Fact checking COVID-19 fake news
By: Prathyush Potluri
Tags: Machine Learning, Misinformation, Covid 19, Fake News, NLP

For each fact check, we collected only the ”claim” and the corresponding “explanation” from this database which were rated as ’False’ or ’Misleading’. In this way, we collected about 5500 false-claim and explanation pairs. We further manually rephrased a few of these false claims to generate a true claim, as the ones that aligned with the explanation, so as to create an equal proportion of true-claim and explanation pairs.

Model Architecture:

The architecture consists of a two stage model, we will refer to the first model as “Model A” and the second model as “Model B”. The objective of Model A is to fetch the candidate “true facts” or explanations for a given claim, which are then evaluated for entailment using Model B.

Model A is trained on all claim-explanation pairs, as we have a lot more of them, and the task of the Model A is to pick out candidate claims for a given explanation. Model A is trained on a Next Sentence Prediction (NSP) task.

Through our experiments, we find that, on this trained model, if we generate embeddings for a single sentence (either claim or explanation individually) and compare matching [claim, explanation] embeddings using the cosine similarity metric, there is a distinction in the distribution of similarity scores between related and unrelated [claim, explanation] pairs.

Therefore, for faster near real-time performance, we cache the embeddings for all our explanations (knowledge base) beforehand and compute the cosine similarity between the claim and the cached embeddings of the explanations. We fetch the top explanations for any given claim exceeding a certain threshold of sentence similarity as there could be several explanations relevant for a given claim.

The second part of the pipeline is to identify the veracity of a given claim. Model A fetches the candidate explanations while Model B is used to verify whether the given claim aligns with our set of candidate explanations or not. To train Model B, we use a smaller subset of “false claim” and “explanation” pairs from our original dataset, and cross-validate each sample with “true claim” or in other words, claims that align with the factual explanation.

However, this small annotated data is not sufficient to train the model effectively. Therefore, the parameters of the Model A, which was trained on a much larger dataset were used as initial parameters for Model B, and fine-tuned further using our cross-validated dataset. Model B is also trained for the sequence classification task. Essentially Model B computes the entailment between its input claim, explanations pairs.

Figure 2. Model Architecture (source)

We trained and evaluated both Model A and Model B using several approaches based on classical NLP methods as well as more sophisticated pre-trained Transformer models. The flow of the Model A + Model B pipeline is shown in the above figure.

Transformer based Models:

We trained and evaluated three Transformer based pre-trained models for both Model A and Model B using the training strategy described before. As our focus was to ensure that the proposed pipeline can be deployed effectively in a near real-time scenario, we restricted our experiments to models that could efficiently be deployed using inexpensive compute. We chose the following three models — BERT(base), ALBERT, and MobileBERT.

Model A was trained on 5000 claim-explanation pairs on the sequence classification task to optimize the softmax cross entropy loss. This trained model was then validated on a test set comprising 1000 unseen claim-explanation pairs. The training data structure here looks like this.

[claim, relevant explanation, 1], [claim, irrelevant explanation, 0]

Model B was trained on a smaller subset of 800 cross-validated [claim, explanation, label] data, on the same sequence classification task, where the label was assigned based on whether the claim aligned with the explanation — 1 or not — 0. This was validated on 200 unseen data-points. The loss function used was softmax cross-entropy. The training data structure here looks like:

[true claim, relevant explanation, 1] [false claim, relevant explanation, 0]

For baselining we implement classical NLP approaches in our use-case and compare those results with transformer based models. We implement GLoVe and TF-IDF architectures for the classical ones.

Evaluation metrics:

For evaluating the performance of the overall pipeline model, we first evaluate the performance of Model A in its ability to retrieve relevant explanations. For this we use Mean Reciprocal Rank(MRR) and Mean Recall@10, that is, the proportion of claims for which the relevant explanation was present in the top 10 most contextual explanation by cosine similarity and their mean inverse rank.

Here, expA are the top-10 explanations returned by Model A. c_vecA and exp_vecA are the vector representations generated by running claims and explanations individually through Model A and t refers to the threshold. (source)

Once, Model A has retrieved relevant explanations, we evaluate the performance of Model B on computing the veracity of the claim. Here, we only used explanations that exceed an empirically defined threshold in cosine similarity between the query claim and the explanation. Through our experiments, we found that a threshold of the mean standard deviation of cosine similarity over the validation data worked well for picking relevant explanations. For evaluating the accuracy, we take a mean of the output probabilities for each claim, explanationᵢ.

Table 1: Results of implemented models on test set. Table 2: Results of latencies and memory of implemented models. (source)

Table 1 illustrates that Transformer based models are significantly better than classical NLP models. An interesting observation was that some models are better at retrieval of relevant explanations while others have a better classification performance. We find that a combination of the best performing Model A (BERT) and best performing Model B (ALBERT) yielded the highest MRR, Recall@10, and Accuracy on the test set for fact checking.

Table 2 shows the memory usage and latencies of the implemented models. The memory consumption and latency per claim in the classical NLP models were observed to be quite low in comparison to the Transformer based models.

This is expected due to the lower parameter size of the TF-IDF and GloVe models. Among the Transformer based models, MobileBERT had the least latency per claim as expected while ALBERT consumed the least memory. The best performing BERT+ALBERT model utilized a memory of 1398MB and fetched relevant explanations of each claim in 2.471 seconds. The model latencies and memory usage were evaluated on an Intel Xeon — 2.3GHz Single-core — 2 thread CPU.

Observations:

Figure 3. BERT+ALBERT Model Faulty predictions (source)

We however do acknowledge that our models could still make errors of two kinds:

Firstly, Model A might not fetch a relevant explanation which automatically means that the prediction provided by Model B is irrelevant, and secondly, Model A might have fetched the correct explanation(s) but Model B classifies it incorrectly. We show some of the errors our models made in this table.

Conclusions:

In this work, we have demonstrated the use and effectiveness of pre-trained Transformer based language models in retrieving and classifying fake news in a highly specialized domain of COVID-19. Our proposed two stage model performs significantly better than other baseline NLP approaches. Our knowledge base, which we prepare through collecting factual data from reliable sources from the web can be dynamic and change to a large extent, without having to retrain our models again for as long as the distribution is consistent. All of our proposed models can run in near real-time with moderately inexpensive compute. Our work is based on the assumption that our knowledge base is accurate and timely.

This assumption might not always be true in a scenario such as COVID-19 where “facts” are changing as we learn more about the virus and its effects. Therefore a more systematic approach is needed for retrieving and classifying claims using this dynamic knowledge base.

Future Work:

Our future work consists of weighting our knowledge base on the basis of the duration of the claims and benchmarking each claim against novel sources of ground truth.

Our model performance can be further boosted by better pre-training, through domain specific knowledge. In one of the more recent work, the authors propose a novel semantic textual similarity dataset specific to COVID-19. Pre-training our models using such specific datasets could help in a better understanding of the domain and ultimately better performance. Fake news and misinformation is an increasingly important and difficult problem to solve, especially in an unforeseen situation like the COVID-19 pandemic.

Leveraging state of the art machine learning and deep learning algorithms along with preparation and curation of novel datasets can help address the challenge of fake news related to COVID-19 and other public health crises.