Keywords

1 Introduction

The spread of fake news is not a new problem. However, with the advancement of the internet and social media, it has become a growing problem [32]. Fast and uncontrolled spread of fake news can affect society in many ways, including ideological polarization [24] and psychological bias [28]. During the Covid-19 pandemic, we have experienced how problematic the situation can be globally [7, 20]. Despite the ongoing efforts for developing automated fake news detection systems, most of the work is still being done by professional journalists in fact checking organizations all around the worldFootnote 1.

As machine learning is one of the promising techniques for automated fake news detection, one of the obstacles is the amount of accurately labeled training data that are available. Unfortunately, there are very few labeled datasets of sufficient size and quality for supervised learning in this domain. This is due to the scarce resources for manual fact checking and labeling efforts. In addition, because of the new events are introduced continuously, the content and topic of news articles are time-dependent and varied [4]. Weakly supervised learning, a new machine learning paradigm, has been developed to work with low-quality labels called weak labels [19].

In this paper, we present a fake news detection system that uses weakly supervised learning based only on content features. We have chosen to work with full news articles instead of social media posts or shares. Even though the social media is seen as the primary source of the spread of fake news, recent research [27] points out the importance of the coverage of fake news in mainstream media.

Weakly supervised systems can utilize content and contextual features. Previous work using this approach for fake news detection has given promising results [10]. However, the contextual features used in these efforts (e.g. likes, comments, shares) are time-dependent (they change over time), take time to accumulate, and are unavailable for some articles. Therefore, our approach is solely based on the content-based features extracted from the title and content of the articles.

To the best of our knowledge, this is the first work that uses weak supervision for fake news detection by using only content features. Our contributions are three-fold: We introduce a probabilistic weak labeling system that relies only on content features. We collect and present a test dataset from a set of fact-checking organizations including SnopesFootnote 2 and PolitiFactFootnote 3. The dataset has been made publicly available on GithubFootnote 4. We apply five machine learning classifiers for fake news detection with and without the weak labels to investigate the efficiency of using weak supervised learning with content features.

The rest of the paper is organized as follows: Sect. 2 discusses the state of the art and existing work on weakly supervised methods for fake news detection. Section 3 presents the dataset for the experiments. Section 4 outlines the proposed architecture and experimental design, and presents the findings. Section 5 concludes and gives an outlook to future research directions.

2 Related Work

Even though the fact checking tasks still rely on the professional journalists, the efforts of developing automated fake news detection systems has been in focus of the researchers for the last couple of years. Within these research there is a wide variety of approaches. Crowd-sourcing has been proposed to obtain the labels for fake news [6, 17]. However, human annotators can process a limited number of articles. [6] had 90 articles annotated, whereas [17] acquired labels for 240 articles. Crowd sourcing suffers from high costs and doubts in annotations’ quality. [35] argues that with a large enough population of fact-checkers, indicating the credibility of articles remains feasible. How to attract and motivate a large enough population remains to be seen. Research on fully automated methods follows different approaches such as content-based, user-based, network-based, and hybrid methods which use the combination of other methods [15]. Content-based methods focus on the analysis of text and non-text content, such as video or sound. For instance, Shrestha et al. [21] combine textual features, sentiment, writing style and psycho-linguistics to identify fake news. User-based methods look at user behaviour and comments to identify fake news [26]. Wang et al. [31] combines a weakly supervised approach with user reports. Network-based methods monitor network activity, which can help to detect bots and investigate the spread patterns. Conversely, Shu et al. [23] reports that humans spread more fake news than bots. In this case, finding out if a user is a real human may help to increase the clues a system collects. Moreover, [23] shows that fake news is more likely to be spread by fake accounts. The computational methods used in the automated detection of fake news is varied. Castelo et al. [4] proposes a topic-agnostic approach to the classification of fake news by using web-markup in addition to LIWC (Linguistic Inquiry and Word Count), and stylistic features. With this approach, they focus on identifying the non-credible web pages spreading fake news instead of detecting individual fake news articles.

Weakly supervised learning (mainly together with contextual features) has been used for fake news detection by many researchers. Helmstetter and Paulheim [10] apply weakly supervised learning to microblogs for detecting fake news in social media and obtain an accuracy of approximately ninety percent. Wang et al. [30] uses reinforcement learning for fake news detection with the use of crowd-sourced labels. Yuan et al. [34] combine weakly supervised learning with a structure-aware multi-head attention network to identify fake news. Weakly supervised learning has been used with content features for tasks such as learning discourse structures in dialogues [2] and building a text classifier in combination with transfer learning [25].

3 Dataset

To choose the best suited dataset for this task, we have reviewed 14 datasets. Table 1 presents an overview of these datasets. Our evaluation considered four properties: size, features, class balance, and labeling method. As a result, we have decided to use the NELA-GT-2019 [9] datasetFootnote 5. The chosen dataset has a large amount of data for all classes. Thus, it supports creating class-balanced subsets. The dataset’s features include title and content. The documentation of the dataset is excellent. NELA-GT-2019 comprises 1.12M news articles from 260 mainstream and alternative news sources. It has been collected between 01 January 2019 and 31 December 2019. There are four different labels: reliable, mixed, unreliable, and unknown. In this work, we consider articles labelled reliable as credible news and unreliable as fake news. We discard the labels mixed and unknown. The labels in the dataset have been assigned based on the credibility of news sources which does not guarantee the correctness of information itself.

Dataset Collection. For more realistic assessment of the developed models, we have collected an independent dataset which consists of manually fact-checked news articles. So the labels of the news articles in this test set are not based only on the sources they were published on, but on the decision of professional fact checkers. In addition, we payed attention to the publishing date of articles to avoid testing our models on the same news items as were included in the training dataset. Therefore the articles collected for this dataset were published in a different period than NELA-GT-2019. During the collection of this dataset we have used entries from FakeNewsNetFootnote 6 [22] and MisInfoText [1] datasets as well as manual collection of articles from Snopes fact-checking archivesFootnote 7. The collected dataset includes 434 news articles where half of them is fake and the other half is real news articles. This dataset is available on GithubFootnote 8.

4 Architecture, Experiments and Results

The proposed system consists of two main components: The weak labeling system and the classification models that use weakly supervised learning. For each of these main components we ran a series of experiments in order to find the best performing models. Then we combine these in our proposed architecture. Figure 1 shows the overall architecture of the proposed system.

Fig. 1.
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Overall system architecture

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First we apply pre-processing and feature engineering to the raw data. Then the output is passed to the weak labeling system which generates weak labels. After the application of document representation, weakly labelled data is passed to the end model. We have experimented with Snorkel and Snuba, two weak labeling frameworks, and five classifiers: Logistic Regression, XGBoost [5], ALBERT [12], XLNET [33], and RoBERTa [13].

Table 1. Fake news datasets reviewed in this work.
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In the following sections, all these steps are explained in detail and the results from various experiments are presented.

4.1 Data Pre-processing

The pre-processing steps includes applying natural language processing (NLP) techniques such as normalization, stop word removal, and tokenization to the news text. More specifically we have normalized the text, removed punctuation, digits and stop-words, and tokenized into words, bigrams, trigrams and sentences. We used the NLTK word tokenizerFootnote 9, NLTK sentence tokenizerFootnote 10, NLTK part-of-speech taggerFootnote 11, WordNetLemmatizerFootnote 12 and Python’s built-in lowercasing function. Each step has been applied to both the title and the body of the articles.

4.2 Feature Engineering

After the pre-processing step, we have determined four types of relevant features based on the literature: stylistic features, complexity features, POS-tagging features, and sentiment features [3, 11, 16, 18].

Stylistic features include author’s writing style such as the use of exclamation marks and uppercase words; complexity features include implicit features of the text such as type-token ratio and words per sentence; POS-tagging features include all POS-tag related features such as the presence of verbs, nouns, and adjectives; finally, sentiment features include the sentiment scores of the text such as the scores for subjectivity, positiveness, and negativeness.

In total, we have used 68 extracted features such as “ratio of stop words”, “number of quote marks”, “ratio of nouns per word” and “document negative score based on sentences”. A complete list of these features can be found in [8]. For each of these features, the resulting numerical values such as the sentence count, word count etc. are then passed as an input to the weak labeling system.

4.3 Weak Labeling System

The first main component of our architecture focuses on the generation of weak labels for fake news detection. For this, we consider two weak labeling frameworks: SnorkelFootnote 13 and SnubaFootnote 14. We have run a set of experiments to compare these two frameworks in the context of fake news detection. Figure 3 shows the overall pipeline of the experiments to evaluate the weak supervised fake news detection.

During our experiments with Snorkel, in order to enhance the performance, we have developed three components for the weak labeling system: Automatic threshold search, automatic labeling function (LF) generation, and labeling function (LF) selection. In order to create this weak labeling system we have used a small portion of the labeled data we have which is not included in the evaluation of the end models to prevent the data leakage. Figure 2 shows the overall pipeline.

Automatic threshold search takes the instances described with descriptive statistics (such as title word count) as input and selects best feature values (thresholds) that define an instance being fake or real. Automatic LF generation component handles the automatic generation of labeling functions in Snorkel. The labels are assigned automatically based on the thresholds defined in the previous step by checking if a feature value of an instance is above or below the threshold. It is also important to find values that cover a large portion of the data set since the higher the coverage the higher the amount of labels assigned is. LF selection component handles the possible extremely noisy labels by selecting a portion of LFs. To do that, we evaluated three sets of LFs by using Snorkel’s generative model and majority vote approaches: All LFs (All), LFs with an individual accuracy above 65% (Acc > 65%, this value has been chosen as a result of separate experiments) and top 25 LFs based on their accuracy (Top 25).

As a result of our experiments, we have found that the best performing model was Acc > 65% with an accuracy of 0.710 and coverage of 0.860. More details of these components can be found in [8].

Fig. 2.
figure 2

The pipeline of the automatic weak labeling system in Snorkel. The purple color indicates the components developed in this work. The white color indicates preliminary processing, yellow color indicates the processes handled by Snorkel and the gray color indicates the input and output of the system. (Color figure online)

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Snuba framework has been proposed by [29] and it creates heuristics that assign probabilistic labels to instances. Compared to Snorkel, it generates less noisy labels and provides more diversity of instances labeled. In this work we have implemented a weak labeling system using Snuba and tested it with tree types of heuristics, namely decision trees, logistic regression and k-nearest neighbor (KNN). Following the findings of [29] which suggested that the maximum cardinality below four would be sufficient for most real-world tasks, we have experimented with the values below four. Due to the hardware limitations we could not get any results from KNN max cardinality three. The results from these experiments are shown in Table 2. Based on these results we have chosen the best method based on accuracy and coverage. Note that the portion of the data set we have used for these experiments does not contain the data from the weak label generation part to prevent data leakage.

Table 2. The results from the experiments with different types of heuristics of Snuba.
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As a result of our experiments with Snorkel and Snuba, we found that Snuba achieves an accuracy of 0.765 and coverage of 0.902, outperforming Snorkel both in terms of accuracy and coverage. We explain this with Snuba’s heuristics being more complex than Snorkel and taking the heuristic’s diversity into account. Therefore we use Snuba as our weak labeling component. Then, we run the best performing weak labeling system on the manually labeled test set to assure that the classifiers would perform better than the weak labeling system so that it is reasonable to train end models. We observed that Snuba, DT, 3 achieved an accuracy of \( 0.646 \), \( F_1 \) score of \( 0.668 \) and coverage of \( 0.956 \).

4.4 Document Representation

Classifiers require the input to come in the form of numerical vectors. We experiment with two different methods to obtain such vectors from the output of the weak labeling system: TF-IDF and BERT-specific. BERT-based models are designed to deal with raw text which reduces the processing to two simple steps. First, we merge the articles’ title and content. Second, we trim the text to conform to the maximum length of token supported by the models. For Logistic regression and XGBoost, we used TF-IDF with an array size of \( 6000 \).

4.5 Weakly Supervised Learning

We have trained five models—Logistic Regression, XGBoost, ALBERT, XLNet, and RoBERTa—to determine the best performing classification model for weakly supervised learning in this domain. We have chosen these models based on their previous success for fake news classification [14]. We have also trained the same models as supervised end models for the comparison. Table 3 shows the size of datasets used in this experiment. As it is shown in Fig. 3, both weakly supervised models and supervised models take a portion of the labeled data as input. The weakly supervised models take the weakly labeled data from the weak labeling system as an additional input.

Table 4 presents the results from our experiments with these models using weak labels. Results show that RoBERTa outperforms the four other classifiers, reaching to an accuracy of 0.753, F1 score of 0.779 for supervised and an accuracy of 0.779, F1 score of 0.798 for weakly supervised method on the manually created test set. The second best performing model in this setting is the XLNet with an accuracy of 0.719, F1 score of 0.742 for supervised and an accuracy of 0.733, F1 score of 0.752 for the weakly supervised method. Results of these experiments show that weakly supervised method performs slightly better than the supervised approach. These results also suggest that the combination of weak labeling system and classifier perform better than the weak labeling system alone as it was explained in Sect. 4.3.

Fig. 3.
figure 3

Experimental pipeline for the end models.

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In order to understand how the amount of weak labels introduced affects the weakly supervised model, we have experimented with three different ratios of weak labels. Based on the result of the previous experiment, we have used RoBERTa for both weakly supervised and supervised models. First, we have trained our models with all the weak labeled instances (approx. 170K), and then 50K and 25K weak labeled instances respectively, where the total number of instances in the dataset for this set of experiments is approximately 201K. Table 5 shows the results from these experiments. The results of these experiments indicate that the supervised model performs better than the weakly supervised method. As we keep adding more weak labeled data the performance decreases. The weak labeled instances are selected by confidence. This suggests that high-confidence labels contribute best to the detection, whereas low-confidence labels spoil the performance. However, results also show that the difference between these models, (especially the supervised, weak 25K and weak 50K) is marginal. Given that we have tested our system with only one test set, we do not know how the results would change for other datasets. Additionally, our test set is relatively small compared to the training set (see Table 3). We expect weakly supervised models to perform better in conditions where the test set is similar or larger in size as the training data set. We believe that weakly supervised learning for fake news detection is a promising method and should be explored further. Also more research is required to verify the effect of weakly labeled data for fake news detection.

Table 3. Size of datasets used.
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Table 4. Comparison of classifiers. For each of the five classifiers, we list the scores on the manually created test set, as well as the difference between the usage of weakly supervised labels. The rows refer to Logistic Regression (LR), XGBoost (XG), ALBERT (AL), XLNet (XL), and RoBERTa (Ro).
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Table 5. The comparison of supervised and weakly supervised models with different ratios of weak labels.
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5 Conclusions and Future Work

Automation will remain necessary to combat fake news as long as fact-checkers remain a scarce resource. Fake news classifiers rely on accurate labels. This work proposed and explored the use of weakly supervised learning that relies only on the content features. Our observations on the performance of different weak labeling frameworks suggest that Snuba performs better than Snorkel for this task. As a result of our experiments with five different classifiers, RoBERTa outperformed the other four classifiers both in supervised and weakly supervised tasks. We tested the weak labels’ utility for fake news detection with help of the NELA-GT-2019 data set and a manually created test set where it has been made publicly available. We observed that the more weak labels we introduced, the more the classification performance dropped. However, this decrease is not significant. Therefore weakly supervised learning may be a suitable method to use in the absence of labeled data. More research is necessary to investigate successful ways to blend weak labels without compromising performance.

As a future work, we intend to use additional data sets to verify our findings. Further, we will explore how to effectively use confidence score to estimate weak label’s effect.