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In this paper, we address the challenge of limited labeled data and class imbalance problem for machine learning-based rumor detection on social media.
We present an offline data augmentation method based on semantic relatedness for rumor detection.
To this end, unlabeled social media data is exploited to augment limited labeled data.
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.
A language model fine-tuned with the a large domain-specific corpus shows a dramatic improvement on training data augmentation for rumor detection over pretrained language models.
We conduct experiments on six different real-world events based on five publicly available data sets and one augmented data set.
Our experiments show that the proposed method allows us to generate a larger training data with reasonable quality via weak supervision.
We present preliminary results achieved using a state-of-the-art neural network model with augmented data for rumor detection.
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 BID5 BID11 BID19 BID25 BID22 and fact-checking BID2 BID21 BID10 .
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 BID26 .
Due to limited labeled training data, existing neural networks (NNs) for rumor detection usually have shallow architecture BID3 BID13 .
The scarcity of labeled data is a major challenge of studying rumors on social media BID0 .
Another problem is that publicly available data sets for rumor-related tasks such as PHEME data BID10 suffer from imbalanced class distributions .
Existing methods for handling the class imbalance problem (e.g., oversampling and the use of synthetic data BID24 ) may cause over-fitting and poor generalization performance.
A methodology for rumor data augmentation with the minimum of human supervision is necessary.
Previous studies presented that rumors can evolve into many variants which share similar propagation patterns in their early stage BID14 BID3 BID1 BID4 .
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.
In this work, we propose a novel data augmentation method for automatic rumor detection based on semantic relatedness.
We exploit a publicly available paraphrase identification corpus as well as context-sensitive embeddings of labeled references and unlabeled candidate source tweets.
Pairwise similarity is used to guide the assignment of pseudolabels to unlabeled tweets.
ELMo BID18 , a SoA context-sensitive neural language model (NLM), is fine-tuned on a large credibility-focused social media corpus and used to encode tweets.
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.
This has potential for further performance improvements using deeper NNs.
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.
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.
SemEval-2015 task 1 data BID23 This data is built for paraphrase identification and semantic similarity measurement.
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).
PHEME data set BID8 The latest PHEME data (6392078) is used as reference set for data augmentation covering 9 manually labeled rumor events.
CrisisLexT26 BID16 This data comprises tweets associated with 26 hazardous events happened between 2012 and 2013.
"2013 Boston bombings" data from this data set is used as reference set in this experiment.
Twitter event data (2012-2016) BID25 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.
This covers 'Ferguson unrest', 'Sydney siege', 'Ottawa shooting', 'Charliehebdo attacks', 'Germanwings plane crash', and 'Boston marathon bombings'.
We refer to the first five events with reference set generated from PHEME data as 'PHEME5'.
For the 'Boston bombings' event, we generate references from CrisisLexT26 and a fact-checking website 'Snopes.com' 1 (refer to Section 3).
CREDBANK BID15 This large corpus comprises more than 60M tweets grouped into 1049 events, each of which were manually annotated with credibility ratings.
This is leveraged to fine-tune ELMo in order to provide better representations for rumor-related tasks (see Section 3).
An overview of our data augmentation method is presented in Figure 1 .
We exploit a limited amount of labeled data as weak supervision (i.e., references).
References are generated separately for PHEME5 and "Boston bombings" data from different data with varying annotations schemes (as mentioned in Section 2).
Due to space constraints, we omit the detailed process of reference generation here.
Candidate tweets refer to any tweets that report an event of interest.
The leftmost box shows how semantic similarity is computed between a given pair of reference and candidate tweets.
Firstly, the contextual embedding model (ELMo) is fine-tuned with a domain-specific corpus to learn representations of rumors.
Given corpora that contain pairs of tweets, we apply language-based filtering and perform linguistic preprocessing.
The preprocessing includes lowercasing, removing 'rt @', URLs 2 , and non-alphabetic characters, and tokenization.
Tweets with at least 4 tokens are considered to reduce noise BID6 .
Then, we compute ELMo embeddings of tweets for subsequent semantic relatedness measurement.
Cosine similarity between each embeding pair is used as a relatedness measure.
We use SemEval-2015 task 1 data set as a benchmark for relatedness threshold fine-tuning (see Section 4).
Having optimum thresholds, semantic similarity computation is performed for reference-candidate pairs.
Rumor and non-rumor source tweets are selected from the candidate pool using the fine-tuned thresholds.
In the final step, data collection is performed to retrieve social-temporal context data (typically retweets and replies) for the selected source tweets.
Source tweets without contexts are filtered out.
We download source tweets for six selected events in the Twitter event (2012) (2013) (2014) (2015) (2016) and CREDBANK using Twitter API 3 .
For the CREDBANK, we downloaded 77,954,446 tweets (i.e., 97.1% of the original data).
After deduplication, the train corpus contains 6,157,180 tweets with 146,340,647 tokens and 2,235,075 vocabularies.
Rumor-specific Embedding (ELMo) Previous research shows that fine-tuning NLMs with in-domain data allows them to learn more meaningful word representations and provides a performance gain BID7 BID18 .
To fine-tune a pretrained ELMo, we generate a data set using the CREDBANK.
Sentences are shuffled and spit into training and hold-out sets (with a ratio of around 0.02%).
We also generate a test set that consists of 6,162 tweets in total using the PHEME data.
TAB1 shows the number of tweets, tokens and vocabularies in the CREDBANK after deduplication.
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.
The training corpus is split into small batches with a maximum of 5000 tweets for each batch.
Training time took more than 800 hours on a NVIDIA Kepler K40M GPU with less than 10 GiB GPU memory.
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.
The result shows a large improvement in perplexity on both hold-out and test sets (See TAB2 ).
Semantic Relatedness Fine-Tuning TAB3 compares different models for word representation on the SemEval-2015 data.
We show the results based on the maximum F-score each model achieves.
It shows the effectiveness of our CREDBANK fine-tuned ELMo over a pretrained ELMo ("Original (5.5B)") and other SoA word embedding models.
To ensure higher quality, we argue that a higher precision is required.
Therefore, relatedness thresholds are fine-tuned based on precision achieved by the best-performing model.
We highlight some statistics in Tabel 4.Data Augmentation We follow our data augmentation procedure described in Section 3.
After performing pairwise similarity computation, a relatedness threshold (0.8) is adopted to select rumor source tweets from a pool of candidates.
We randomly sample 3*(# of rumor source tweets) non-rumor source tweets if the score of a candidate tweet is less than 0.3.
Sampling more negative examples is an attempt to balance class distributions after source tweets without contexts are removed.
This is based on a hypothesis that non-rumors are less likely to have reactions than rumors Table 4 : Results of fine-tuning thresholds based on precision.
as they generally draw less attention.
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.
Data Augmentation Before filtering out source tweets without replies, 1,238 rumors and 3,714 non-rumors are collected for "bostonbombings".
After filtering, 165 rumors and 228 non-rumors remain.
Although the augmented data size is very limited for "bostonbombings", experiments on "sydneysiege" and "ottawashooting" show encouraging results.
A total of 25,486 rumors and 76,106 non-rumors are additionally obtained for "sydneysiege", and 21,519 rumors and 62,590 non-rumors are additionally obtained for "ottawashooting".
We make our augmented data publicly available 4 .
Rumor Detection We conduct rumor detection experiments using two different data sets: (1) PHEME5, (2) PHEME5 with the "bostonbombings" data ("PHEME5+Boston").
We employ BID10 's method as a SoA baseline model for rumor detection with slight modifications.
For the sake of simplicity, we modify the implementation of "MTL2 Veracity+Detection" for rumor detection only.
We construct input by using a source tweet and the top (i.e., most recent) 24 replies in this task.
We perform leave-one-out cross-validation (LOOCV) on the PHEME5 and augmented data sets.
The overall experimental results for rumor detection are presented in TAB4 .
TAB5 shows LOOCV results.
We observe that overall performance decreases with the augmented data (i.e., PHEME5+Boston).
The "fergusonunrest" is the most difficult event for a rumor detection model as it has a unique class distribution distinguished from all other events BID10 .
It is worth noting that our data augmentation improves the performance of rumor detection on the "fergusonunrest".
The completion of data augmentation for events other than "'bostonbombings" has potential to boost overall and per event performance of rumor detection.
We present a methodology of data augmentation for rumor detection that exploits semantic relatedness between limited labeled data and unlabeled data.
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.
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.
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.
We will evaluate the effectiveness of augmented data in alleviating over-fitting and its usefulness in facilitating deeper NNs for rumor detection.
Further experiments will be conducted to examine the generalization of rumor detection models on unseen rumors.

assistant: text: We propose a methodology of augmenting publicly available data for rumor studies based on samantic relatedness between limited labeled and unlabeled data.