario following Wen et al.
Revised: We extend it to the cross-document scenario following Wen et al.

Original: Given a cluster containing N documents, we concatenate each pair of documents into a “mega-document”.
Revised: Given a cluster containing N documents, we concatenate each pair of documents into a “mega-document”.

Original: The model then conducts coreference resolution on each mega-document.
Revised: The model then conducts coreference resolution on each mega-document.

Original: More speciﬁcally, for each event mention, the model uses SpanBERT (Joshi et al., 2020) to extract contextualized text embeddings and builds manually designed symbolic features such as event types, attributes, and arguments.
Revised: More speciﬁcally, for each event mention, the model uses SpanBERT (Joshi et al., 2020) to extract contextualized text embeddings and builds manually designed symbolic features such as event types, attributes, and arguments.

Original: Then, the two features are combined selectively using a gated mechanism.
Revised: Then, the two features are combined selectively using a gated mechanism.

Original: Eventually, for each pair of event mentions in a mega-document, the model predicts whether they are coreferential.
Revised: Eventually, for each pair of event mentions in a mega-document, the model predicts whether they are coreferential.

Original: In this work, we use the model released by Wen et al.
Revised: In this work, we use the model released by Wen et al.

Original: The model achieves 84.8 CoNLL score on
Revised: The model achieves 84.8 CoNLL score on

Original: ACE 2005.
Revised: ACE 2005.

Original: B. KG-to-Text Generator
Revised: B. KG-to-Text Generator

Original: We train the KG-to-text generator  following Fung et al.
Revised: We train the KG-to-text generator by following Fung et al.

Original: We ﬁrst linearize the IE-based KG.
Revised: We ﬁrst linearize the IE-based KG.

Original: For example, the Leadership relation between “Nicolas Maduro” and “Venezuelan” is represented by “ Nicolas Maduro, Leadership, Venezuelan ”, and the DetonateExplode event with “drone” as ExplosiveDevice argument and “ﬂat” as Place argument is represented by “ [DetonateExplode | ExplosiveDevice = drone, Place = ﬂat] ”.
Revised: For example, the Leadership relation between “Nicolas Maduro” and “Venezuelan” is represented by “ Nicolas Maduro, Leadership, Venezuelan ”, and the DetonateExplode event with “drone” as ExplosiveDevice argument and “ﬂat” as Place argument is represented by “ [DetonateExplode | ExplosiveDevice = drone, Place = ﬂat] ”.

Original: We represent the entire KG with graph by concatenating the text representations of all relations and events.
Revised: We represent the entire KG in graph by concatenating the text representations of all relations and events.

Original: Since generating the entire document is very challenging, we ﬁne-tune a sentence-level KG-totext generator from BART (Lewis et al., 2020).
Revised: Since generating the entire document is very challenging, we ﬁne-tune a sentence-level KG-totext generator from BART (Lewis et al., 2020).

Original: The generator takes the linearized KG and the previous sentence as input and generates the next sentence.
Revised: The generator takes the linearized KG and the previous sentence as input and generates the next sentence.

Original: Here, the KG only contains information presented in the sentence rather than in the entire document.
Revised: Here, the KG only contains information presented in the sentence rather than in the entire document.

Original: During inference, the generator generates the entire document sentence-by-sentence in an autoregressive manner.
Revised: During inference, the generator generates the entire document sentence-by-sentence in an autoregressive manner.

Original: C Experiment Details
Revised: C Experiment Details

Original: Detailed settings of our method: For our proposed method, we use a 4 -layer heterogeneous
Revised: Detailed settings of our method: For our proposed method, we use a 4 -layered heterogeneous

Original: GNN, where each GAT layer contains 8 heads.
Revised: GNN, where each GAT layer contains 8 heads.

Original: To initialize the node and edge embeddings, we use bert-base-uncased model with the feature dimension of 768 .
Revised: To initialize the node and edge embeddings, we use bert-base-uncased model with the feature dimension of 768 .

Original: Our model contains 233 M parameters.
Revised: Our model contains 233 M parameters.

Original: For hyperparameters, we use a batch size of 16 , and search the learning rate from { 10 − 3 , 10 − 4 , 10 − 5 } and the number of layers within { 2 , 4 , 8 } .
Revised: For hyperparameters, we use a batch size of 16 , and search the learning rate from { 10 − 3 , 10 − 4 , 10 − 5 } and the number of layers within { 2 , 4 , 8 } .

Original: Our best-found hyperparameters are a learning rate of 10 − 5 and a number of layer of 4 .
Revised: Our best-found hyperparameters are a learning rate of 10 − 5 and a number of layers of 4 .

Original: We train our model with Adam optimizer until convergence.
Revised: We train our model with Adam optimizer until convergence.

Original: To reduce computation cost, we freeze BERT’s parameters.
Revised: To reduce computation cost, we freeze BERT’s parameters.

Original: The training process takes approximately 6 hours on a Tesla P100 GPU.
Revised: The training process takes approximately 6 hours on a Tesla P100 GPU.

Original: Detailed settings of KG-to-text generator: We ﬁne-tune the model from bart-large model containing 24 layers, 1024 hidden dimensions,heads, and 406 M parameters.
Revised: Detailed settings of KG-to-text generator: We ﬁne-tune the model from bart-large model containing 24 layers, 1024 hidden dimensions,heads, and 406 M parameters.

Original: on the three datasets respectively.
Revised: on the three datasets respectively.

Original: We train the model on a Tesla P
Revised: We train the model on a Tesla P

Original: GPU using the batch size of 1024 tokens, the gradient accumulation step of 16 , the learning rate of 3 × 10 − 5 , the warmup steps of 500 steps, and the total training steps of 12000 .
Revised: GPU using the batch size of 1024 tokens, the gradient accumulation step of 16 , the learning rate of 3 × 10 − 5 , the warmup steps of 500 steps, and the total training steps of 12000 .

Original: Document-level baselines:
Revised: Document-level baselines:

Original: For document-level detection, we compare our method against two baselines:
Revised: For document-level detection, we compare our method against two baselines:

Original: HDSF that models inter-sentence dependency tree (Karimi and Tang, 2019), and GROVER (Zellers et al., 2019), a Transformer-based detector.
Revised: HDSF that models inter-sentence dependency tree (Karimi and Tang, 2019), and GROVER (Zellers et al., 2019), a Transformer-based detector.

Original: For HDSF, we use the implementation at https: //github.com/hamidkarimi/HDSF/ .
Revised: For HDSF, we use the implementation at https: //github.com/hamidkarimi/HDSF/ .

Original: We train the model on our datasets using their default hyper-parameters.
Revised: We train the model on our datasets using their default hyper-parameters.

Original: For GROVER, we use the implementation at https://github.com/ rowanz/grover and experiment with two settings, medium setting and mega setting.
Revised: For GROVER, we use the implementation at https://github.com/ rowanz/grover and experiment with two settings, medium setting and mega setting.

Original: Since ﬁne-tuning the GROVER model is computationally expensive, we use GROVER in the zero-shot setting.
Revised: Since ﬁne-tuning the GROVER model is computationally expensive, we use GROVER in the zero-shot setting.

Original: Event-level baselines:
Revised: Event-level baselines:

Original: For event-level detection, since there are no existing methods, we use three baselines, random guessing, logistic regression, and BERT.
Revised: For event-level detection, since there are no existing methods, we use three baselines, random guessing, logistic regression, and BERT.

Original: In random guessing, for each event, we randomly draws a value from a uniform distribution between [0 , 1] as the probability that the event is false.
Revised: In random guessing, for each event, we randomly draw a value from a uniform distribution between [0 , 1] as the probability that the event is false.

Original: In logistic regression, we use the following features: event type (represented by onehot feature), number of arguments, and the size of the event cluster that the given event belongs to.
Revised: In logistic regression, we use the following features: event type (represented by onehot feature), number of arguments, and the size of the event cluster that the given event belongs to.

Original: The features are normalized on the training set.
Revised: The features are normalized on the training set.

Original: We use the implementation of logistic regression and default parameters provided by sklearn .
Revised: We use the implementation of logistic regression and default parameters provided by sklearn .

Original: In the BERT baseline, we use the same BERT-based event features as our method, and replace the 4layer GNN in our model with a feed-forward network.
Revised: In the BERT baseline, we use the same BERT-based event features as our method, and replace the 4layer GNN in our model with a feed-forward network.

Original: We use the same hyper-parameters to train the model.
Revised: We use the same hyper-parameters to train the model.

Original: D Examples of Fake News Generation
Revised: D Examples of Fake News Generation

Original: We present two examples of generated fake news in Figure 6 and 7, including the original real news, manipulated KG, and generated fake news.
Revised: We present two examples of generated fake news in Figure 6 and 7, including the original real news, manipulated KG, and generated fake news.

Original: The generated fake news conveys the manipulated mis- information and meanwhile is stylistically similar to real news.
Revised: The generated fake news conveys the manipulated misinformation and meanwhile is stylistically similar to real news.

Original: E Scientiﬁc Artifacts
Revised: E Scientiﬁc Artifacts

Original: In this work, we use three datasets including IED (Li et al., 2021), TL17 (Tran et al., 2013) and Crisis (Tran et al., 2015).
Revised: In this work, we use three datasets including IED (Li et al., 2021), TL17 (Tran et al., 2013) and Crisis (Tran et al., 2015).

Original: There are no licenses or terms of use associated with all three datasets.
Revised: There are no licenses or terms of use associated with all three datasets.

Original: We use ﬁve software.
Revised: We use ﬁve software.

Original: Among them, HDSF (Karimi and Tang, 2019), OneIE (Lin et al., 2020) and RESIN (Wen et al., 2021) have no license or terms of use.
Revised: Among them, HDSF (Karimi and Tang, 2019), OneIE (Lin et al., 2020) and RESIN (Wen et al., 2021) have no license or terms of use.

Original: GROVER (Zellers et al., 2019) and huggingface are licensed under the Apache License 2.0.
Revised: GROVER (Zellers et al., 2019) and huggingface are licensed under the Apache License 2.0.

Original: Fairseq (Ott et al., 2019) is licenced under the
Revised: Fairseq (Ott et al., 2019) is licenced under the

Original: MIT License.
Revised: MIT License.

Original: We use two models, BERT (Devlin et al., 2019) and BART (Lewis et al., 2020), licenced under the
Revised: We use two models, BERT (Devlin et al., 2019) and BART (Lewis et al., 2020), licenced under the

Original: Apache License 2.0 and the MIT License respectively.
Revised: Apache License 2.0 and the MIT License respectively.

Original: In summary, all artifacts involved either have no associated licenses or terms of use, or are licensed under the Apache License 2.0 or the MIT License.
Revised: In summary, all artifacts involved either have no associated licenses or terms of use, or are licensed under the Apache License 2.0 or the MIT License.

Original: Both the Apache License 2.0 or the MIT License permit commercial and private use.
Revised: Both the Apache License 2.0 or the MIT License permit commercial and private use.

Original: Therefore, our use is consistent with their intended use.
Revised: Therefore, our use is consistent with their intended use.

Original: We will release the dataset and software with licenses compatible with the original access conditions.
Revised: We will release the datasets and software with licenses compatible with the original access conditions.