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: Anonymous authors

Original: Paper under double-blind review

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 in 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: 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: Context-aware neural language model and a large credibility-focused Twitter corpus are employed to learn effective representations of rumor tweets forour semantic relatedness measurement method.
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 ﬁne-tuned with the  large domain-speciﬁc corpus shows a dramatic improvement on training data augmentation for rumor detection over pre-trained 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 6 different real-world events based on 5 publicly available datasets and 1 augmented dataset.
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 large amount of 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 modelfor rumor detection 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  ML methodsfor rumor studies is that they require a vast amount of labeled data to be trained. However, the manual annotation of large-scale and noisy social media data for rumors is highly labor-intensive 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 challengefacing of studying rumors in 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 datasets 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 thatvariations of rumors  share similar propagation patterns  (Maddock et al., 2015; Chen et al., 2018; Baxter 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; Chen et al., 2018; Baxter et al., 2015; Friggeri et al., 2014).

Original: Based on this hypothesis, we argue that enriching existing labeledrumor data with duplicated tweets  or corresponding variations 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: 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 pseudo-labels to unlabeled tweets.
Revised: Pairwise similarity is used to guide the assignment of pseudolabels to unlabeled tweets.

Original: ELMo (Peters et al., 2018), a state-of-the-art 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 shows 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 presentthe data augmentation results for threereal-world events and the performance of a state-of-the-art 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 datasets 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 dataset is employed in our semantic relatedness method in order to ﬁne-tunea optimum relatedness threshold througha pairwise comparison between  labeled reference and unlabeled candidatessource tweets embeddings (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 dataset (6392078) (Kochkina et al., 2018a)
Revised: PHEME data set (Kochkina et al., 2018a)

Original: This is an extension of the PHEMEdataset of rumors and non-rumors and contains 9 manually labeled rumor event data set. This data  is used asa reference dataset for data augmentation.
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 dataset 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: A subset of data is manually labeled based on informativeness, information types, and information sources.

Original: This dataset is used asa reference dataset on which data augmentation is performed.
Revised: “2013 Boston bombings” data from this data set is used as reference set in this experiment.

Original: Twitter event datasets (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. We use this data as a pool of candidate 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: Among the 30 events, we choose six events for which we can generate references using publicly available datasets and resources: ‘Ferguson unrest’, ‘Sydney siege’, ‘Ottawa shootng’, ‘Charliehebdo’, ‘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  ﬁve events except the ‘Boston marathon bombings’ as ‘PHEME5’ .
Revised: We refer to the ﬁrst ﬁve events with reference set generated from PHEME data as ‘PHEME5’ .

Original: Although the ‘Boston bombings’ event is not available in PHEME data, it is suitable for our task as references can be built using CrisisLexT26 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 data comprises more than 60M tweets grouped into 1049real-world 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: Thislarge corpus is leveraged to ﬁne-tune ELMomodel in order to provide better representations for rumorrelated tasks (refer to 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  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).

Revised: References are generated separately for

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

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

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

Original: The leftmost box shows how semantic similarity  between a given pair of reference and candidate tweets is computed.
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  domain-speciﬁc corpusin order 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 pre-processing.
Revised: Given corpora that contain pairs of tweets, we apply language-based ﬁltering and perform linguistic preprocessing.

Original: The pre-processing includes converting words in lowercase, removing ’rt @’, URLs, and non-alphabetic characters, and tokenization.
Revised: The preprocessing includes lowercasing, removing ‘rt @’, URLs 2 , and non-alphabetic characters, and tokenization.

Original: Tweets with a minimum of 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  is used to compare each tweet pair.
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 forﬁne-tuning relatedness thresholds (see Section 4).
Revised: We use SemEval-2015 task 1 data set as a benchmark for relatedness threshold ﬁne-tuning (see Section 4).

Original: Havingan optimum threshold, we follow the same relatedness measurement procedure to compute semantic similarity  for each pair of reference and candidate tweets in the Twitter event datasets (2012-2016) data. Candidate tweets refer to any tweets that report about an event of interest. Schemes for constructing references are varying between data sets. For PHEME5 , we use annotations in the PHEME data.
Revised: Having optimum thresholds, semantic similarity computation is performed for reference-candidate pairs.

Original: A reference dataset of “Boston marathon bombings” event is generated separately as mentioned in Section 2.

Original: Due to space constraints, we omit the detailed process here.

Original: The next step is to select rumors and non-rumors from  candidate tweets based on the optimum relatedness threshold.
Revised: Rumor and non-rumor source tweets are selected from the candidate pool using the ﬁne-tuned thresholds.

Original: In the ﬁnal step, data collection 2 is performed to retrieve social-temporal context data (typically retweets and replies) for  selected candidate tweets.
Revised: In the ﬁnal step, data collection 3 is performed to retrieve social-temporal context data (typically retweets and replies) for the selected source tweets.

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

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

Original: For  CREDBANK , we downloaded 77,954, 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, 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]

Original: [Figure]

Original: [Table]

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

Original: Sentences are shufﬂed and spitted into training and holdout sets (with proportion 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  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.

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

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

Original: Since the CREDBANK training set is still a relatively smalldataset for NLM, we only ﬁne-tune pre-trained 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-outset and test set (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 achieved.
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 pre-trained model (“Original (5.5B)”) and state-of-the-art 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.

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

Original: Relatedness thresholds are ﬁnetuned 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: [Table] Data Augmentation Table 4 shows the number of candidate source tweets and references after pre-processing and ﬁltering out empty strings.
Revised: We highlight some statistics in Tabel 4.

Original:  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  pairwise similarity computation on all datasets, we adopt 0.8 , which achieves aprecision of 0.9 in the benchmark task illustrated above, as relatedness threshold for selecting 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 context data are  removed.
Revised: Sampling more negative examples is an attempt to balance class distributions after source tweets without contexts are [Table] [Table] removed.

Original: This is due to the fact that non-rumors generally draw lessattention, and therefore, it is highly likely that they do not have replies or retweets.
Revised: This is based on a hypothesis that non-rumors are less likely to have reactions than rumors as they generally draw less attention.

Original: [Table]
Revised: In terms of computational performance, ELMo embedding computation and semantic relatedness measurement are performed with CPU. 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: [Table] 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 and retweets, 1,238 rumors and 3,714 non-rumors are collected for “bostonbombings”.

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

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

Original: Total 25,486 rumors and 76,106 non-rumors areobtained additionally  for ‘sydneysiege’, and  total  21,519 rumors and 62,590 non-rumors areobtained additionally  for ‘ottawashooting’.
Revised: A total of 25,486 rumors and 76,106 non-rumors are additionally obtained for “sydneysiege”, and a total of 21,519 rumors and 62,590 non-rumors are additionally obtained for “ottawashooting”. [Table]

Original: Rumor Detection We conduct rumor detection experiments using two different datasets: (1) PHEME5 , (2) PHEME5 with ’bostonbombings’ data (‘PHEME5+Boston’).
Revised: Rumor Detection We conduct rumor detection experiments using two different data sets: [Equation] PHEME5 , (2) PHEME5 with the “bostonbombings” data (“PHEME5+Boston”).

Original: We employ Kochkina et al. ’s method as state-of-the-art baseline model of rumor detection with slight modiﬁcations (Kochkina et al., 2018b).
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 tweets 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  on the PHEME5 and augmented datasets.
Revised: We perform leave-one-out cross-validation (LOOCV) on the PHEME5 and augmented data sets.

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

Original: It shows the performance of rumor detection for each event in the PHEME5 and augmented datasets as well as for the entire datasets.
Revised: Table 6 shows LOOCV results.

Original: When using the augmented data, we observe that overall performance decreases compared to when using the PHEME data.
Revised: We observe that overall performance decreases with the augmented data (i [Table] e., PHEME5+Boston).

Original: The ’ferguson’ 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).
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) [Table]

Original: It is worth noting that our data augmentation improves the performance of rumor detection on the ‘ferguson’.
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. [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 potentials of DNNstechniques in a wide range of tasks related to rumors in 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: Current study showed the potential efﬁciency and effectiveness of semantically augmented data for combating the labeled data scarcity and class imbalance problems ofexisting publicly available rumor datasets.
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 datasets 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.