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

Original: Anonymous ACL submission

Original: Abstract
Revised: Abstract

Original: For emerging events, human readers are oftenexposed 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 readerscan 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 noveltask 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 relatednews 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 lackof data, we generate fake news by manipulating real news, and construct 3 new datasetswith 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 resultsinto 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 upto 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 multiplenews documents, where some are real and othersare 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 sourcesnaturally 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 sametopic may contain conﬂicting or complementaryinformation, 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 Figure1.
Revised: An example is shown in Figure 1.

Original: As shown in the knowledge graph, the deathof Rosanne Boyland in 2021 US Capitol attack isa 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, which makes 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 crossdocument 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 existing work in 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 cross-document 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 conduct the task at both 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., the death 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 death cause 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: Eventlevel 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 detects fake knowledge triplets (Fung et al., 2021).
Revised: Existing work on ﬁne-grained misinformation detection detects fake knowledge triplets (Fung et al., 2021).

Original: However, we focus on identifying false events instead of relations or entities, because events are more important to 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 to 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: By tracking the manipulation operations, we also obtain supervision for

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

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

Original: Then, weconnect the within-document KGs to form a crossdocument KG using cross-document event coreference resolution (Lai et al., 2021; Wen et al., 2021).
Revised: Then, we connect the withindocument KGs to form a cross-document KG using cross-document event coreference resolution (Lai et al., 2021; Wen et al., 2021).

Original: Eventually, we use a heterogeneous graph neuralnetwork (GNN) to encode the cross-document KGand 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 cate- gorized 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 cross- document information and improve document- level 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 datasets demonstrate that our method signiﬁ- cantly outperforms existing methods.
Revised: Experiments ondatasets demonstrate that our method signiﬁcantly outperforms existing methods.

Original:  Related Work
Revised:  Related Work

Original: Fake News Detection: Early work for fake newsdetection uses hand-crafted features to conductdocument classiﬁcation (Rubin et al., 2016; Wang,2017; Rashkin et al., 2017; Pérez-Rosas et al., 2018;
Revised: 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;

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

Original: Recentwork uses neural network such as RNN (Karimiet al., 2018; Nasir et al., 2021) and Transformer (Zellers et al., 2019) to encode the news document.
Revised: Recent work uses neural network such as recurrent neural network (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) models the intersentence dependency tree, Vaibhav et al.
Revised: To model the internal structure of a news document, Karimi and Tang (2019) models the inter-sentence dependency tree, Vaibhav et al.

Original: (2019) and Hu et al.
Revised: (2019) and 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) compares the news with external knowledge base (KB) to check for inconsistencies.
Revised: (2021) compares the 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 multi-modal 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 multimodal 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 cannotmodel the complicated internal structure of a newsdocument.
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, andunreliable 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 issueis that the detector may only learn to distinguishthe style of different news sources, rather thanthe 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)collects annotations from fact-checking websites,and Pérez-Rosas et al.
Revised: (2020) collects annotations from fact-checking websites, and Pérez-Rosas et al.

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

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

Original: With the development of powerfulgenerative models capable of mimicking humanwritten news (Zellers et al., 2019), recent work hasconstructed datasets by using generative models togenerate 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 generates fakenews from manipulated KG, which we follow toconstruct our dataset.
Revised: (2021) further generates 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 inthe 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 which 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 conduct detection.

Original: We further incorporate the results of event-level detection tohelp 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 awithin-document IE-based knowledge graph foreach 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  and entity coreference resolution (Lee et al., 2017; Wenet al., 2021) to merge multiple mentions of the sameentities 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 argumentsare 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 connectthe 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 (Laiet al., 2021; Wen et al., 2021) to identify clustersof events from multiple documents that refer tothe 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 utilizesboth textual contexts of the event mentions and thesymbolic features such as the event type information.
Revised: The system utilizes both textual contexts of the event mentions and the symbolic features such as the event type information.

Original: An example of the detected event cluster isshown in Table 1, where the four events from fourdocuments all refer to the same explosion attack on
Revised: 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

Original: Venezuela President Nicolas Marduro.
Revised: Venezuela President Nicolas Marduro.

Original: These fourevents contain complementary or contradictorydetails, which can be used for misinformationdetection.
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 torepresent the overall information of the real-worldcomplex 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 andcorresponding cluster node to allow reasoningamong 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 eventbelongs to and capture the global information ofeach document, we further introduce a documentnode and connect it to the associated entity andevent 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, andevent cluster nodes) and 5 types of edges (i.e. relation edges, event argument edges, document-toentity edges, document-to-event edges, and edgesconnecting event nodes to event cluster nodes).
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: Since all edges are directional, we add an inverseedge for each edge to propagate features along both
Revised: Since all edges are directional, we add an inverse

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: · · ·  directions, and the ﬁnal KG contains 10 edge types, accounting for the inverse of existing edge types.
Revised: · · · 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: To initialize the node and edge embeddings in the KG, we use BERT(Devlin et al., 2019) to encode the text descriptions of nodes or edges and take the embeddings of [CLS] tokens.
Revised: KG representation: We use BERT (Devlin et al., 2019) to initialize the node and edge embeddings in the KG. For a document node, we use BERT to encode the entire document and take the embeddings of [CLS] tokens.

Original: For a document node, we use the entire document as the text description; for an entity node, we use its canonical mention; for an event node, we use the sentence where the event trigger occurs; and for an event cluster node, we  average  the embeddings of all events in the cluster as the embedding for the cluster node.
Revised: Similarly, for an entity node, we encode its canonical mention. For an event node, we encode the sentence where the event trigger occurs. 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 use the linearized representation as the text description.
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 described as “ Nicolas Maduro, Leadership, Venezuelan ” , and “guns” as the ExplosiveDevice argument of the DetonateExplode event is described as “ DetonateExplode, ExplosiveDevice, guns ”.
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 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 het-  ereogeneous GNN to encode the KG.
Revised: Heterogeneous GNN: Given the heterogeneous nature of the cross-document KG, we adopt a hetereogeneous GNN to encode the KG.

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

Original: We use R to denote the 10 types ofedges as discussed in the previous section, and foreach 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 thel -th layer, the inputs are output features producedby 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: Foreach edge type r ∈ R , we apply a separate GNNto encode G r and produce a set of features denotedas 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 edgetypes 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 toevent cluster nodes, we use standard graph attentionnetwork (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 leveragethe 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 tothe BERT embeddings of text descriptions suchas “ 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 Section4.2 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 neighborsvia 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 theneighbors 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, theattention 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: [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 considersedge features in addition to node features (Huanget 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 denotethe features of the edge between node i and j .
Revised: Let r ij denote the features of the edge between node 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:[Equation]
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, andmore examples are presented in Appendix C.
Revised: Figure 3 shows an overview of 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)’smethod and ours in terms of manipulating KG are:(1) we only conduct entity swapping, and do notadopt other types of manipulation including addingrelations 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) weselect entities from other documents in the samedocument cluster to replace the original entities, sothat the entities before and after replacement aremore 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 usea heuristic rule to obtain supervision for event-leveldetection 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 
Revised:  Experiments 6. Data

Original: 6. Data

Original: We construct 3 new benchmark datasets based onthree datasets that naturally have clusters of topically related documents.
Revised: We construct 3 new benchmark datasets based on three datasets that naturally have clusters of topically related documents.

Original: IED dataset is a complexevent corpus, where each complex event refers toa real-world story (e.g., Boston bombing) and isdescribed by multiple documents (Li et al., 2021).
Revised: IED dataset 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 adocument cluster.
Revised: Therefore, a complex event can be considered as a document cluster.

Original: TL17 and Crisis are two timeline summarization datasets containing multiple 
Revised: TL17 and Crisis are two timeline summarization datasets containing multiple “timelines”.

Revised: Each timeline contains multiple documents describing an evolving long-term event such # Cluster

Original: [Table]
Revised: [Figure]

Revised: Doc

Revised: [Figure]

Revised: Fake

Original: “timelines”.
Revised: [Table] per [Table] [Figure]

Original: 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.
Revised: IED Train [Table] 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, the 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 size of 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 of 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 -layer 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 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,since there are no existing methods, we compare our method againsttwo heuristic baselines: random guessing and logistic regression  .
Revised: On the event-level detection task, we compare our method against random guessing , logistic regression and BERT .

Original: For logistic regression, we use handcrafted 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 Appendix B.
Revised: The detailed settings are presented in 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 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 detection.

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

Original: [Table] [Table]
Revised: [Table] [Table]

Original: 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 improvementson all 3 datasets, and signiﬁcantly outperforms thebaselines that judge the authenticity for each document in isolation.
Revised: Our method yields consistent improvements on all 3 datasets [Table] and signiﬁcantly outperforms the baselines that judge the authenticity for each document in isolation.

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

Original: We have the following ﬁndings:(1)
Revised: We have the following ﬁndings:

Original:  We remove the edges between event nodesand event center nodes to analyze the impact ofcross-document event coreference resolution, andﬁnd that such information signiﬁcantly improvesthe 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 trainour detector with smaller clusters on TL17 and getworse performance ( 84 .
Revised: We also train our detector with smaller clusters on TL17 and get worse performance ( 84 .

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

Original: 37% on clusters with size 1 and 2 respectively), which veriﬁesthat our model beneﬁts from more cross-documentinformation.
Revised: 37% on clusters with size 1 and 2 respectively , which veriﬁes that our model beneﬁts from more cross-document information

Original: The beneﬁt of cross-document eventcoreference resolution is less signiﬁcant on thelarge-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 emergingnew events where large-scale training data is notavailable.(2) Using the event-level detection results consistently improves the performance by 1 - 3 points 
Revised: This may imply that cross-document misinformation detection is more useful for emerging new events where large-scale training data is not available. (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

Original: IED
Revised: IED

Revised: TL

Original: [Table]
Revised: [Table]

Original: on all datasets. Since the projection modules introduce additional parameters, we further train a detector utilizing random features and ﬁnd that using random features reduces the performance.
Revised: 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: Since there are no existing methods for this new task, we compare our method withtwo heuristic baselines, random guessing and logistic regression with hand-crafted event features.
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 achieves satisfactory performance, and our method signiﬁcantly outperforms these two 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 document-level 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 over 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 from 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 propagatedinto 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 ableto 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 secondexample is a more challenging case where the eventcontaining fake information is not mentioned byany other documents.
Revised: The second example is a more challenging case where the event containing fake information is not mentioned by any other documents.

Original: This makes it difﬁcult toeither verify or disprove via cross-document reasoning, and may require the detector to activelysearch 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, somecross-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 examplein Figure 1, knowing that the police is unlikelyto help and attack Boyland at the same time requires commonsense knowledge reasoning, whichwe leave as our future work.
Revised: In the example in Figure 1, knowing that the police is unlikely to help and attack Boyland at the same time requires commonsense knowledge reasoning, which we leave as our future work.

Original: Second, an underlyingassumption of our framework is that real news articles are consistent and complementary with eachother, 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 datasetsbecause 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 human- written fake news 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.
Revised: However, certain types of humanwritten fake news documents, such as conspiracy theories, tend to be closely related to each other and convey highly similar information because they visit of Vajpayee to Mumbai is fake information but not mentioned by any other documents, and no coreference is detected for the Transportation event. Therefore, our detector does not have enough information to detect the fake information. 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.

Revised: 6. Human Evaluation on Fake News Generation

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

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:   Conclusions and Future Work
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. This shows that it is difﬁcult for human readers to detect the generated fake news. This indicates that the automatically generated fake documents are of sufﬁcient quality and likely to trick human readers.  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 3 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 3 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: Experimental 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 sametopic.
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 afake news generator, and propose a detector thatachieves 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: Wehave released the constructed datasets and detectorcodes in this submission as a useful reference forfuture 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 thecommunity.
Revised: We hope 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 appliedto 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 sharecodes 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 fewcrucial 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: Association for
Revised: Acknowledgement

Original: Linmei Hu, Tianchi Yang, Luhao Zhang, Wanjun
Revised: This research is based upon work supported by U.S.

Original: Zhong, Duyu Tang, Chuan Shi, Nan Duan, Ming the Hsiang
Revised: DARPA SemaFor Program No. HR001120C and DARPA KAIROS

Original: Mu Nanyun Peng
Revised: Program No. FA8750-19-21004.

Original: Dhruv Khattar Singh
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: Manish ACM. North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2021, Online, June 6-11, 2021 ,pages 3491–3499.
Revised: The U.S. Government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copyright annotation therein.

Original:  Association for Computational Linguistics.
Revised: . Association for Computational Linguistics.

Original: Kenton Lee, Luheng He, Mike Lewis, and Luke Zettlemoyer.
Revised: Lee, Luheng He, Mike Lewis, and Luke Zettlemoyer

Original: End coreference resolution.
Revised: End-to-end neural coreference reso lution

Original: In Proceedings of the 2017 Conferenceon
Revised: 2017 Conference

Original: Empirical Methods in Natural Language Processing,
Revised: Empirical Methods in Natural Language Processing

Original: EMNLP 2017, Copenhagen, Denmark, September 911, 2017 , pages 188–197.
Revised: EMNLP 2017, Copenhagen, Denmark, September 911, 2017 , pages 188–197.

Original: Association for Computational Linguistics.
Revised: Association for Computational Linguistics.

Original: Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer
Revised: Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer

Original: Levy, Veselin Stoyanov, and Luke Zettlemoyer.
Revised: Levy, Veselin Stoyanov, and Luke Zettlemoyer

Original: BART sequence pre

Original: training translation, comprehension

Original: nual Meeting of Association Linguistics, ACLOnline
Revised: ACL

Original: 2020 ,pages 7871–7880.

Original: Association for Computational Linguistics.
Revised: Association for Computational Linguistics.

Original: Manling Li, Sha Li, Zhenhailong Wang, Lifu Huang,
Revised: Manling Li, Sha Li, Zhenhailong Wang, Lifu Huang,

Original: Kyunghyun Cho, Heng Ji, Jiawei Han, and Clare R.
Revised: Kyunghyun Cho, Heng Ji, Jiawei Han, and Clare R

Original: Voss. 2021. Future is not one-dimensional: Graphmodeling based complex event schema induction forevent prediction. CoRR , abs/2104.06344.
Revised: CoRR abs

Original: Ying Lin, Heng Ji, Fei Huang, and Lingfei Wu.
Revised: Ying Lin, Heng Ji, Fei Huang, and Lingfei Wu

Original: A joint neural model for information extraction withglobal features.

Original: In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, Online, July 5-10, 2020 , pages7999–8009.
Revised: International Conference on Computational Linguistics, COLING 2018, Santa Fe, New Mexico, USA, August 20-26, 2018 , pages 3391–3401.

Original: Association for Computational Linguistics.
Revised: Association for Computational Linguistics.

Original: Jamal Abdul Nasir, Osama Subhani Khan, and Iraklis
Revised: Sameer Pradhan, Alessandro Moschitti, Nianwen Xue, Olga Uryupina, and Yuchen Zhang. Conll2012 shared task:

Original: Verónica Pérez-Rosas
Revised: Modeling multilingual unrestricted coreference in ontonotes. In Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning - Proceedings of the Shared Task: Modeling Multilingual Unrestricted Coreference in OntoNotes, EMNLP-CoNLL 2012, July 13, 2012,

Original: Bennett Kleinberg
Revised: Jeju Island, Korea , pages 1–40. ACL. Hannah Rashkin, Eunsol Choi, Jin Yea Jang, Svitlana Volkova, and Yejin Choi. 2017.

Original: Alexandra in fake news and political fact-checking
Revised: Truth of varying shades: Analyzing language in fake news and political fact-checking.

Original: In Proceedings of the 2017 Conference on Empirical Methods in Natural Language
Revised: In Proceedings of the 2017 Conference on Empirical Methods in Natural Language

Original: Processing, EMNLP 2017, Copenhagen, Denmark,
Revised: Processing, EMNLP 2017, Copenhagen, Denmark,

Original: September 9-11, 2017 , pages 2931–2937.
Revised: September 9-11, 2017 , pages 2931–2937.

Original: Association for Computational Linguistics.
Revised: Association for Computational Linguistics.

Original: Victoria L Rubin, Niall Conroy, Yimin Chen, and Sarah
Revised: Victoria L Rubin, Niall Conroy, Yimin Chen, and Sarah

Original: Cornwell. 2016.
Revised: Cornwell. 2016.

Original: Fake  satirical cues to  potentially misleading 
Revised: Fake news or truth? using satirical cues to detect potentially misleading news.

Original:  Proceedings  workshop  computational approaches to deception detection  pages 
Revised: In Proceedings of the second workshop on computational approaches to deception detection , pages 7–17.

Original: Arkadiy Saakyan, Tuhin Chakrabarty, and Smaranda
Revised: Arkadiy Saakyan, Tuhin Chakrabarty, and Smaranda

Original: Muresan. 2021.
Revised: Muresan. 2021.

Original: Covid-fact: Fact extraction and veriﬁcation of real-world claims on COVID-19 pandemic
Revised: Covid-fact: Fact extraction and veriﬁcation of real-world claims on COVID-19 pandemic.

Original: In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on
Revised: In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on

Original: Natural Language Processing, ACL/IJCNLP 2021, (Volume 1: Long Papers), Virtual Event, August 1-6, 2021 , pages 2116–2129.
Revised: Natural Language Processing, ACL/IJCNLP 2021, (Volume 1: Long Papers), Virtual Event, August 1-6, 2021 , pages 2116–2129.

Original: Association for Computational Linguistics.
Revised: Association for Computational Linguistics.

Original: Sohan De Sarkar, Fan Yang, and Arjun Mukherjee.
Revised: Sohan De Sarkar, Fan Yang, and Arjun Mukherjee.

Original: Attending sentences to  satirical fake news.
Revised: Attending sentences to detect satirical fake news.

Revised: In Proceedings of

Revised: Volume 1 (Long Papers) pages 885–

Revised: November 16-

Revised: James

Revised: Thorne,

Original: In Proceedings of the 27th International
Revised: Andreas Vlachos, Christos Christodoulopoulos, and

Revised: Arpit

Revised: Mittal.

Revised: FEVER: and veriﬁcation.

Revised: In Proceedings of the

Original: Conference on Computational Linguistics, COLING 2018,Santa Fe, New Mexico, USA, August 20-26, 2018 , pages 3371–3380.
Revised: Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2018, New Orleans, Louisiana, USA, June 1-6, 2018, Volume 1 (Long Papers) , pages 809–819.

Original: Association for Computational Linguistics.
Revised: Association for Computational Linguistics.

Original: Michael Sejr Schlichtkrull, Thomas N. Kipf, Peter
Revised: Giang Binh Tran, Mohammad Alrifai, and Eelco Herder. 2015. Timeline summarization from relevant headlines.

Original: Bloem, Rianne van den Berg, Ivan Titov, and Max
Revised: In Advances in Information Retrieval - 37th European Conference on IR Research, ECIR 2015, Vienna, Austria, March 29 - April 2, 2015. Proceedings , volume 9022 of Lecture Notes in Computer Science , pages 245–256. Giang Binh Tran, Mohammad Alrifai, and Dat Quoc

Original: Welling.
Revised: Nguyen. 2013.

Original: convolutional networks
Revised: Predicting relevant news events for timeline summaries.

Original: The Semantic Web 15th
Revised: In 22nd International World

Original: International Conference, ESWC Heraklion,
Revised: Wide Web Conference, WWW ’13, Rio de Janeiro,

Original: Gabriel Stanovsky, Julian Michael, Luke Zettlemoyer, Giang Binh Tran
Revised: Brazil, May 13-17, 2013, Companion Volume , pages 91– . International World Wide Web Conferences

Original: Mohammad Alrifai and Eelco
Revised: Steering Committee / ACM.

Original: Vaibhav Vaibhav Raghuram Mandyam Annasamy
Revised: Vaibhav Vaibhav, Raghuram Mandyam Annasamy, and

Original: Association forComputational Linguistics. Petar Velickovic
Revised: Eduard H. Hovy. 2019. Do sentence interactions matter? leveraging sentence level representations for

Original: Cucurull
Revised: November

Original: Arantxa Casanova
Revised: Arantxa Yoshua

Original: Adriana Romero Liò
Revised: Representations ICLR

Original: Yoshua Bengio
Revised: Vancouver BC Canada

Original:  David Wadden, Shanchuan Lin, Kyle Lo, Lucy Lu
Revised: April May 3, 2018, Conference Track Proceedings . OpenReview.net. David Wadden, Shanchuan Lin, Kyle Lo, Lucy Lu

Original: Wang, Madeleine van Zuylen, Arman Cohan and
Revised: Wang van Zuylen Arman Cohan, and

Original: Hannaneh Hajishirzi.
Revised: Hannaneh Hajishirzi guage

Revised: EMNLP

Original: Linguistics.
Revised: Online Novem ber pages William 55th Annual Meeting of the Association for Computational Linguistics, ACL

Revised: Vancouver, Canada, July 30 - August 4, Volume 2:

Revised: Short Papers , pages 422–426.

Revised: Association for Computational Linguistics.

Original: Haoyang Wen, Ying Lin, Tuan Lai, Xiaoman Pan, Sha
Revised: Haoyang Wen, Ying Lin, Tuan Lai, Xiaoman Pan, Sha

Original: Li, Xudong Lin, Ben Zhou, Manling Li, Haoyu
Revised: Li, Xudong Lin, Ben Zhou, Manling Li, Haoyu

Original: Wang, Hongming Zhang, Xiaodong Yu, Alexander
Revised: Wang, Hongming Zhang, Xiaodong Yu, Alexander

Original: Dong, Zhenhailong Wang, Yi Fung, Piyush Mishra,
Revised: Dong, Zhenhailong Wang, Yi Fung, Piyush Mishra,

Original: Qing Lyu, Dídac Surís, Brian Chen, Susan Windisch
Revised: Qing Lyu, Dídac Surís, Brian Chen, Susan Windisch

Original: Brown, MarthaPalmer, Chris Callison-Burch, Carl 
Revised: Martha Chris Callison-Burch, Carl Vondrick Shih

Revised: Fu

Original: Vondrick, Jiawei Han, Dan Roth, Shih-Fu Chang, and Heng Ji
Revised: Association for Computational Linguistics: Human Language Technologies: Demonstrations, NAACL-HLT 2021, Online, June 611, 2021 , pages 133–143. Association for Computational Linguistics. Michihiro Yasunaga, Hongyu Ren, Antoine Bosselut, Percy Liang, and Jure Leskovec. 2021.

Revised: QAGNN: reasoning with language models and knowledge graphs for question answering.

Revised: In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics:

Revised: Human Language Technologies, NAACL-HLT 2021,

Revised: Online, June 6-11, 2021 , pages 535–546.

Revised: Association for Computational Linguistics.

Revised: Rowan Zellers, Ari Holtzman, Hannah Rashkin,

Original: cember 8-14, 2019, Vancouver, BC, Canada , pages 9051–9062.
Revised: Yonatan Bisk, Ali Farhadi, Franziska Roesner, and Yejin Choi. 2019. Defending against neural fake news. In Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada , pages 9051–9062.

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. [Table] Crisis

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

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

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.

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

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).

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

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

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

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

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

Revised: The model is released by Wen et al.

Revised: and achieves 92.

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

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.

Revised: B. Event Coreference Resolution

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

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

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

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

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

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.

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

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

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

Revised: The model achieves 84.8 CoNLL score on

Revised: ACE 2005.

Revised: B. KG-to-Text Generator

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

Revised: We ﬁrst linearize the IE-based KG.

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] ”.

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

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

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

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

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

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 -layer 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 # Cluster # Doc # Doc per cluster IED
Revised: To initialize the node and edge embeddings, we use bert-base-uncased model with the feature dimension of 768 .

Original: TL

Original: Crisis   dimension of 768 .

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

Original: For hyperparameters, we use a batch sizeof 16 , and search the learning rate from{ 10 − 3 , 10 − 4 , 10 − 5 } and the number of layerswithin { 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 hyperparametersare a learning rate of 10 − 5 and a number of layerof 4 .
Revised: Our best-found hyperparameters are a learning rate of 10 − 5 and a number of layer of 4 .

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

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

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

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.

Revised: on the three datasets respectively.

Revised: We train the model on a Tesla P

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-leveldetection, we compare our method against twobaselines:
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: For
Revised: We train the model on our datasets using their default hyper-parameters.

Original:  GROVER, we use the implementation at https://github.com/rowanz/grover and experiment with two settings, medium setting and megasetting.
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 iscomputationally expensive, we use GROVER inthe 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 twoheuristic baselines, random guessing and logisticregression.
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 theevent is false.
Revised: 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.

Original: In logistic regression, we use thefollowing features: event type (represented by onehot feature), number of arguments, and number ofcoreferential events.
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 normalizedon the training set.
Revised: The features are normalized on the training set.

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

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.

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

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

Original: We present two examples of generated fake newsin 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: Thegenerated fake news conveys the manipulated misinformation 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: D 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 dataset and software with licenses compatible with the original access conditions.