Fighting Filterbubbles with Adversarial BERT-Training for News-Recommendation
Authors: Lukas Pfahler
Katharina Morik
Venue:  (2021)
Keywords: Adversarial Learning
Natural Language Processing
BERT
News Recommendation
Attention

Abstract: Recommender engines play a role in the emergence and reinforcement of filter bubbles. When these systems learn that a user prefers content from a particular site, the user will be less likely to be exposed to different sources or opinions and, ultimately, is more likely to develop extremist tendencies.
We trace the roots of this phenomenon to the way the recommender engine represents news articles. The vectorial features modern systems extract from the plain text of news articles are already highly predictive of the associated news outlet. We propose a new training scheme based on adversarial machine learning to tackle this issue . Our  experiments show that the features we can extract this way are significantly less predictive of the news outlet and thus offer the possibility to reduce the risk of manifestation of new filter bubbles. We validate our intuitions in a news recommendation task using a recent attention-based recommendation system.

Under review as a conference paper at ICLR 2021
FIGHTING FILTERBUBBLES WITH ADVERSARIAL BERT-TRAINING FOR NEWS-RECOMMENDATION
Anonymous authors
Paper under double-blind review
ABSTRACT
Recommender engines play a role in the emergence and reinforcement of filter bubbles. When these systems learn that site, the user will be less likely to be exposed to different sources or opinions and, ultimately, is more likely to develop extremist tendencies. We trace the roots of this phenomenon to the way the recommender engine represents news articles. The vectorial features modern systems extract from the plain text of news articles are already highly predictive of the associated news outlet. We propose new training scheme based on adversarial machine learning to tackle this issue.. Our experiments show that the features we can extract this way are significantly less predictive of the news outlet and thus offer the possibility to reduce the risk of manifestation of new filter bubbles. We validate our intuitions in news recom- mendation task using a recent iitention-basee recommendation system.
prefers content from particular
INTRODUCTION
Recommender systems are a source of unwanted societal effects like filter bubbles or echo chambers, iee. situations where users reinforce their own beliefs by only engaging with content that corresponds to their world views (Pariser, 2011). This effect is also driven by algorithmic news recommenda- tions, for instance on Facebook (Bakshy et al., 2015). Typically, these automatic recommendations preferences and properties of the content: The sys-
based the combination of inferred tem learns which properties a user wants to see in the content he consumes. When working with texts, like news articles social media posts, content properties features extracted with large neural network architectures. A prominent example of these neural representations are bidirectional encoder representation from transformers, BERT (Devlin al., 2019), which are for instance applied in Google search'.
often represented by numerical
We investigate the space of feature vectors induced by BERT models in the domain of news articles and ask if the extraction of features is already a source of societal and cultural divides. We discover that the features are predictive of the news source and hence content recommended based on similar- ities is likely to come from the same source also, thereby possibly promoting the filter bubble effect. For this study we assume that the news outlet is good proxy feature for the political orientation or world-view of given news article. Hence, to address this issue, we propose to apply adversarial learning to compute features that are less predictive of their corresponding news source but are still informative of the semantics of the text. Our adversarial training schemes allow for efficient training of BERT models with adversarial heads.
We begin this paper by briefly discussing the data set and the BERT model use in this study. Then we describe our novel adversarial training procedure that trains our model, such that the vectorial features it extracts are less predictive of the news outlet. We evaluate our model in a two-fold vali- dation: First we investigate the quality of our new BERT feature representations in isolation using a large corpus of news articles. Second, we investigate the BERT features in news recommendation setting using a large dataset of real-world user interactions. We conclude our paper with discussion of our results.
//bl::///loe /bl
bert/

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Under review as a conference paper at ICLR 2021
2 RELATED WORK
Adversarial Training has been used in many applications. It was introduces by Goodfellow et al. (? in the context of generative image modeling, where generator neural network generates images that are hard to separate from real images for discriminator network. Since then, this interplay between two adversaries, the generator and the discriminator, has been used many appolications. Adversarial learning is a popular choice for tackling many problems with machine learning systems. Prominent examples include learning privacy-aware feature representations, that exclude sensitive private information like gender race from learned representations. An important subdomain is applying these methods to image data in order to modify images in such a way, that it shadows information like race, age location from the machine learning systems. Recently, Li et al. 2019 have used an adversarial approach to obfuscate privacy-relevant attributes in images using adver- sarial learning. But also in other data domains, this becomes increasingly important. The recent work of Lia et al. shows that we can obfuscate privacy-relevant information from user data while maintaining all other relevant information in a feature vector. Prior to this, Pittaluga et al. (2019) have demonstrated a similar result. In our study, this private attribute is the news outlet.
In this study we use the adversarial training to train neural text representations that make hard to guess their authoring news outlets. Filter Bubbles can also be targeted from the user side. Using techniques from data obfuscation, users can "hide" their true preferences from a system by consum- ing media from opposing viewpoints. This obfuscation can be done data-driven as demonstrated by and Strucks et al. 2019, where an algorithm suggests the most effective items to interact with to trick the system.
ADVERSARIAL TRAINING OF BERT MODELS
For feature extraction, we rely on a DistilBERT model Sanh et al. (2019), a more resource-efffcient variant of the original BERT deep network architecture. The model takes a sequence of tokens produced by the sentence-piece tokenizer 3 T1, and, in a sequence of 6 transformer layers (Vaswani et al., 2017), produces an output sequence 21, ... 21 where each training stage, the model is trained solve masked language modeling task, where 15% of the input tokens are shadowed and we learn to predict the masked inputs x; based on the output vector 24.
IR 768 At the
We use model by HuggingFace (Wolf et al., 2019) that pretrained on the Toronto Book Corpus as well as the English Wikipedia. Then we fine-tune this pretrained model on the masked language modeling task using paragraphs from our training data to adapt to the content and style of news articles. This fine-tuned model will act the baseline in our study.
At inference time- following standard practice (e.g. (Xiao, 2018)) we compute embeddings of the respective news articles by computing the mean embedding on the output sequence. The BERT model does not process full texts, but only sequences of up to length 512. To mitigate this, process each paragraph individually, cropping excess tokens, and also average these paragraph vectors into a document vector. This way we reduce the full text, including headline, to a 768- dimensional vector that could be fed into recommendation system.
We propose a machine learning model comprised of two parts, in accordance with the terminology commonly used in adversarial learning we call them generator and discriminator. The task of the generator model is to produce vectorial features of the tokenized text that contain little information about the news outlet. The discriminator tries to predict the news outlet of the text.
fgen( ri) = BERTTII,, 21; θ)
fai (2).)))).,, - o(1- W x))
Both models act as adversaries: While the discriminator tries to classify feature vectors correctly by learning V and W. the generator learn the BERT parameters θ to produce features that allow reconstruction of masked tokens while not allowing the discriminator to correctly classify the output. This is captured in the different optimisation functions associated with the models. The generator
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tries to achieve small loss the masked masked-language model task while simultaneously forcing the discriminator to suffer a high classification loss:
Ligen (maak (geu)) - felassification (Fais fgea)).
(1)
On the other hand, the discriminator tries correctly predict the news outlet by minimising its classification loss:
Ldis = Lelassification (Sdis fgen).
We alternatingly minimize Tggen with respect θ and Tddia with respect V, W. Following Good- fellow et al. Goodfellow et al. (2014), we perform k updates of the discriminator for one update of the generator. Furthermore we pretrain the discriminator for one epoch to give head-start and begin the adversarial training with a good discriminator.
There are two variants of training the generator: Instead of using the negative classification loss of the discriminator for the generator training objective (1), we also propose to use the entropy of the classifier
Lent = mmakk (fgea) +
Σ loga where = softmax [(fdia fgen) (z)],
(3)
This method has the advantage of not needing ground truth label information, when the label infor- mation is only available on subset of the data. Then we can train the discriminator on subset of data and use the data for the generator.
ISOLATED EXPERIMENTS
In our first experimental analysis, we evaluate the adversarial text embedding on its own. We have investigate hypothesis: a) The features obtained with adversarial training contain less in- formation about the media outlet than the features from the plain model and b) the features from adversarial training are still useful representations of text.
Quality Measures In order to measure these effects we define quantitative measures. To measure how well the features predict the news outlet, test three different machine learning models on the classification task and report estimates of their performance. We report the perplexity of
logistic regression evaluated by 10-fold stratified cross-validation. which resembles the discriminator in our training algorithm,
one hidden-layer feed-forward network evaluated by 10-fold stratified cross-validation, which is more powerful discriminator than used during training, and
random forest classification also evaluated by 10-fold stratified cross-vans voss--valiattion.
The hyperparameters of these evaluation metric classifiers were optimized in a grid search.
To measure of the adversarial features still capture semantic relatedness, we use twoadifferent simi- larity metrics for texts and check their scores for all nearest neighbor pairs under Cosine similarity of BERT-fcature vectors. We use pretrained Glove word embeddings and compute the Cosine sim- ilarity of the resulting document embeddings using the spacy library (Honnibal & Montani, 2017).
Dataset Schubert et al. (2018) have crawled collection of news articles from a wide variety of publishers starting in 2017 up to January of 2020 and kindly have provided us access. We filtered this collection to include only English articles from the 100 most frequent news outlets. Unfortunately we are not at liberty to share this data due to copyright regulations. We split this dataset into training and test data by using all the articles crawled in the last 4 month well as articles crawled on 33% of the remaining days for testing and the remaining data for training. This way we obtain a training set of 2,998,001 articles and test set of 377,440 articles.

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Only Fine-Tuning
Adversarial Training
Entropy
Training
Logistic Regression
11.23
39.71
38.76
MLP
2.62
9.00
5.07
Random Forest
15.03
37.61
26.56
Glove Similarity
0.9884
0.9482
0.9559
Table 1: Metrics of our BERT features. The adversarial training makes the outlet less predictable by all three classifiers. Simultaneously, the similarity of nearest neighbors judged by Glove- Vectors does not suffer substantially.
Quantitative Results We report our findings in Table 1. We see that the baseline BERT model produces feature vectors that are very predictive the news outlet: Using a multilayer perceptron the perplexity is 2.62, which indicates very high chance of correctly guessing the news outlet given the BERT embedding. However, by using the adversarial training. we can dramatically increase the perplexity, making correct guess much less likely. Interestingly, the quality of the features for judging the semantic similarity does not seem to suffer dramatically. The entropy-based objective is consistently worse than the classification-loss objective. We note that this effect may change upon introduction of regularization parameter that reweights the two components of the generator losses.
5 NEWS RECOMMENDATION EXPERIMENTS
In this section, we analyze the influence of our adversarial news embeddings the task of news recommendation.
Model To this end we test a state-of-the-an neural network model for news recommendation. The NRMS recommender model (Wu et al., 2020a) based on attention that has been shown achieve great performance on the dataset. The model represents each user by combining its recently clicked news articles using a self-attention layer. Then we model the click probability for a document of user by the dot product of the user-representation and the document representation. The model is trained with cross-entropy loss where the candidate set corresponds to the classes.
We begin by using fixed embedding model where the features of the news articles are pre-computed using our BERT models from the last section. We use the adversarial model based on classification loss rather than entropy, it performed slightly better in the last section. Next, train the rec- ommendation while simultaneously fine-tuning the embedding model with the adversarial training algorithm. In both cases represent each news article by its title and abstract. These shorter texts can be embedded on the fly during training.
Dataset We use the Microsoft News Dataset (Wu et al., 2020b), short MIND, for our experiments with news recommendation. The dataset comes in a large and small variant. The large variant includes million users and 3.38 million behavior logs for training, the small variant includes 50,000 users with 236.344 click behavior logs for training. Each behavior log contains the history of the last clicked news articles of the respective user, well as the set of candidate news articles with labels that indicate whether the user clicked on the candidate in the given session. For the NRMS training, we process the behavior logs as follows: For each positive example in the candidate set, we sample K 16 negative examples from the log. When there are less than 16 negative examples in the log data, we use the full candidate set. We truncate the history contain at most 16 news articles.
To obtain the news outlets for the news articles in the dataset, following the original authors' guide, we crawl the articles that are still accessible online using the available crawler tool. We modify the crawler to also extract the news outlet whenever possible. This way we were able to extract news outlet information for subset of 38983 of the 101,527 news articles in the training data. Unfortu- nately, presumably due to licensing issues, the datasets features mostly news articles from smaller, non-mainstream media outlets, whereas study in Section 4 featured mostly larger organizations.
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Fixed Encoder
End-To-End
Metric
Dataset
Pre-Trained
Adversarial
Pre-Trained
Adversarial
small
0.67
0.59
0.77 pending
0.74 pending
AUC-ROC
large 0.66
0.58
small
4.18
4.36
Diversity
large
4.82
5,23
pending
pending
Table 2: Test AUC-ROC scores and diversity estimates for the different models on the different datasets. We see that the recommendation performance suffers using our adversarial models, how- ever as intended we increase the diversity of the recommendations.
Quality Measures We measure the quality of our recommendations using the area under the ROC curve (AUC-ROC), as proposed by the original authors of the MIND dataset. The adversarial ob- jective and the recommendation objective are at odds when the suggestion of news articles is based on publisher preferences. Hence we can expect digression recommendation performance, par- ticularly when the observed data is biased by the currently rumning system that does not care about reducing the filter bubble effect.
Second, we evaluate the diversity of the news outlets recommended to the users. To this end, for each user in the test data we compute the 100 most probable articles from all of the test articles where news outlet is known using recommender model. We measure the diversity of these recommendations by the entropy of the empirical news outlet distributions. A high entropy indicates high diversity among the recommendations, where uniform guessing would yield an entropy of 6.64. We hope to see more diversity the recommendations of our models trained with the adversarial objective.
that the recommendation performance suffers from using our adversarial news embeddings. However, this decrease perfor- mance is rather small. Also expected, the as this fine-tuning allows the model to learn a representation tailored to the recommendation use- case. Furthermore, learning. This illustrates that we have found a new strategy for increasing diversity in news recom- mendation.
Results We summarize findings in Table 2. As expected
recommendation profits from end-to-end training.
that the diversity in our recommendations increases with adversarial
On a side note: Using the BERT model pretrained on the news article corpus improves the per- formance over using the pre-trained model provided by Huggingface, which achieves AUC-ROC of 0.76 in the end-to-end setting. This illustrates the importance of appropriate training data for self-supervised representation learning.
DISCUSSION AND OUTLOOK
We were able to show that features extracted from news articles using modern deep neural networks are highly predictive of the associated news source. With our adversarial training approach we were able significantly reduce this predictability. However, we are still far away from random guessing. However perfect obfuscation can probably not be achieved without seriously sacrificing the usefulness the features. For instance, an article on finance is more likely to appear in Financial Times than other news outlets. In order to hide this fact from the recommendation engine, we have to hide substantial parts of the content, rendering the features pretty much useless.
A caveat of our study the many hyperparameters in the BERT model, the training process, but also the metrics in our evaluation. We tried to set them according to known best best-practices, but right now results hold only for one particular configuration. Consequently, more thorough, larger investigation is still required. So far, we have not learned the full recommender engine with the adversarial training routine. This is obviously the logical next step.
In the future we want to also investigate other adversarial objective, for instance learning text rep- resentations that longer predictive of the general sentiment of news article. This way
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hope to identify different views the news, e.g. from commentators with different political backgrounds.
Finally should discuss that there are studies that suggest that exposing people opposing views might in fact further promote division (Bail et al., 2018). This is attributed to the fact that views that very far away from ones own views easily dismissed and declared invalid, thereby creating a backfire-effect of further radicalization. To mitigate this, researchers have looked into recommending items that are sufficiently close the user, but stimulate movement, eg. (Starke et al.). We think this is an interesting research direction for follow up studies.
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Reviews:
[{"review_id": "Vjz9VS0KfYg", "reviewer": "AnonReviewer2", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 2, "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": "This paper proposed an adversarial training framework for reducing the predictive ability of the new outlet from the news recommendation system. While deep learning-based news recommendation systems work very well, these systems tend to recommend contents of the same site, and are likely to develop extremist tendencies.\n\nFirst, this paper contains many typos and flaws, e.g., the missing references in Section 2, the fluctuation of reference format style, and the different AUC-ROC metric values for the end-to-end setting in the text and table. These flaws make this paper hard to be read. If this paper will be published, I strongly recommend that authors fix these flaws.\n\nSecond, while this paper starts with a good motivation, I was not sure that preventing the same outlet from adversarial learning procedures. If we want to recommend news from a large variety of sites, the recommendation service may be able to limit the maximum number of news articles from the same site. Therefore, I was not sure the decrease of the AUC-ROC can be compensated by the diversity increase of sites only. It is better if authors can provide evidence where the proposed adversarial settings can actually prevent filter bubbles, for example, by recommending different opinions than before.", "extra_scores": {}, "extra_text": {}}, {"review_id": "FPocKAuhupI", "reviewer": "AnonReviewer4", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 4, "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": "Authors propose an adversarial training task for tackling the problem of filter bubbles in news recommendation problem. The main aim of the paper is to propose a method which can obfuscate the news outlet information embedded in the news article's vectorial representation. They propose an adversarial training based method for it. \n\nThey propose a standard generator - discriminator setting, where the generator is trying to learn the masked language modelling task and the discriminator is trying to predict the news outlet of the news article. The generator simultaneously tries to optimize for the language modelling task and maximize the news outlet prediction loss. The discriminator tries to minimize the outlet prediction loss. \n\nStrong points of the paper:\n\n1) It tackles a very relevant problem in the current news recommendation problem, i.e. filter bubbles. \n2) The paper is well-written with relevant experiments. \n\nWeak points:\n\n1) The paper is a simple application of the well-established adversarial training methods and there is very little technical novelty in terms of the core contributions. Would advise authors to submit this work in an applied data sciences conferences like SIGIR, CIKM, ECIR etc. \n\n2) Authors have clearly put in a lot of efforts and they are praiseworthy, but given the nature of the contributions, submission to a more applied conference is suggested. ", "extra_scores": {}, "extra_text": {}}, {"review_id": "5N4Gd1kBkj0", "reviewer": "AnonReviewer3", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "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 authors use adversarial training in an attempt to diversify recommendations of news articles. The effect on filter bubbles is not clear.\n\nThe authors address the issue of filter bubbles that according to their rationale result from recommendation engines for news articles. Their assumption is, that a news outlet constantly reports the same opinion and thus users should be recommended articles from a more diverse set of outlets to increase the diversity of opinions consumed by an user. To achieve that, the authors propose adversarial training to generate feature representations that encode outlet-specific information less. They evaluate if their obtained representation is less predictive of the outlet and also how this affects the recommendation performance.\n\nA strong point of this paper is the motivation and application area since it tackles a problem with direct societal impact, something that is typically not attempted in general machine learning papers. The technical novelty is minimal, since only well established techniques are applied. No technical novelty would be acceptable if some insights for the application area are generated. Unfortunately, several naive assumptions are being made and the evaluation goals are insufficient to obtain any valuable results:\n1. Outlets are not necessarily biased to one-sided opinions.\n2. Forcing recommendations from a variety of outlets can be achieved much simpler. No reasonable baseline is implemented and evaluated.\n3. The assumption that content from different sources has higher diversity in embedding space is not necessarily true. Different outlets commonly publish identical texts (for instance copied from the same press agencies).\n4. Embedding similarity is not equal to semantic similarity. Bert embeddings capture also syntactic characteristics, which does not necessarily translate to opinions.\n6. The data used to measure the recommendation performance is likely biased towards similar outlets. Thus, a performance drop is obvious.\n\nWhile I really like the general application area and the attempt to tackle such relevant issues, I think the paper is not ready. It needs to be clearer about it's goals, limitations and assumptions. The experimental design needs to be redone to allow to gain any valuable insights. I assume that also a manual analysis is required to confirm any of the hypothesis underlying this research.", "extra_scores": {}, "extra_text": {}}, {"review_id": "2HhLeVS6iW", "reviewer": "AnonReviewer1", "summary": "", "questions": "", "limitations": "", "rating": 4, "confidence": 4, "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 problem, fighting filter bubble, is an interesting and important problem to study for recommender systems. The writing of most parts in this paper are pretty clear and easy to follow. Using adversarial BERT training to solve this problem sounds like an interesting idea.\n\nHowever, the paper has multiple flaws:\n1. Its literature review in Sec. 2 is not complete. It only covers a few work related to adversarial training. In my view, another two lines of work that need to covered are fighting filterbubble/diversity in recommender systems and BERT-based recommender systems.\n2. The model is incremental, which is a direct application of adversarial BERT in the news recommendation domain. Its novelty is not enough, especially for ICLR. \n3. Experiments of this work are quite limited. No SOTA recommender systems or models to fight filter bubbles are compared. There're no experiments using variants of the proposed model, which left a few questions unanswered. For example, is predicting the news outlet the only task that is effective to train the model? In my view, news outlet only does not represent a user's preference well, so it doesn't really reflect filter bubbles. What will the result be if you use news topic as the target? How about other options? How about using muti-label to train the model?\n4. The paper doesn't seem to ready for submission. There're quite a few typos and loose ends. For example, a few citations in Sec. 2  are not in the right format. Several results in Tab. 2 are \"pending\".", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: All reviewers agree that this paper is not ready for publication. In addition to the technical comments, the authors should pay attention to the comments by Reviewer 3 about the naivete of the motivation provided for the work. Filter bubbles (to the extent that they really exist; there is controversy about this) have multifactorial origins. 
Decision: Reject