Original: D ATA AUGMENTATION FOR RUMOR DETECTION USING CONTEXT - SENSITIVE NEURAL LANGUAGE MODEL WITH LARGE - SCALE CREDIBILITY CORPUS
Revised: D ATA AUGMENTATION FOR RUMOR DETECTION USING CONTEXT - SENSITIVE NEURAL LANGUAGE MODEL WITH LARGE - SCALE CREDIBILITY CORPUS

Original: A BSTRACT
Revised: A BSTRACT

Original: In this paper, we address the challenge of limited labeled data and class imbalance problem for machine learning-based rumor detection on social media.
Revised: In this paper, we address the challenge of limited labeled data and class imbalance problem for machine learning-based rumor detection on social media.

Original: We present an ofﬂine data augmentation method based on semantic relatedness for rumor detection.
Revised: We present an ofﬂine data augmentation method based on semantic relatedness for rumor detection.

Original: To this end, unlabeled social media data is exploited to augment limited labeled data.
Revised: To this end, unlabeled social media data is exploited to augment limited labeled data.

Original: A context-aware neural language model and a large credibility-focused Twitter corpus are employed to learn effective representations of rumor tweets for semantic relatedness measurement.
Revised: A context-aware neural language model and a large credibility-focused Twitter corpus are employed to learn effective representations of rumor tweets for semantic relatedness measurement.

Original: A language model ﬁnetuned with the a large domain-speciﬁc corpus shows a dramatic improvement on training data augmentation for rumor detection over pretrained language models.
Revised: A language model ﬁnetuned with the a large domain-speciﬁc corpus shows a dramatic improvement on training data augmentation for rumor detection over pretrained language models.

Original: We conduct experiments on six different real-world events based on ﬁve publicly available data sets and one augmented data set.
Revised: We conduct experiments on six different real-world events based on ﬁve publicly available data sets and one augmented data set.

Original: Our experiments show that the proposed method allows us to generate a larger training data with reasonable quality via weak supervision.
Revised: Our experiments show that the proposed method allows us to generate a larger training data with reasonable quality via weak supervision.

Original: We present preliminary results achieved using a state-of-the-art neural network model with augmented data for rumor detection.
Revised: We present preliminary results achieved using a state-of-the-art neural network model with augmented data for rumor detection.

Original:  I NTRODUCTION
Revised:  I NTRODUCTION

Original: Research areas that have recently been received much attention in using Machine Learning (ML) and Natural Language Processing for automated rumor and fake news detection (Helmstetter &
Revised: Research areas that have recently been received much attention in using Machine Learning (ML) and Natural Language Processing for automated rumor and fake news detection (Helmstetter &

Original: Paulheim, 2018; Kwon et al., 2017; Shu et al., 2017; 2018; Wong et al., 2018) and fact-checking (Boididou et al., 2018; Vosoughi et al., 2017; Kochkina et al., 2018b).
Revised: Paulheim, 2018; Kwon et al., 2017; Shu et al., 2017; 2018; Wong et al., 2018) and fact-checking (Boididou et al., 2018; Vosoughi et al., 2017; Kochkina et al., 2018b).

Original: One major bottleneck of state-of-the-art (SoA) ML methods is that they require a vast amount of labeled data to be trained and manual labeling of rumors source on social media requires special skills and time-consuming (Zubiaga et al., 2016).
Revised: One major bottleneck of state-of-the-art (SoA) ML methods is that they require a vast amount of labeled data to be trained and manual labeling of rumors source on social media requires special skills and time-consuming (Zubiaga et al., 2016).

Original: Due to limited labeled training data, existing neural networks (NNs) for rumor detection usually have shallow architecture (Chen et al., 2018; Ma et al., 2016).
Revised: Due to limited labeled training data, existing neural networks (NNs) for rumor detection usually have shallow architecture (Chen et al., 2018; Ma et al., 2016).

Original: The scarcity of labeled data is a major challenge of studying rumors on social media (Aker et al., 2017).
Revised: The scarcity of labeled data is a major challenge of studying rumors on social media (Aker et al., 2017).

Original: Another problem is that publicly available data sets for rumor-related tasks such as PHEME data (Kochkina et al., 2018b) suffer from imbalanced class distributions (Liu et al., 2017).
Revised: Another problem is that publicly available data sets for rumor-related tasks such as PHEME data (Kochkina et al., 2018b) suffer from imbalanced class distributions (Liu et al., 2017).

Original: Existing methods for handling the class imbalance problem (e.g., oversampling and the use of synthetic data (Xu & Chen, 2015)) may cause over-ﬁtting and poor generalization performance.
Revised: Existing methods for handling the class imbalance problem (e.g., oversampling and the use of synthetic data (Xu & Chen, 2015)) may cause over-ﬁtting and poor generalization performance.

Original: A methodology for rumor data augmentation with the minimum of human supervision is necessary.
Revised: A methodology for rumor data augmentation with the minimum of human supervision is necessary.

Original: Previous studies presented that rumors can evolve into many variants which share similar propagation patterns in their early stage (Maddock et al., 2015;
Revised: Previous studies presented that rumors can evolve into many variants which share similar propagation patterns in their early stage (Maddock et al., 2015;

Original: Chen et al., 2018; Baxter et al., 2015; Friggeri et al., 2014).
Revised: Chen et al., 2018; Baxter et al., 2015; Friggeri et al., 2014).

Original: Based on this hypothesis, we argue that enriching existing labeled data with unlabeled source tweets conveying the same or similar meanings is a promising attempt for rumor detection methods that rely on the structure of rumor propagation in social media.
Revised: Based on this hypothesis, we argue that enriching existing labeled data with unlabeled source tweets conveying the same or similar meanings is a promising attempt for rumor detection methods that rely on the structure of rumor propagation in social media.

Original: In this work, we propose a novel data augmentation method for automatic rumor detection based on semantic relatedness.
Revised: In this work, we propose a novel data augmentation method for automatic rumor detection based on semantic relatedness.

Original: We exploit a publicly available paraphrase identiﬁcation corpus as well as context-sensitive embeddings of labeled references and unlabeled candidate source tweets.
Revised: We exploit a publicly available paraphrase identiﬁcation corpus as well as context-sensitive embeddings of labeled references and unlabeled candidate source tweets.

Original: Pairwise similarity is used to guide the assignment of pseudolabels to unlabeled tweets.
Revised: Pairwise similarity is used to guide the assignment of pseudolabels to unlabeled tweets.

Original: ELMo (Peters et al., 2018), a SoA context-sensitive neural language model (NLM), is ﬁne-tuned on a large credibility-focused social media corpus and used to encode tweets.
Revised: ELMo (Peters et al., 2018), a SoA context-sensitive neural language model (NLM), is ﬁne-tuned on a large credibility-focused social media corpus and used to encode tweets.

Original: Our results show that data augmentation can contribute to rumor detection with deep learning with increased training data size and a reasonable level of quality.
Revised: Our results show that data augmentation can contribute to rumor detection with deep learning with increased training data size and a reasonable level of quality.

Original: This has potential for further performance improvements using deeper NNs.
Revised: This has potential for further performance improvements using deeper NNs.

Original: We present data augmentation results for three events and the performance of a SoA DNN model for rumor detection with augmented data in Section 5.
Revised: We present data augmentation results for three events and the performance of a SoA DNN model for rumor detection with augmented data in Section 5.

Original:  D ATA
Revised:  D ATA

Original: Four publicly available data sets covering a wide range of real-world events on social media as well as a Twitter paraphrase corpus are used for this project.
Revised: Four publicly available data sets covering a wide range of real-world events on social media as well as a Twitter paraphrase corpus are used for this project.

Original: SemEval-2015 task 1 data (Xu et al., 2014)
Revised: SemEval-2015 task 1 data (Xu et al., 2014)

Original: This data is built for paraphrase identiﬁcation and semantic similarity measurement.
Revised: This data is built for paraphrase identiﬁcation and semantic similarity measurement.

Original: This data set is employed in our semantic relatedness method in order to ﬁne-tune optimum relatedness thresholds through pairwise comparisons between the tweet embeddings of labeled reference and unlabeled candidates (see details in Section 4).
Revised: This data set is employed in our semantic relatedness method in order to ﬁne-tune optimum relatedness thresholds through pairwise comparisons between the tweet embeddings of labeled reference and unlabeled candidates (see details in Section 4).

Original: PHEME data set (Kochkina et al., 2018a)
Revised: PHEME data set (Kochkina et al., 2018a)

Original: The latest PHEME data (6392078) is used as reference set for data augmentation covering 9 manually labeled rumor events.
Revised: The latest PHEME data (6392078) is used as reference set for data augmentation covering 9 manually labeled rumor events.

Original: CrisisLexT26 (Olteanu et al., 2015)
Revised: CrisisLexT26 (Olteanu et al., 2015)

Original: This data comprises tweets associated with 26 hazardous events happened between 2012 and 2013.
Revised: This data comprises tweets associated with 26 hazardous events happened between 2012 and 2013.

Original: “2013 Boston bombings” data from this data set is used as reference set in this experiment.
Revised: “2013 Boston bombings” data from this data set is used as reference set in this experiment.

Original: Twitter event data (2012-2016)
Revised: Twitter event data (2012-2016)

Original: (Zubiaga, 2018)
Revised: (Zubiaga, 2018)

Original: This data consists of over 147 million tweets associated with 30 real-world events unfolded between February 2012 and May 2016, among which six events are selected as a pool of candidates source tweets.
Revised: This data consists of over 147 million tweets associated with 30 real-world events unfolded between February 2012 and May 2016, among which six events are selected as a pool of candidates source tweets.

Original: This covers ‘Ferguson unrest’, ‘Sydney siege’, ‘Ottawa shooting’, ‘Charliehebdo attacks’, ‘Germanwings plane crash’, and ‘Boston marathon bombings’.
Revised: This covers ‘Ferguson unrest’, ‘Sydney siege’, ‘Ottawa shooting’, ‘Charliehebdo attacks’, ‘Germanwings plane crash’, and ‘Boston marathon bombings’.

Original: We refer to the ﬁrst ﬁve events with reference set generated from PHEME data as ‘PHEME5’ .
Revised: We refer to the ﬁrst ﬁve events with reference set generated from PHEME data as ‘PHEME5’ .

Original: For the ‘Boston bombings’ event, we generate references from CrisisLexT and a fact-checking website ‘Snopes.com’ 1 (refer to Section 3).
Revised: For the ‘Boston bombings’ event, we generate references from CrisisLexT and a fact-checking website ‘Snopes.com’ 1 (refer to Section 3).

Original: CREDBANK (Mitra & Gilbert, 2015)
Revised: CREDBANK (Mitra & Gilbert, 2015)

Original: This large corpus comprises more than 60M tweets grouped into 1049 events, each of which were manually annotated with credibility ratings.
Revised: This large corpus comprises more than 60M tweets grouped into 1049 events, each of which were manually annotated with credibility ratings.

Original: This is leveraged to ﬁne-tune ELMo in order to provide better representations for rumor-related tasks (see Section 3).
Revised: This is leveraged to ﬁne-tune ELMo in order to provide better representations for rumor-related tasks (see Section 3).

Original:  M ETHOD
Revised:  M ETHOD

Original: An overview of our data augmentation method is presented in Figure 1.
Revised: An overview of our data augmentation method is presented in Figure 1.

Original: We exploit a limited amount of labeled data as weak supervision (i.e., references).
Revised: We exploit a limited amount of labeled data as weak supervision (i.e., references).

Original: References are generated separately for
Revised: References are generated separately for

Original: PHEME5 and “Boston bombings” data from different data with varying annotations schemes (as mentioned in Section 2).
Revised: PHEME5 and “Boston bombings” data from different data with varying annotations schemes (as mentioned in Section 2).

Original: Due to space constraints, we omit the detailed process of reference generation here.
Revised: Due to space constraints, we omit the detailed process of reference generation here.

Original: Candidate tweets refer to any tweets that report an event of interest.
Revised: Candidate tweets refer to any tweets that report an event of interest.

Original: The leftmost box shows how semantic similarity is computed between a given pair of reference and candidate tweets.
Revised: The leftmost box shows how semantic similarity is computed between a given pair of reference and candidate tweets.

Original: Firstly, the contextual embedding model (ELMo) is ﬁne-tuned with a domain-speciﬁc corpus to learn representations of rumors.
Revised: Firstly, the contextual embedding model (ELMo) is ﬁne-tuned with a domain-speciﬁc corpus to learn representations of rumors.

Original: Given corpora that contain pairs of tweets, we apply language-based ﬁltering and perform linguistic preprocessing.
Revised: Given corpora that contain pairs of tweets, we apply language-based ﬁltering and perform linguistic preprocessing.

Original: The preprocessing includes lowercasing, removing ‘rt @’, URLs 2 , and non-alphabetic characters, and tokenization.
Revised: The preprocessing includes lowercasing, removing ‘rt @’, URLs 2 , and non-alphabetic characters, and tokenization.

Original: Tweets with at least 4 tokens are considered to reduce noise (Ifrim et al., 2014).
Revised: Tweets with at least 4 tokens are considered to reduce noise (Ifrim et al., 2014).

Original: Then, we compute ELMo embeddings of tweets for subsequent semantic relatedness measurement.
Revised: Then, we compute ELMo embeddings of tweets for subsequent semantic relatedness measurement.

Original: Cosine similarity between each embeding pair is used as a relatedness measure.
Revised: Cosine similarity between each embeding pair is used as a relatedness measure.

Original: We use SemEval-2015 task 1 data set as a benchmark for relatedness threshold ﬁne-tuning (see Section 4).
Revised: We use SemEval-2015 task 1 data set as a benchmark for relatedness threshold ﬁne-tuning (see Section 4).

Original: Having optimum thresholds, semantic similarity computation is performed for reference-candidate pairs.
Revised: Having optimum thresholds, semantic similarity computation is performed for reference-candidate pairs.

Original: Rumor and non-rumor source tweets are selected from the candidate pool using the ﬁne-tuned thresholds.
Revised: Rumor and non-rumor source tweets are selected from the candidate pool using the ﬁne-tuned thresholds.

Original: In the ﬁnal step, data collection3 is performed to retrieve social-temporal context data (typically retweets and replies) for the selected source tweets.
Revised: In the ﬁnal step, data collection is performed to retrieve social-temporal context data (typically retweets and replies) for the selected source tweets.

Original: Source tweets without contexts are ﬁltered out.
Revised: Source tweets without contexts are ﬁltered out.

Original: We download source tweets for six selected events in the Twitter event (2012-2016) and CREDBANK using Twitter API 4 .
Revised: We download source tweets for six selected events in the Twitter event (2012-2016) and CREDBANK using Twitter API 3 .

Original: For the CREDBANK , we downloaded 77,954,446 tweets (i.e., 97.1% of the original data).
Revised: For the CREDBANK , we downloaded 77,954,446 tweets (i.e., 97.1% of the original data).

Original: After deduplication, the train corpus contains 6,157,180 tweets with 146,340,647 tokens and 2,235,075 vocabularies.
Revised: After deduplication, the train corpus contains 6,157,180 tweets with 146,340,647 tokens and 2,235,075 vocabularies.

Original: [Figure]
Revised: [Figure]

Revised: [Figure]

Revised: [Table]

Revised: [Figure]

Original: [Table]
Revised: [Table]

Original: Rumor-speciﬁc Embedding (ELMo)
Revised: Rumor-speciﬁc Embedding (ELMo)

Original: Previous research shows that ﬁne-tuning NLMs with in-domain data allows them to learn more meaningful word representations and provides a performance gain (Kim, 2014; Peters et al., 2018).
Revised: Previous research shows that ﬁne-tuning NLMs with in-domain data allows them to learn more meaningful word representations and provides a performance gain (Kim, 2014; Peters et al., 2018).

Original: To ﬁne-tune a pretrained ELMo, we generate a data set using the CREDBANK .
Revised: To ﬁne-tune a pretrained ELMo, we generate a data set using the CREDBANK .

Original: Sentences are shufﬂed and spit into training and hold-out sets (with a ratio of around 0.02%).
Revised: Sentences are shufﬂed and spit into training and hold-out sets (with a ratio of around 0.02%).

Original: We also generate a test set that consists of 6,162 tweets in total using the PHEME data.
Revised: We also generate a test set that consists of 6,162 tweets in total using the PHEME data.

Original: Table 1 shows the number of tweets, tokens and vocabularies in the CREDBANK after deduplication.
Revised: Table 1 shows the number of tweets, tokens and vocabularies in the CREDBANK after deduplication.

Original: Following the practice in (Perone et al., 2018), a linear combination of the states of each LSTM layer and the token embeddings is adopted to encode tweets.
Revised: Following the practice in (Perone et al., 2018), a linear combination of the states of each LSTM layer and the token embeddings is adopted to encode tweets.

Original: Training corpus are split into small batches with a maximum of 5000 tweets for each batch.
Revised: The training corpus is split into small batches with a maximum of 5000 tweets for each batch.

Original: Training time took more than 800 hours on a NVIDIA Kepler K40M GPU with less than 10 GiB GPU memory.
Revised: Training time took more thanhours on a NVIDIA Kepler K40M GPU with less than 10 GiB GPU memory.

Original: Since the CREDBANK training set is still a relatively small for NLMs, we only ﬁne-tune a pretrained ELMo with 1 epoch to avoid over-ﬁtting.
Revised: Since the CREDBANK training set is still a relatively small for NLMs, we only ﬁne-tune a pretrained ELMo with 1 epoch to avoid over-ﬁtting.

Original: The result shows a large improvement in perplexity on both hold-out and test sets (See Table 2).
Revised: The result shows a large improvement in perplexity on both hold-out and test sets (See Table 2).

Original: [Table]
Revised: [Table]

Original:  E XPERIMENTAL S ETUP
Revised:  E XPERIMENTAL S ETUP

Original: Semantic Relatedness Fine-Tuning Table 3 compares different models for word representation on the SemEval-2015 data .
Revised: Semantic Relatedness Fine-Tuning Table 3 compares different models for word representation on the SemEval-2015 data .

Original: We show the results based on the maximum F-score each model achieves.
Revised: We show the results based on the maximum F-score each model achieves.

Original: It shows the effectiveness of our CREDBANK ﬁne-tuned ELMo over a pretrained ELMo (“Original (5.5B)”) and other SoA word embedding models.
Revised: It shows the effectiveness of our CREDBANK ﬁne-tuned ELMo over a pretrained ELMo (“Original (5.5B)”) and other SoA word embedding models.

Original: To ensure higher quality, we argue that a higher precision is required.
Revised: To ensure higher quality, we argue that a higher precision is required.

Original: Therefore, relatedness thresholds are ﬁne-tuned based on precision achieved by the best-performing model.
Revised: Therefore, relatedness thresholds are ﬁne-tuned based on precision achieved by the best-performing model.

Original: We highlight some statistics in Tabel 4.
Revised: We highlight some statistics in Tabel [Table]

Original: Data Augmentation We follow our data augmentation procedure described in Section 3.
Revised: Data Augmentation We follow our data augmentation procedure described in Section 3.

Original: After performing pairwise similarity computation, a relatedness threshold ( 0.8 ) is adopted to select rumor source tweets from a pool of candidates.
Revised: After performing pairwise similarity computation, a relatedness threshold ( 0.8 ) is adopted to select rumor source tweets from a pool of candidates.

Original: We randomly sample 3*(# of rumor source tweets) non-rumor source tweets if the score of a candidate tweet is less than 0.3.
Revised: We randomly sample 3*(# of rumor source tweets) non-rumor source tweets if the score of a candidate tweet is less than 0.3.

Original: Sampling more negative examples is an attempt to balance class distributions after source tweets without contexts are 
Revised: Sampling more negative examples is an attempt to balance class distributions after source tweets without contexts are removed.

Revised: This is based on a hypothesis that non-rumors are less likely to have reactions than rumors

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

Original: removed. This is based on a hypothesis that non-rumors are less likely to have reactions than rumors as they generally draw less attention.
Revised: as they generally draw less attention.

Original: In terms of computational performance, ELMo embedding computation and semantic relatedness measurement are performed with CPU.
Revised: In terms of computational performance, ELMo embedding computation and semantic relatedness measurement are performed with CPU.

Original: Tweet encoding takes around 10 per second and pairwise comparison takes around 869 pairs per second on average.
Revised: Tweet encoding takes around 10 per second and pairwise comparison takes around 869 pairs per second on average.

Original:  R ESULTS AND D ISCUSSION
Revised:  R ESULTS AND D ISCUSSION

Original: Data Augmentation Before ﬁltering out source tweets without replies and retweets, 1,238 rumors and 3,714 non-rumors are collected for “bostonbombings”.
Revised: Data Augmentation Before ﬁltering out source tweets without replies, 1,238 rumors and 3, non-rumors are collected for “bostonbombings”.

Original: After ﬁltering, 165 rumors andnon-rumors remain.
Revised: After ﬁltering, 165 rumors and 228 non-rumors remain.

Original: Although the augmented data size is very limited for “bostonbombings”, experiments on “sydneysiege” and “ottawashooting” show encouraging results.
Revised: Although the augmented data size is very limited for “bostonbombings”, experiments on “sydneysiege” and “ottawashooting” show encouraging results.

Original: A total of 25,486 rumors and 76,106 non-rumors are additionally obtained for “sydneysiege”, anda total of 21,519 rumors and 62,590 non-rumors are additionally obtained for “ottawashooting”.
Revised: A total of 25,486 rumors and 76, non-rumors are additionally obtained for “sydneysiege”, and 21,519 rumors and 62,590 non-rumors are additionally obtained for “ottawashooting”.

Revised: We make our augmented data publicly available 4 .

Original: [Table]
Revised: [Table]

Original: Rumor Detection We conduct rumor detection experiments using two different data sets:
Revised: Rumor Detection We conduct rumor detection experiments using two different data sets:

Original: [Equation]
Revised: [Equation]

Original: PHEME5 , (2) PHEME5 with the “bostonbombings” data (“PHEME5+Boston”).
Revised: PHEME5 , [Equation] 2) PHEME5 with the “bostonbombings” data (“PHEME5+Boston”).

Original: We employ (Kochkina et al., 2018b)’s method as a SoA baseline model for rumor detection with slight modiﬁcations.
Revised: We employ (Kochkina et al., 2018b)’s method as a SoA baseline model for rumor detection with slight modiﬁcations.

Original: For the sake of simplicity, we modify the implementation of “MTL2 Veracity+Detection” for rumor detection only.
Revised: For the sake of simplicity, we modify the implementation of “MTL2 Veracity+Detection” for rumor detection only.

Original: We construct input by using a source tweet and the top (i.e., most recent) 24 replies in this task.
Revised: We construct input by using a source tweet and the top (i.e., most recent) 24 replies in this task.

Original: We perform leave-one-out cross-validation (LOOCV) on the PHEME5 and augmented data sets.
Revised: We perform leave-one-out cross-validation (LOOCV) on the PHEME5 and augmented data sets.

Original: The overall experimental results for rumor detection are presented in Table 5.
Revised: The overall experimental results for rumor detection are presented in Table 5.

Original: Table 6 shows
Revised: Table 6 shows

Original: LOOCV results.
Revised: LOOCV results.

Original: We observe that overall performance decreases with the augmented data (i [Table] e.,
Revised: We observe that overall performance decreases with the augmented data (i.e.,

Original: PHEME5+Boston).
Revised: PHEME5+Boston).

Original: The “fergusonunrest” is the most difﬁcult event for a rumor detection model as it has a unique class distribution distinguished from all other events (Kochkina et al., 2018b) [Table]
Revised: The “fergusonunrest” is the most difﬁcult event for a rumor detection model as it has a unique class distribution distinguished from all other events (Kochkina et al., 2018b).

Original: It is worth noting that our data augmentation improves the performance of rumor detection on the “fergusonunrest”.
Revised: It is worth noting that our data augmentation improves the performance of rumor detection on the “fergusonunrest”.

Original: The completion of data augmentation for events other than “’bostonbombings” has potential to boost overall and per event performance of rumor detection.
Revised: The completion of data augmentation for events other than “’bostonbombings” has potential to boost overall and per event performance of rumor detection.

Original: [Table]
Revised: [Table]

Original:  C ONCLUSION AND F UTURE W ORK
Revised:  C ONCLUSION AND F UTURE W ORK

Original: We present a methodology of data augmentation for rumor detection that exploits semantic relatedness between limited labeled data and unlabeled data.
Revised: We present a methodology of data augmentation for rumor detection that exploits semantic relatedness between limited labeled data and unlabeled data.

Original: This study is part of further research that aims to use a massive amount of publicly available unlabeled Twitter data and the potential of DNNs in a wide range of tasks related to rumors on social media.
Revised: This study is part of further research that aims to use a massive amount of publicly available unlabeled Twitter data and the potential of DNNs in a wide range of tasks related to rumors on social media.

Original: Our current research has demonstrated the potential efﬁciency and effectiveness of semantically augmented data in combating the labeled data scarcity and class imbalance problems of publicly available rumor data sets.
Revised: Our current research has demonstrated the potential efﬁciency and effectiveness of semantically augmented data in combating the labeled data scarcity and class imbalance problems of publicly available rumor data sets.

Original: In future work, we plan to augment data for more events to build comprehensive data sets for rumor detection, and conduct experiments on rumor detection via deep learning.
Revised: In future work, we plan to augment data for more events to build comprehensive data sets for rumor detection, and conduct experiments on rumor detection via deep learning.

Original: We will evaluate the effectiveness of augmented data in alleviating over-ﬁtting and its usefulness in facilitating deeper NNs for rumor detection.
Revised: We will evaluate the effectiveness of augmented data in alleviating over-ﬁtting and its usefulness in facilitating deeper NNs for rumor detection.

Original: Further experiments will be conducted to examine the generalization of rumor detection models on unseen rumors.
Revised: Further experiments will be conducted to examine the generalization of rumor detection models on unseen rumors.