LEX-GAN: Layered Explainable Rumor Detector Based on Generative Adversarial Networks
Authors: Mingxi Cheng
Yizhi Li
Shahin Nazarian
Paul Bogdan
Venue:  (2020)
Keywords: explainable rumor detection
layered generative adversarial networks

Abstract: Social media have emerged to be increasingly popular and have been used as tools for gathering and propagating information. However, the vigorous growth of social media contributes to the fast-spreading and far-reaching rumors. Rumor detection has become a necessary defense. Traditional rumor detection methods based on hand-crafted feature selection are replaced by automatic approaches that are based on Artificial Intelligence (AI). AI decision making systems need to have the necessary means, such as explainability to assure users their trustworthiness. Inspired by the thriving development of Generative Adversarial Networks (GANs) on text applications, we propose LEX-GAN, a GAN-based layered explainable rumor detector to improve the detection quality and provide explainability. Unlike fake news detection that needs a previously collected verified news database, LEX-GAN realizes explainable rumor detection based on only tweet-level text. LEX-GAN is trained with generated non-rumor-looking rumors. The generators produce rumors by intelligently inserting controversial information in non-rumors, and force the discriminators to detect detailed glitches and deduce exactly which parts in the sentence are problematic. The layered structures in both generative and discriminative model contributes to the high performance. We show LEX-GAN's mutation detection ability in textural sequences by performing a gene classification and mutation detection task.

Under review as a conference paper at ICLR 2020
LEX-GAN: LAYERED EXPLAINABLE RUMOR DETEC- TOR BASED ON GENERATIVE ADVERSARIAL NET- WORKS
Anonymous authors Paper under houble-blind review
ABSTRACT
Social media have emerged to increasingly popular and have been used as tools for gathering and propagating information. However, the vigorous growth of so- cial media contributes to the fast-spreading and far-reaching rumors. Rumor de- tection has become necessary defense. Traditional rumor detection methods based on hand-crafted feature selection are replaced by automatic approaches that are based Artificial Intelligence (AI). AI decision making systems need to have the necessary means, such as explainability to assure users their trustworthiness. Inspired by the thriving development of Generative Adversarial Networks (GANs) on text applications, we propose LEX-GAN, a GAN-based layered explainable rumor detector improve the detection quality and provide explainability. Un- like fake news detection that needs previously collected verified news database, LEX-GAN realizes explainable rumor detection based on only tweet-level text. LEX-GAN is trained with generated non-rumor-looking rumors. The generators produce rumors by intelligently inserting controversial information in non-rumors, and force the discriminators detect detailed glitches and deduce exactly which parts in the sentence are problematic. The layered structures in both generative and discriminative model contributes the high performance. We show LEX-GAN's mutation detection ability in textural sequences by performing a gene classifica- tion and mutation detection task.
INTRODUCTION
Sequential synthetic data generation such as generating text and images that are indistinguishable to human generated data have become an important problem in the era of Artificial Intelligence (AI). Generative models, e.g., Variational AutoEncoders (VAEs) (Kingma & Welling 2013). Generative Adversarial Networks (GANs) (Goodfellow et al.] 2014). Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) cells (Hochreiter & Schmidhuber 1997), have shown outstand- ing generation power of fake faces, fake videos, etc. Consequently, we require discriminative models capable of detecting Al-generated fake data with explainability in order to manage the malicious vi- ral information (Knight 2019). black box decision maker without explainability that does not shed light into how the decision are made may hence lose the trust of its users.
GANs estimate generative models via adversarial training process (Goodiellow al. 2014). Powerful vell-vllued generators have found applications in image and video generation. However, GANs face challenges when the goal to generate sequences of discrete tokens such text (Yu et al. 2017). Given the discrete nature of text, backpropagating the gradient from the discriminator to the generator becomes infeasible (Fedus et al.) 2018). Training instability a common problem of GANs, especially those with discrete settings. Unlike image generation, the autoregressive prop- crty in text generation exacerbates the training instability since the loss from discriminator is only observed after sentence has been generated completely (Fedus et al. 2018).
In addition to the recent development in GAN-base text generation, discriminator-orienni GAN- style approaches proposed for detection and classification applications, such rumor detection (Ma et al.) 2019). Differently from the original generator-oree oriented GANs, discriminator GAN-based models take real data input the generator instead of noise. Hence fundamentally,

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the detector may get high performance through the adversarial training technique. Current adversar- ial training strategies improve the robustness against adversarial samples. However, these methods lead to reduction of accuracy when the input samples are clean (Raghunathan et al., 2019).
On a related note, social media and micro- blogging have become increasingly popular (Yaddanfarrd et al. 2011 Viviani & Pasi 2017). The convenient and fast-spreading nature of micro-blogs fosters the emergence of various rumors. Commercial giants, government authorities, and academic re- searchers take great effort in diminishing the negative impacts of rumors (Cao et al. 2018). Rumor detection has been formulated into a binary classification problem by lot of researchers. Tradi- tional approaches based on hand-crafted features describe the distribution of rumors (Castillo al., 2011 Kwon et al.] 2013). More recently, Deep Neural Network (DNN)-base) methods extract and learn features automatically, and achieve significantly high accuracy on rumor detection (Chen et al.) 2018). Generative models have also been used to improve the performance of rumor detectors (Ma et al. 2019). However, binary rumor classification lacks explanation since it only provides binary result without expressing which parts of sentence could be the source of problem. The majority of the literature defines rumors as "an item of circulating information whose veracity status is yet to be verified at the time of posting" (Zubiaga et al. 2018). Providing explainability for unverified rumor detection is challenging. A related research area works with fake news is more well-studied since fake news has verified veracity. Autribute information, linguistic features, and semantic meaning of post (Yang et al.] 2019) and/or comments (Shu et al.] (019)) have been used to provide explain- ability for fake news detection. A verified news database has to be established for these approaches. However, for rumor detection, sometimes a decision has to be made based on the current tweet only. Text-level models with explainability that recognize rumors by feature extraction should be developed to tackle this problem.
In this work, we propose LEX-GAN, a GAN-based layered explainable framework for text-level rumor detection. LEX-GAN keeps the ability of discriminating between real-worke and generated samples, and also serves as discriminator-oriente fake samples. We overcome the infeasibility of propagating the gradient from discriminator back to the generator by applying Reinforcement Learning (RL) to train the layered generators. The training instability of long sentence generation is lowered by selectively replacing words in the sentence. We solve the per time step error attribution difficulty by word-level generation and evaluation. We show that our model outperforms the baselines in terms of addressing the degraded accuracy problem with clean samples only. The major contributions of this work are listed as follows:
model that classifies real-world and generated
LEX-GAN delivers an explainable rumor detection without requiring verified news database. Rumors could stay unverified for a long period of time because of information insufficiency. Providing explainability of which words in the sentence could be problem- atic is critical especially when there is no verified fact. When a verified news database is achievable, LEX-GAN be applied to fake news detection with minor modification.
The layered structure of LEX-GAN avoids the function mixture and boosts the perfor- mance. During framework design, we found that using one layer to realize two functions either in generative or discriminative model causes function mixture and hurts the perfor- mance. LEX-GAN generates high-quality rumors by first intelligently selecting words to be replaced, then choosing appropriate substitutes to replace. The explanation generation and rumor detection are realized separately by two layers in the discriminative model.
LEX-GAN is powerful framework in textural mutation detection. We demonstrate the mutation detection power by applying LEX-GAN to a gene classification and mutation detection task. LEX-GAN accurately identifies tokens in the gene sequences that are likely from the mutation, and classifies mutated gene sequences with high precision.
2 RELATED WORK
GANs in continuous space promise full differentiabilityy When dealing with discrete elements such as text, the discrete nature results in non-differentiabiiity (Huszár 2015). Researchers work with discrete element generation to either avoid the issue and reformulate the problem. or consider RL methods Yu et al. 2017). Guirajani et al. (Gulrajani et al.) 2017) propose fully differentiable Convolutional Neural Network (Cse) wasserstein GAN that produces sequences of 32 char- acters. However, the quality of generated sentences would be sufficient only if spelling emror
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frequent. RNN-based GANs, the other hand, generate sequence word by word, hence the qual- ity issues such as spelling errors are rare. Many researchers have explored the non-differentiable problem in RNN-based GANs. Wolf et al. (Press al. (017) extend the work of (Gulrajani et al. 2017] by training RNN using curriculum learning. They start by training on short sequences and then slowly increase the sequence length. SeqGAN (Yu et al. (017)) bypasses the generator differ- entiation problem by performing gradient policy update. The RL reward signal coming from the discriminator calculated on complete sequence. Differently from SeqGAN, MaskGAN (Fedus) et al. 2018) employs an actor-critic training procedure a text infilling task designed to provide rewards every time step.
Inspired by the thriving development of GANs on text applications, GAN-style rumor detection has become the state-of-the-arr. GAN-GRU (Ma et al.) 2019) is one the latest GAN-based rumor detectors. By utilizing adversarial training, generator is designed to create uncertainty. Hence, the complicated sequences pressurise the discriminator to learn stronger rumor representations. GAN- GRU reaches the highest accuracy of 78.1% on a bench- bench-marking rumor dataset PHEME (Kochkinal ct al. 2018). Although GAN-GRU outperforms other models, it does not offer explainability, nor does it provide any information regarding the parts of the sentences that are problematic. Addressing such deficiencies has been the main motivation for our work.
LEX-GAN
Figure shows the architecture of our proposed LEX-GAN. In rumor detection task, the generators have to intelligently construct rumor that appears like non-rumor to deceive the discriminators. Since a good lie usually has some truth in it, we choose to replace some of the tokens in the sequence and keep the majority realize this goal. Two steps for intelligently replacing tokens in sequence are: i) determine where to replace, and ii) choose what substitutes to use. Gurbere and Greplace designed to realize these steps. After constructing the strong generators, the discriminators are designed to provide defense mechanism. Through adversarial training, the generators and discriminators grow stronger together, in terms of generating and detecting rumors, respectively. In rumor detection, given a sentence, there are two questions that need to be answered: i) is it rumor non-rumor, and if rumor, which parts are problematic. Delassify and Desplain are designed to answer these two questions. We found that realizing two functions by one layer either in discriminative model or generative model hurts the performance. Hence the layered structure designed.
G_where
D_explain
BILSTM
BILSTV
BILSTM
BILSTM
Bilsm BI.STM
BILST SILSTM
Rumma Moo mumnor
Discriminative Model
Generative Model
BLLTT
SI.STM
BILSTM
BILSTMM
Cony
Comy
Wesstonn Layer
GRU Decater
Whore
Rumer Nors rutror"
Rumor/ Non numor
Coll
Corn
O_roplace
D_classiffy
Figure 1: LEX-GAN framework. The generative model (shown on the left hand side) consists of two generators Gwhere and Greplece- The discriminative model (shown on the right hand side) consists of two discriminators, namely Desploin for explainability and D Delassify for classification.

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3.1 GENERATIVE MODEL
The sequence generation task of LEX-GAN is done by the generative model: Gwkere and Greptace- Given human-generated real-world sequence input example a tweet-level sentence containing M words, Gwhere outputs probability vector p = (Pt, P2, ... PM) indicating the probabilities of each item z; (i E MZ) being replaced. p is applied to imput x to construct new sequence x xukere with some items replaced by blank. For example, becomes blank and x xareree
(211 (02,-.-,3M)) with length M, for
(11, -01; T.M
TMM.
x whhere
f(p) ox= f(G (x))
(1)
where f(-) binarizes the input based on a hyperparameter N Nreplace. determines the percentage of h
the words to be replaced in sentence. Operator works as follows. If = 1. then Qob = b. If a = 0, Greplace an encoder-decoder model with attention mechanism. It takes xwhere
then a
and fills in the blank, then outputs a sequence xreplace 1,,,,22 replaae TM). The generative model not fully differentiable because of the sampling operations on Gwhere and Greploce. To train the generative model, we adopt policy gradients (Sutton et al. (0000 from RL to solve the non non-differentiable issue.
3.1.1 Greplace GRU-BASED ENCODER
Gated Recurrent Units (GRUs) Cho et al. 2014) improved versions of standard RNNs that use update gates and reset gates to resolve the vanishing gradient problem of a standard RNN. In our GRU-based encoder, the hidden state h2 is computed
GRUencodee (z:thhere, hh-1):
he (1 (1-2)
24) @ h4-1 + 24 N,,,
2, (Wene It where Ueechh,1 + BEnc),
(3)
h4 tanh (W p whrre URRCC(T @ hi-1) bRTC).
(4)
Ti o (W wnkere +U, - + by""),
(5)
where Wene, Wene, Were, Uenc, U and Uene are encoder weight matrices. o()) is the sigmoid function. O represents element-wise multiplication.
3.1.2 Greplace GRU-BASED DECODER WITH ATTENTION MECHANISM
In LEX-GAN, our encoder-decoder Greplace utilizes attention mechanism (Bahdanau et al. 20144) to automatically search for parts of sentence that are relevant to predicting the target word. The con- tent vector e summarizes all the information of words in a sentence. It depends on the annotations h and is computed weighted sum of these h4:
Σ j=1
atyhi,
(6)
ear((er))
Otj
Σ M
k= eap(en)
Ctj = a a85-11
(8)
where erj scores how well the inputs around position j and the output position t match. Alignment model a is a neural network that jointly trained with all other components. The GRU decoder takes the previous target It-1 and the context vector & as input, and utilizes GRU to compute the hidden state as GRU RUdooeer (3t-11 St-1, C.):
=1) 3 81-1 200%
(9)
* = @Wder y1- + Udees 8 + Cideoe);
(10)
of = tanh yt-1 + Ude 2 00-1)))-----))))) Code o = ( + sedess;-1++ + Ct)
(11)
(12)
where Wdde, Wddee, Wdde, Uder, Uder, Udler Oidea, Odee and Odee are decoder weight matrices. Through this attention- equipped encoder-decodee Greptace intelligently replaces items in sequences and outputs adversarial samples.
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3.2 DISCRIMINATIVE MODEL
The generated adversarial samples xreplace combined with original data x are fed to the discrimina- tive model. fylassify and Desplain are trained independently. We note that the two discriminators can depend on each other, but we have chosen to explore the dependency as part of our future work on LEX-GAN, Delossifu provides a probability in rumor detection, and Demplain provides the prob- ability of each word in the sentence being problematic. The explainability of LEX-GAN is gained by adversarial training. We first insert adversarial items in the sequence, then train Desplain to detect them. Through this technique, LEX-GAN can not only classify data with existing patterns, but also classify sequences with unseen patterns that may appear in the future. Adversarial training improves the robustness and generalization ability of LEX-GAN. We will show in Section 5 the effectiveness of LEX-GAN in addressing the accuracy reduction problem, when compared to prior work.
3.3 TRAINING
In rumor detection, a sequence has true label Y being either rumor R non-rumor N. After manipulating the sequence x, output of the generative model "eeplace is labeled as R since it is machine generated. The objective of a o-parameterized generative model is mislead the 8- parameterized discriminators. In our case, D Dalassity (xreplace) indicates how likely the generated
reellace is classified N. De Dezpiain (xreplace) indicates how accurately D Dezplain detects the placed words in sequence. The error attribution per time step in LEX-GAN is achieved naturally since Do Déxptain evaluates each token and therefore provides a inneggriieed supervision signal to the generators. For example, case where the generative model produces sequence that deceives the discriminative model. Then the reward signal from De Desplain indicates how well the position of each
replaced word contributes to the error result. The reward signal from D D'daesify represents how well the combination of the position and the replaced word deceived the discriminator. The generative model is updated by applying a policy gradient on the received rewards from the discriminative model.
The rumor generation problem is defined as follows. Given a sequence x, Gwhere is used to produce a sequence of probabilities p indicating the replacing probability of each token in x. G repiace takes xwheer and produces a new sequence rreplace This newly generated rreplace is a sentence, part of which is replaced and labeled as R. At time step t. the state s consists of giheere and sreplace guhere -PPI...,::-1 sheplace = (2 Feplace ,.... 2 replacr). The policy model and G Greptace (2, (epepiace reppaace 21- (replane) are stochastic. Following RL,
Go where (PolPL,.... ....
G where 's objective is to maximize its expected long-term sergttermue reward:
Jwhere (Ø) = E(Rr (S0 o1= Σ G (pr (al (heeee
Q (s
replace a),
(13)
-D 2h reploce a) Desxpiain (s (srepluee) +D D
Dessssifyl (Sp (Seplace)..
(14)
where Q (So. a) is the accumulative reward following policy G starting from state So (sahhere, 580 repparny.
D explain (srepiace) indicates how much the generative model misleads
D Pexplain is an action set that contains output of both G wwhere and G Greplaee Ri the reward for complete sequence. Similarly to Go Gwhere' G Grepiace maximizes its expected long term reward: replace replace) Q8" replace a).
Jreplace (0)
Σ
G Greptace (x
(8)
(15)
replace
We apply a discriminative model provided reward value to the generative model after the sequence is produced. The reason is that our Grepiace doesn't 't need to generate each and every word in the sequence, but only fills a few blanks that are generated by 'whhere' Under this assumption, long-term reward is approximated by the reward gained after the whole sequence is finished.
The discriminative model and the generative model are updated alternately. The loss function of discriminative model defined as follows:
Lp X
yezplain eerlaain classify y classsfy
T.D
X
T.
(16)
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L
explain D
yo f(G where (x)) [ylog( D
explein (x ) 1 eepplain (x (xeelecc))]
(17)
I.D ccasssiv = - E (yloolloogssaao ( classify (xreplace }) + (1 y) - Delassif fy (xeelacc)))
(18)
where X eaplain and X x the balancing parameters.
We adopt the training method in GANs to train the networks. In each epoch, the generative model and the discriminative model are updated alternately. Over-training the discriminators the gener- ators may result in a training failure. Thus syper-parameters GSTEP and D DSTEP are introduced to balance the training. In each epoch, the generators trained GSTEP times. Then discriminators are trained DSTEP times.
4 EXPERIMENT SETTINGS
4.1 DATASETS
We evaluate LEX-GAN on a benchmark Twitter rumor detection dataset PHEME (Kochkina et al., 2018), and a splice site benchmark dataset NN269 (Reese et al. 1997). PHEME has two versions. PHEMEv5 contains 5792 tweets related to five news, 1972 of them are rumors and 3820 of them are non-rumors. PHEMEv9 contains 6411 tweets related to nine news, 2388 of them are rumors and 4023 of them are non-rumors. NN269 dataset contains 13323 splice site sequences. It has 6985 acceptor splice site sequences with length of 90 nucleotides, 5643 of them are positive AP and 1324 of them are negative AN. It also has 6246 donor splice site sequences with length of 15 nuclcotides, 4922 of them are positive DP and 1324 of them are negative DN.
4.2 MODELS
In the rumor detection task, compare LEX-GAN with six popular rumor detectors: RNN with LSTM cells, CNN, VAE-LSTM, VAE-CNN, a contextual embedding model with data augmenting (DATA-AUG) (Han et al. 2019), and GAN-based rumor detector (GAN-GRU) (Ma et al.] 2019). One of the strengths of LEX-GAN is that under the delicate layered structure that we designed, the choice of model structure effects the results but significantly. To showcase this ability of the layered structure, we generate a variation of LEX-GAN as one baseline. LEX-LSTM is generated by replacing LEX-GAN's Greplace with LSTM model. LEX-GAN generates set of sequences by substituting around 10% of the words in original sequences. We pre-train the Delasei by fix- ing Nreplace = 10%. We then freeze Delessify and train the other three models. During training, we lower replace from 50% to 10% to guarantee data balancing for Desplain and hence better re- sults in terms of explainability. All the embedding layers in the generators and discriminators are initialized with 50 dimension GloVe (Pennington et al. (014) pre-trained vectors. Early stopping technique is applied during training. LEX-LSTM is trained under the same training process. The generated data in the rumor task are labeled R. and we denote this dataset as PHEME'. For fair- ness and consistency, we train baselines LSTM, CNN, VAE-LSTM, and VAE-CNN with PHEME and PHEME+PHEME: For all baselines, we tWO evaluation principles: (i) hold out 10% of the data for model tuning. (ii) Leave-one-out (L.) principle, i.e., leave out one news for test, and train the models on other news. Final results are calculated as the weighted average of all results. L principle constructs realistic testing scenario and evaluates the rumor detection ability under new out-of-domain data. For DATA-AUG and GAN-GRU, we import the best results reported in their papers.
For the gene classification and mutation detection task, LEX-GAN generates a dataset NN269' by replacing nine characters in acceptor sequences and three characters in donor sequences. We compare LEX-GAN with six models: RNN with L.TTM cells, CNN, VAE-LSTM, VAE-CNN, LEX- LSTM, and state-of-the-ar splice site predictor EFFECT from (Kamath et al. 2014). The first four baselines are trained under NN269+NN269', and tested on both NN269+NN269 and clean data NN269. We import EFFECT's results from (Kamath et al., 2014). To evaluate LEX-GAN's generalization ability, we label the generated sequences by the following rule: if the input sequence has label Y, then the output sequence x" replace is labeled as Y', indicating that reptace is from class Y but with modification. The final classification output of LEX-GAN is two-fold: AP, AN for acceptor, or DP. DN for donor. We merge the generated classes AP', AN' and DP', DN

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with original classes to evaluate the generalization ability of LEX-GAN. Given sequence, LEX- GAN can classify into one of the known classes, although the sequence could be either clean or modified.
Architecture setup of LEX-GAN is as follows. Gwhere is a RNN with Bidirectional LSTM (BiL- STM). It has two hidden BiLSTM layers followed by dense layer. The one used in all ex- periments has the architecture of EM-32-32-16-OUTE where EM, OUT represent embedding and output, respectively. Grepiace is an encoder-decoder with attention mechanism. The encoder has two GRU layers, and the decoder has two GRU layers equipped with attention mechanism. The architecture of Greplace we used in all experiments is EM-66 EM-64-64-END ------6------000--- 6U-OTT. Desplain has the same architecture as Gwhere- Delessify y is CNN with two convolutional layers followed by dense layer. The one we used in all experiments has the architecture of -1----244----UUU LEX-LSTM utilizes an LSTM- encoder-decoder with architecture For baselines architecture details, see Appendix A
as Grepiace-
Table 1: Macro-fl and accuracy comparison between LEX-GAN and baselines on the rumor detec- tion task. BOTH represents the models are trained on PHEME+PHEME' indicates the best result from the work that proposed the corresponding model. L represents the model is evaluated under leave-one-out principle.
PHEMENS
PHEMErY
PHEME
PHEME
PHEME
PHEME+PHEME
Macro-f1 fll
Accuracy
Macro-f1
Accuracy
Macro-fl
Accuracy
Macro-f1
Accuracy
PHEME
0.6425
0.6542
0.4344
0.4345
0.6261
0.6260
0.4599
0.5283
LSTM
BOTH
0.5-113
0.5761
0.6421
0.6621
0.1938
0.53-11
0.5937
0.6103
CNN
PHEME
0.6808
0.6660
0.4792
0.4833
0.6549
0.6552
0.54/2
0.5253
BOTH
0.4936
0.5273
0.5757
0.5927
0.5025
0.5400
0.5880
0.6040
VAE-LSTM
PHEME
0. 0-1677
0.5625
0.2582
0,2871
0.1454
0. 1-1589
0.1231
0. 0.1326
BOTH
0. 0.4587
0. 0.4589
0.5001
0.5595
0.33-19
0. 0.4960
0.21448
0.2592
VAE-CNN
PHEME
0.5605
0.5605
0,1655
0,49022
0. 0.3859
0.5029
0.2513
0.2778
BOTH
0.5395
0.5429
0.4838
0.5302
0.5164
0.5166
0.45733
0.4794
GAN-GRU
PHEME
0.7810
0.7810
LEX-LSTM
PHEME
0.8242
0.8242
0.6259
0.6300
0.8066
0.8066
0.6884
O. 7044
LEX-GAN
PHEME
0,8475
0,8476
0.6524
0.6777
0,8084
0.8095
0.7620
0,8085
LSTM (L)
PHEME
0.5893
0.6033
0.5280
0.57I0
0.5217
U. 0.5827
0.5033
0.39U0
BOTH
0.3854
0-4478
0.4980
0.6372
0.4120
0149333
0.5650
0.6729
PHEME
0.5994
0.6406
0.5324
0.5779
0.5477
0.6035
0.5051
0.5769
CNN (L)
BOTH
0.4265
0. 0.1719
0.5256
0.6587
0. 0.3679
O. 1601
0. 0.4562
0. 0.6:155
PHEME
0.3655
0.3996
0.3620
0.3959
0.4256
0.5367
0.4284
0.5397
VAE-LSTM (L)
BOTH
0.3919
0.5198
0.3876
0,5174
O. 1225
0.5442
0. 0.12700
0,3412
VAE-CNN (L)
PHEME
0.4807
0.5190
0.4816
0.5214
0.4316
0.4507
0.4314
0.4587
BOTH
0.1594
0.5320
0. 1662
0,5380
0. 0.1686
0.5341
0.1786
0.5-111
DATA-AUG (L)
PHEME
O. 0.5350
0.7070
LEX-LSTM
PHEME
0.6666
0.6866
0.5703
0.6411
0.5972
0. 0.6272
0.5922
0.6371
LEX-GAN (L)
PHEME
0.6745
0.7016
0.6126
0.6342
0.6207
0.6438
0.6016
0.6:00
RESULTS
5.1
RUMOR DETECTION
Comparison between LEX-GAN Delassify and baselines in the rumor detection task is shown in Table [ In this experiment, we use PHEME data to train LEX-GAN, During training. LEX-GAN generates PHEME' enhance the discriminative model. In real world applications, original clean dataset is available all the time. However, the modified or adversarial data that contains different pat- terns are not always accessible. Models like LSTM and CNN do not have generalization ability and usually perform worse facing adversarial input. Generative models such as GANs are more robust. In VAE-LSTM and VAE-CNN, we first pre-train VAEs, then L.STM and CNN are trained under latent representations of pre-trained VAEs. Under the first evaluation principle (described in Section 4.2). LEX-GAN and LEX-LSTM outperform all baselines in terms of both macro-fl and accuracy. Accuracy is not sufficient when the test data are not balanced, hence macro-fl is provided for com- prehensive comparison. Under the first evaluation principle, the robustness and generalization ability of LEX-GAN and LEX-LSTM are shown by comparing with baselines under PHEME+PHEME LEX-GAN reaches the highest values in both versions of PHEME+PHEME' and lows the second best. Under I. principle, LEX-GAN and LEX-LSTTM achieves highest macro-f1
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scores in all cases. These results confirm the rumor detection ability of the proposed layered struc- ture under new, out-of-domain data. Adversarial training of baselines improves generalization and robustness under PHEME+PHEME' but hurts the performance under clean data as expected. Al- though LEX-GAN and LEX-LSTM are trained adversarially, they achieve the highest macro-fl under clean data PHEME. The results confirm that LEX-GAN outperforms the baselines in terms of addressing accuracy reduction problem.
LEX-GAN's Deaplain recognizes the modified parts in sequences accurately. Its macro-fl on PHEME'vS and PHEME'v9 are 80.42% and 81.23%, respectively. Examples of Deapioin predict- ing suspicious parts in rumors are shown in Table In the first rumor, "hostage escape" the most important part in the sentence, and if these two words are problematic, then the sentence is highly likely to be problematic. Given an unverified or even unverifiable rumor, Perplain provides reason- able explanation without requiring previously collected verified news database. Additional results of LEX-GAN's comparison with baselines can be found in Appendix
Table 2: Examples of D Demplain predicting suspicious words in rumors (marked in bold). Delassify outputs probabilities in range [0, 1], where and 1 represents rumor and non-rumor, respectively.
0.0010
breaking update hostages escape lindt café through front door via fire door url sydneysiege url
0.0255
newest putin rumour his girlfriend just gave hirth to their child url cdnpoli russia
0.0300
soldier gets epr after being shot at war memorial in ottawa url
0.0465
sydney's central business district is under lockdown gunman lakes hostages cale live stream it unfolds url
0.2927
so in 5mins mike brown shaved his head and changed his scandals to shoes think your being lied too ferguson url
5.2 GENE CLASSIFICATION AND MUTATION DETECTION
In this experiment, all models are trained under NN269+NN269' to ensure fairness. When test with NN269+NN269', there are 8 classes in total: AP, AN, DP, DN from NN269 and AP', AN', DP', DN from NN269'. If solely clean data from NN269 is accessible during training, then LEX-GAN and LEX-LSTM are the only models that can recognize if a given sequence is modified or unmodified. Comparison between LEX-GAN's (and LEX-LSTM's) Detassify and baselines is shown in Table 3 Under long acceptor data, baselines perform significantly worse than LEX-GAN and LEX-LSTM. Under short donor data, LEX-GAN and LEX-LSTI achieve highest AURoCs. This implies that LEX-GAN and LEX-LSTM are stronger when the input are long sequences. The layered structure and adversarial training under the augmented dataset provide LEX-GAN the ability of extracting meaningful patterns from long sequences. For short sequences, LEX-GAN and LEX- LSTM provide highest AURoC, and simpler models such as CNN can also give good classification results. This is because for short sequences, textural feature mining and understating is relatively easier then in long sequence. Under NN269', LEEX-AN' Detassify and Deaplain achieve 22.25% and 72. 72.69% macro-fl, respectively. Additional gene mutation detection experiments can be found in Appendix B.2
5.3 LIMITATIONS AND ERROR CASES IN RUMOR DETECTION
Examples of eтror cases of LEX-GAN in rumor detection task are presented in Table 4 short sentences, Derplain sometimes fails to predict the suspicious parts. The reason is that the majority of LEX-GAN's training data are long sentences, hence for short sentences, Dexplain does not perform as well as in long sentences. This problem could be solved by feeding more short sentences to LEX-GAN. In most cases, although Dezpioin does not generate predictions, D Delassify still can provide accurate classification. As shown in Table Deaasify outputs low score, i.e., classifies the input as rumor, for four out of five rumors.
For some
6 DISCUSSION
In this work, we proposed LEX-GAN, a layered explainable text-level rumor detector based on GAN. We used the policy gradient method to effectively train the layered generators. LEX-GAN
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Table 3: Comparison between LEX-GAN and baselines the gene classification and mutation
detection task. indicates the best result from the corresponding paper. 2-class refers to AP, AN for acceptor, and DP. DN for donor. 4-class refers AP, AN, AP,, AN for acceptor, and DP,
DN, D DN' for donor. A and D indicate acceptor and donor.
NN269 (2-class)
NN269+NN269' (2-class)
NN269+NN269 (4-class)
Macro-f1
Accuracy
AURoC
Macro-f1
Accuracy
AURoC
Macro-fl
Accuracy
AURoC
(A)
0.8120
0.8870
0.9305
0.7794
0.8580
0.9036
0.7800
0.8580
0.9715
CNN (A)
0.5663
0.7933
0.6324
0.5594
0.7808
0.6131
0.5593
0,7808
0.8875
VAE-LSTM (A)
0.7664
0.8566
0.8451
0.6781
0.8323
0.7780
0.6531
0.8342
0.8806
VAE-CNN (A)
U.5657
0.7539
0.6135
0.5744
0.7651
0.6219
0.5379
0.7470
0.8411
EFFECT (A)
0.9770
LEX-LSTM (A)
0.9131
0.9458
0.9781
0.8794
0.9243
0.9658
0.8758
0.9223
0.9879
LEX-GAN (A)
0,9175
0,9494
0,9807
0.8831
0,9301
0,9691
0,8839
0,9311
0,9894
LSTM(D) (D)
0.8336
0.8214
0,9003
0.8148
0.7398
0.8802
0.7648
0.7530
0.92-6
CNN (D)
0.9131
0.9398
0.9795
0.9025
0.9323
0.9746
0.8336
0.8583
0.9596
VAE-LSTM (D)
0.8011
0.8515
0.9218
0.7336
0,8329
0.8217
0.5774
0.7692
0.9194
VAE-CNN (D)
0.8386
0.8772
0.9554
0.7909
0.8593
0.8528
0.5585
0.7415
0.9190
EFFECT (D)
0.9820
LEX-LSTM (D)
0.9272
0.9484
0,9822
0.8802
0.9140
0.9766
0.8113
0.8580
0.9541
LEX-GAN (D)
0.9274
0.9494
0.9810
0.8988
0.9296
0.9635
0,8119
0.8477
0.9776
Table 4: Examples of Deaplain failing to predict suspicious words in some short rumors. Deloseify outputs probabilities in range [0, 1J. where 0 and 1 represents rumor and non-rumor, respectively.
0.0112
ottawa police report a third shooting at rideau centre no reports of injuries
0.0118
breaking swiss art museum accepts artworks hequeathed by late art dealer gurlitt url
0.0361
breaking germanwings co pilot was muslim convert url
0.5771
the woman injured last night ferguson url
0.4451
germanwings passenger plane crashes in france url
outperforms the baseline models in mitigating the accuracy reduction problem, that exists in case of only clean data. We demonstrate the classification ability and generalization power of LEX-GAN by applying two applications: rumor detection and gene classification and mutation detection.
On average, in the rumor detection task, LEX-GAN outperforms the baselines on clean dataset PHEME and enhanced dataset PHEME+PHEME" by 26.85% and 17.04% in terms of macro-fl, respectively. LEX-GAN provides reasonable explanation without a previously constructed verified news database, and achieves significantly high performance. In the gene classification and mutation detection task, LEEX-AN identifies the mutated gene sequence with high precision. On average, LEX-GAN outperforms baselines in both NN269 and NN269+NN269' (2-class) by 10.71% and 16.06% in terms of AURoC, respectively. In both rumor detection and gene mutation detection tasks, LEX-GAN's explainability is demonstrated by identifying the mutations accurately (above 70% macro-fl). We find that using two discriminators perform classification and explanation separately achieves higher performance than using one discriminator to realize both functions. We also found the pre-train of Delassify and varying Nreetace contribute to the high accuracy of Dezplain- As part of our future work, we would like to explore the application of hierarchical attention network Yang et al., 2016) to improve the performance of our generative model. We will also investigate the dependencies between the discriminators of LEX-GAN.
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Carlos Castillo, Marcelo Mendoza, and Barbara Poblete. Information credibility on twitter. In Proceedings of the 20th international conference on World wide web, pp. 675-684, ACM, 201 1.

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A BASELINES ARCHITECTURE SETUP
The architectures of baselines LSTM, CNN, VAE-LSTM, and VAE-CNN used both tasks are defined as in Table 5 VAE-LSTM and VAE-CNN use pre-trained VAE followed by LSTM and CNN with the architectures we defined Table 5 The VAE we pre-trained incoder-decoder The encoder with architecture EM-32-32-32-OU has two LSTM layers followed by a dense layer. The decoder has the architecture IN-32-32-OUT, where IN stands for input layer.
LSTM-based
Table 5: Baselines' architecture.
Model
Gene mutation detection task
Rumor detection task
LSTM
LMML)SMMIG))LLSMM)))) LEA.LLLSSTMMMEE--EEEEEEEEE DENSE(8)-OUT
EM-L MMLLSTM(22--LLTMMM S
CNN
(ANCONN3))-CONCCEEE (ENSEEEE DENSE(16) OUT
EM-CONV (ONVV/GB-CONVVEE CONY(16) (EVIIIDEEEEEE)---CO
VAE-LSTM
(STM(32)-LSTMG2) -ETTTMG22-DENEES).OOO. OUT
LSTMG32)-LSMILL LTA.LLLSS-I-----EEEOOOOOOO OET
VAE-CNN
CONV(2)-CONVI66 (ONGG)//COOONG))DDEEEEE-------0
CONV(32)-CONV/CC) (ODOVV66)DDENSE(()))OLL
B ADDITIONAL SAMPLES
B. RUMOR DETECTION TASK
Additional results for the rumor detection task are shown Table 6 D Demptain provides reasonable
explanation in most rumors. Here also provide examples to demonstrate the rumor detection
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Table 6: Examples of Dezplain's 's prediction on rumors.
twin hostage situations erupt in paris two victims have been killed updated story url pilots of crashed germanwings flight declared emergency at 10 47 a m as plane fell rapidly url doomed germanwings co pilot 'suffered burnout or depression years before crash url url sky news australia a sixth hostage has escaped from the lindt cafe in sydney sydneysiege approximately 50 hostages may be held captive at lindt café local reports url sydneysiege url
Table 7: Examples of Demplain and Delassi 's prediction on rumor (first) and non-rumor (second). The suspicious words in the rumor predicted by Despluin marked in bold. Deiossify provides score ranges from 0 to 1. O and 1 represent rumor and non-rumor, respectively.
0.1579 who's your pick for worst contribution to sydneysiege mamamia uber the daily tele 0.8558 glad to hear the sydneysiege is over but saddened that il even happened to begin with my heart goes out to all those affected
power of LEX-GAN compared to baselines. Two examples that are correctly detected by LEX-GAN but incorrectly detected by other baselines is shown in Table 7
For the first rumor, baselines CNN, LSTM, VAE-CNN, and VAB-LSTM provide scores 0.9802, 0.9863, 0.4917, and 0.5138, respectively. LEX-GAN provides a very low score for a rumor, while other baselines all generated relatively high scores, and even detect as non-rumor. This is a very difficult example since from the sentence itself, we as human rumor detection agents even cannot pick the suspicious parts confidently. However, LEX-GAN gives a reasonable prediction and shows that it has the ability to understand and analyze complicated rumors. For the second non-rumor, baselines CNN, LSTM, VAE-CNN, and VAE-LSTM provide scores 0,0029, 0.1616, 0.6150, and 0.4768, respectively. In this case, a non-rumor sentence gains high score from LEX-GA LEX-GAN, but several relatively low scores from the baselines. This example again confirms that our proposed LEX-GAN indeed captures the complicated nature of rumors and non-rumors.
B.2 GENE CLASSIFICATION AND MUTATION DETECTION TASK ON GENE DATASET
We evaluate LEX-GAN on molecular biology splice-junction gene sequences dataset (GENE) (Dua & Graff 2017). GENE dataset contains 3109 splice junetion gene sequences, and each se- quence has 60 characters. Splice junctions are points on a DNA sequence at which superfluous DNA is removed during the process of protein creation in higher organisms (Dua & Graff 2017).
They are labeled as exon/intron boundaries (EI sites), intron/exon boundaries (IE sites), or neither (N).
For the gene classification and mutation detection task, LEX-GAN generates dataset GENE' by replacing five characters in each sequence. We compare LEX-GAN with five models: RNN with LSTM cells, CNN, VAE-LSTM, VAF-CNN, and DNA classifier (DNAC) from (Deshpande & Karypis 2002). The first four baselines are trained under GENE+GENE', and tested on both GENE+GENE and clean data GENE. We import DNAC's results from (Deshpande & Karypis 2002). To evaluate LEX-GAN's generalization ability, we label the generated sequences by the fol- lowing rule: if the input sequence has label Y, then the output sequence xreplace is labeled as Y'. indicating that x roplace is from class Y but with modification. The final classification output of -LEX-GAN is three-fold: EI, IE, or N. We merge the generated classes EI', IE'. N' with original classes to evaluate the generalization ability of LEX-GAN. Given a sequence, LEX-GAN can classify into one of the three known classes, although the sequence could be either clean or modified.
In this experiment, all models are trained under GENE+GENE' to ensure fairness. When test with GENE+GENE', there are 6 classes in total: E1, IE, N from GENE and ET', IE', N' from GENE'. If solely clean data from GENE are accessible during training, then LEX-GAN is the only model that can recognize if a given sequence is modified or unmodified. LEX-GAN can endue the baselines with this ability by generating GENE'. Comparison between LEX-GAN' D Dasssifu and basclines is shown in Table Baselines perform significantly worse than LEX-GAN in 6-class classification.
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This implies that the baselines only roughly classify modified sequence into known class as shown in 3-class experiment, but their fine-grained classification ability is significantly lower than LEX-GAN. On GENE', D and Dexplann achieve 91.80% and 49.49% macro-fl, respectively. An example of Dexplain 's prediction is shown in Table This suggests that LEX-GAN can not only identify whether gene-sequence is modified from which known class, but also provide accurate prediction that explains which part of the sequence is modified.
Table 8: Comparison between LEX-GAN and baselines on the gene classification and mutation detection task. indicates the best result from the corresponding paper. Note that DNAC does binary classification between E' and I E, hence its result is relatively higher than 3-class classification. 3-
class refers to EI, IE, N. 6-class refers EI, IE, N. EI' N'.
GENE (3-class)
GENE+GENE' (3-class)
GENE+GENE" (6-class)
Precision
Recall
Macro-f1
Precision
Recall
Macro-f1
Precision
Recall
Macro-fl
LSTM
0.7834
0.7930
0.7911
0.7971
0.7978
0.7972
0.7931
0.7990
0.7953
CNN
0.7233
0.7306
0.7265
0.7270
0.7301
0.7280
0.7146
0.7207
0.7165
VAF-LSTM
0.4733
0.3835
0.3494
0.5184
0.4899
0.4806
0.2851
0.3583
0.3086
VAE-CNN
0.8360
0.8583
0.8441
0.8210
0.8321
0.8236
0.3571
0.4605
0.3870
DNAC
0.9390
LEX-GAN
0.9510
0,9642
0.9571
0.9391
0.9418
0.9403
0.9266
0.9336
0.9297
Table 9: Examples of the generative model modifies gene sequences and the discriminative model
detects the modifications (marked in bold).
Modified Prediction Original
Modified
Prediction
Original
Modificd
Predicted
Original
Modified
Prediction
13


Reviews:
[{"review_id": "ryeF57Xc9r", "reviewer": "AnonReviewer4", "summary": "", "questions": "", "limitations": "", "rating": 8, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "In this paper, the authors proposed an interesting model to solve rumor detection problem. The LEX-GAN model takes the advantage of GAN in generating high-quality fake examples by substitute few works in original tweets.  It achieved excellent performance on two kinds of dataset. \n\nThe term ‘layered’ was a little confusing to me at the very beginning, though it is strengthened in many places around the paper. Maybe the author could use some other word to better summarize the two layers.\n\nAnother question is about the extended dataset with generated data, are they generated using the same distribution from G of the final model? What the result would it be if we use real, out-of-domain data?\n\nI would like to see this paper accepted to motivate future works on fake news detection and rumor detection..\n", "extra_scores": {"experience_assessment": "I have read many papers in this area.", "review_assessment:_checking_correctness_of_derivations_and_theory": "I assessed the sensibility of the derivations and theory.", "review_assessment:_checking_correctness_of_experiments": "I assessed the sensibility of the experiments.", "review_assessment:_thoroughness_in_paper_reading": "I read the paper at least twice and used my best judgement in assessing the paper."}, "extra_text": {}}, {"review_id": "SkxQro2BqH", "reviewer": "AnonReviewer2", "summary": "", "questions": "", "limitations": "", "rating": 1, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "The paper presents a method for detecting rumours in text. Early on in the paper, the authors claim that this method:\n1) is more accurate in rumour detection than prior work, \n2) can provide explainability, and\n3) does not need labelled data because it is trained on synthetic \"non-rumour looking rumours\".\n\nAll three of these statements are problematic.\nThe experimental evaluation uses a small dataset of rumour classification (about 15000 tweet related to 14 news topics) and an even smaller dataset of gene classification. The rationale is to use the gene classification task as a proxy for rumour detection. This is not valid. The gene classification task does not contribute to the evaluation of the rumour detection method. The rumour classification dataset is relatively small, but even more importantly, the experimental results on that dataset are not thoroughly analysed, for instance through an ablation test. \n\nExplainability is not evaluated experimentally, nor formally proven. \n\nThe claim that the method does not need labelled data because it is trained on synthetic \"non-rumour looking rumours\" is shaky, because 1) one could train the method on labelled data, and 2) it is not clear how \"non-rumour looking rumours\"  are guaranteed in the synthesis phase (how are they defined? how are they evaluated to be \"non-rumour looking rumours\"? etc).\n\nNote that there is no definition of what sort of data representation corresponds to a \"rumour\" in the paper. \n", "extra_scores": {"experience_assessment": "I have published one or two papers in this area.", "review_assessment:_thoroughness_in_paper_reading": "I made a quick assessment of this paper.", "review_assessment:_checking_correctness_of_experiments": "I assessed the sensibility of the experiments.", "review_assessment:_checking_correctness_of_derivations_and_theory": "I assessed the sensibility of the derivations and theory."}, "extra_text": {}}, {"review_id": "Ske9ubVNYS", "reviewer": "AnonReviewer1", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "In the paper, authors proposed a generative adversarial network-based rumor detection model that can label short text like Twitter posts as rumor or not. The model can further highlight the words that are responsible for the rumor accusation. \n\nProposed model consists of 4 sub models: a G_Where model finds the word to replace so to create an artificial rumor; a G_replace model decides what the replacement word should be; a D_classify model detects if a sequence is a rumor; a final D_explain model pinpoints the word of concern. D_ models and G_ models are trained in an adversarial competing way.\n\nExperiments showed that the LEX-GAN model outperforms other non-GAN models by a large margin on a previously published rumor dataset (PHEME) and in a gene classification task.\n\nMy questions:\n\n1) The task modeled is essentially a word replacement detection problem. Is this equivalent to rumor detection? Even if it performs really well on a static dataset, it could be very vulnerable to attackers. Various previous works mentioned in the paper, including the PHEME paper by Kochkina et al, used supporting evidence for detection, which sounds like a more robust approach.\n\n2) Authors didn't explain the rationale behind the choice of model structure, e.g. GRU vs LSTM vs Conv. The different structures have been used in mix in the paper. Are those choices irrelevant or critical?\n\n3) I would like to see more discussion on the nature of errors from those models, but it's lacking in the paper. This could be critical to understand the model’s ability and limitation, esp given that it’s not looking at supporting evidences from other sequences.\n\nSmall errors noticed: The citation for PHEME paper (Kochkina et al) points to a preprint version, while an ACL Anthology published version exists.", "extra_scores": {"experience_assessment": "I have read many papers in this area.", "review_assessment:_checking_correctness_of_derivations_and_theory": "N/A", "review_assessment:_checking_correctness_of_experiments": "I assessed the sensibility of the experiments.", "review_assessment:_thoroughness_in_paper_reading": "I read the paper at least twice and used my best judgement in assessing the paper."}, "extra_text": {}}, {"review_id": "BygbyqPT5r", "reviewer": "AnonReviewer3", "summary": "", "questions": "", "limitations": "", "rating": 1, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "Three strengths:\n1. This paper has been well written and easy to follow. Adequate details have been provided to help easily reproduce the experimental results.\n2. The technical part is sound - the authors apply GAN for rumor detection and propose to use model-where and model-replace to extend conventional GAN models.\n3. Experiments are conducted on real-world data.\n\nWeaknesses:\n1. Contributions of novelty are limited. The idea of using GAN to detect misinformation such as rumors and fake news has been studied in the literature several times, and the proposed method does not differ from them significantly. The problem of explainable rumor and fake news detection has also been well studied. Therefore, this piece of work is more a marginal extension of existing solutions.\n2. The technical solution can be very limited. The generator can only manipulate content by replacing something from a true statement. The hidden assumption that misinformation is mostly generated by replacing some word definitely underestimates the complicated nature of fake news/rumor detection problem. If the assumption holds, the rumor detection problem can be easily done by collecting and comparing against true statements.\n3. The limited experimental results cannot resolve my concerns. The rumor dataset is very small for a typical deep learning model. I am also curious about how many rumors in the dataset are generated by replacing words.\n", "extra_scores": {"experience_assessment": "I have published in this field for several years.", "review_assessment:_checking_correctness_of_derivations_and_theory": "I assessed the sensibility of the derivations and theory.", "review_assessment:_checking_correctness_of_experiments": "I carefully checked the experiments.", "review_assessment:_thoroughness_in_paper_reading": "I read the paper thoroughly."}, "extra_text": {}}]

Author rebuttal:

Meta-review: The paper is well-written and presents an extensive set of experiments. The architecture is a simple yet interesting attempt at learning explainable rumour detection models. Some reviewers worry about the novelty of the approach, and whether the explainability of the model is in fact properly evaluated. The authors responded to the reviews and provided detailed feedback. A major limitation of this work is that explanations are at the level of input words. This is common in interpretability (LIME, etc), but it is not clear that explanations/interpretations are best provided at this level and not, say, at the level of training instances or at a more abstract level. It is also not clear that this approach would scale to languages that are morphologically rich and/or harder to segment into words. Since modern approaches to this problem would likely include pretrained language models, it is an interesting problem to make such architectures interpretable. 
Decision: Reject