A Language-Based Approach to Fake News Detection Through Interpretable Features and BRNN (2020)

Abstract: ‘Fake news’ – succinctly defined as false or misleading information masquerading as legitimate news – is a ubiquitous phenomenon and its dissemination weakens the fact-based reporting of the established news industry, making it harder for political actors, authorities, media and citizens to obtain a reliable picture. State-of-the art language-based approaches to fake news detection that reach high classification accuracy typically rely on black box models based on word embeddings. At the same time, there are increasing calls for moving away from black-box models towards white-box (explainable) models for critical industries such as healthcare, finances, military and news industry. In this paper we performed a series of experiments where bi-directional recurrent neural network classification models were trained on interpretable features derived from multi-disciplinary integrated approaches to language. We apply our approach to two benchmark datasets. We demonstrate that our approach is promising as it achieves similar results on these two datasets as the best performing black box models reported in the literature. In a second step we report on ablation experiments geared towards assessing the relative importance of the human-interpretable features in distinguishing fake news from real news.

## introduction the topic of 'disinformation' -an umbrella term used to encompass a wide range of types of information disorder, "including 'fake news', rumors, deliberately factually incorrect information, inadvertently factually incorrect information, politically slanted information, and 'hyperpartisan' news" @xcite -is attracting more and more attention. this reflects a deeper concern that the prevalence of disinformation leads to an increased political polarization, decreases trust in public institutions, and undermines democracy. for example, the spread of 'fake news' -concisely defined as intentionally false information masquerading as genuine news -for financial and political gains had a potential impact on the contentious brexit referendum or 2016 u.s. presidential elections @xcite . against this background, it is hardly surprising that there has been an increased interest in the development of methods, measures and computational tools that efficiently and effectively detect disinformation using machine learning and deep learning techniques. among different approaches to fake news detection, language-based approaches have emerged as promising (for more details, see section 2). here the term 'language-based' is used in a broad sense to include a variety of approaches, such as those that employ traditional linguistic features, readibility features, style-based features, discourse and rhetorical features or those that draw on word embedding techniques. the latter have proven to be particularly successful in detecting fake news. despite their success, however, their detection is based on latent features that are not human interpretable and thus cannot explain why a piece of news was detected as fake news. as recently pointed out by @xcite , white-box (explainable) approaches to fake news detection are desirable, since model-derived explanations can (1) provide valuable insights originally hidden to different stakeholders, such as policy makers, professional journalists and citizens and (2) can contribute to further improvement of fake news detection systems. this paper seeks to respond to recent calls for more explainable (white-box) approaches to fake news detection by performing a series of experiments where bi-directional recurrent neural network classifiers were trained on interpretable features derived from multi-disciplinary integrated approaches to language. the data come from two benchmark datasets and fake news detection is formulated as a binary classification and as a multiclass classification tasks correspondingly. the results of our experiments are promising, as our classification models achieve similar performance as the best-performing black box models reported in the literature. in a second step we report on ablation experiments geared towards assessing the relative importance of the human-interpretable features in distinguishing fake news from real news. the remainder of the paper is organized as follows: after a concise overview of related work in section 2, section 3 introduces the two data sets, section 4 describes our approach to automated text analysis and six groups of language features used in the paper, section 5 describes the model architecture, the training procedure and the method used to assess the relative feature importance. sections 6 presents and discusses the main results and concluding remarks follow in section 7. ## related work here we provide a concise overview of recent approaches geared towards fake news detection that employ machine learning and deep learning techniques and we focus in particular on language-based approaches that are most pertinent to the purposes of this paper (for a more systematic and comprehensive overviews, see recent reviews and surveys by @xcite , @xcite , @xcite and @xcite . fake news detection is most often formulated as a binary classification task. however, categorizing all the news into two classes (fake vs real) is not the only conceivable way, since there are cases where the news is partially real and partially fake. a common practice is to add more classes distinguishing between several degrees of truthfulness and thus formulating fake news detection as a multi-class classification task. as will become evident later in this paper, we apply our approach to both scenarios. three approaches to fake news detection frequently described in the literature are: (1) knowledge-based fake news detection (commonly using techniques from information retrieval to determine the veracity/truthfulness of news), (2) language-based fake news detection (drawing on traditional linguistic, style-related, readability or rhetorical features or using word embedding methods to distinguish between fake and real news) and (3) propagation-based fake news detection (typically using network analyses to determine the credibility of news sources at various stages, being created, published online and their spread via social media). compared to knowledge-based and propagation-based approaches, language-based approaches are advantageous for several reasons, including: (1) they enable near real-time feedback (proactive rather than retroactive), i.e. they are not restricted to being applied only a posteriori @xcite and (2) they are scalable. a guiding assumption of language-based approaches is that there are statistical regularities inherent in natural languages and distributional patterns of language use indicative of fake news that are not consciously accessible to fake news creators. space limitations prevent us from going into further details (but see reviews and survey cited above). in what follows, we will zoom in on previous studies on fake news detection conducted on the bases of the publicly available benchmark datasets used in the corpus study: the isot dataset, an 'entire article' dataset comprising 20k+ real and fake news texts @xcite , and the liar dataset, a 'claims dataset' comprising 12k+ real-world short statements collected from a variety of online sources (wang, 2017) (see section 3 for details). upon introduction of the isot dataset, @xcite report on the results of experiments using n-gram features with two different features extraction techniques -term frequency (tf) and term frequency-inverted document frequency (tf-idf) -and six different machine learning techniques -stochastic gradient descent, support vector machines, linear support vector machines (lsvm), k-nearest neighbour and decision trees. their best-performing model reached a classification accuracy of 92% using tf-idf for feature extraction and an lsvm classifier, showing that real and fake news can be discriminated with high accuracy on the basis of the use of multiword sequences. however, subsequent studies have demonstrated that classification accuracy on this dataset can be pushed even higher -beyond the 99% accuracy mark -through the employment of deep neural networks trained on word embedding vectors: @xcite reported classification accuracy between 95.04% and 99.86% using an lstm neural network trained on different word em-beddings (glove, news, twitter, crawl) implemented in the flair nlp framework @xcite . @xcite achieved a classification accuracy of 99.8% using a non-static capsule network and 'glove.6b.300d' word embeddings @xcite . while the dataset isot involves a binary classification (fake vs. real), the liar dataset presents a six-way multiclass classification problem, where individual claims statement was evaluated for its truthfulness and received a much more finegrained veracity label. in the experiments presented upon publication of the liar dataset, (wang, 2017) provided several benchmarks based on several shallow learning classifiers (e.g. logistic regression and support vector machines) trained on n-gram features and deep learning classifiers (bi-directional long short-term memory and convolutional neural networks architectures) using pre-trained 300-dimensional word2vec embeddings from google news @xcite . the latter reached a classification accuracy task of up to 27%. incorporating available meta-data about the subject, speaker and context raised classification accuracy to 27.4%. subsequent studies have shown that the classification accuracy on the liar set can be further increased to just over 45% by more complex hybrid models that integrate the linguistic information with speaker profiles into an attention based lstm model @xcite , by supplementing the data with verdict reports written by annotators @xcite or by replacing the credibility history in liar with a larger credibility source @xcite . importantly, however, all state-of-the-art models designed to detect the veracity of a news article or claim exploit the information contained in high-dimensional word embeddings that are uninterpretable to humans, thereby severely limiting our ability to understand 'why' a given claim or news article was predicted to be fake or real. ## data the experiments were conducted on two recently released datasets for fake news detection, the isot dataset compiled by the information security and object technology research lab @xcite and the liar dataset introduced in @xcite . the datasets were selected based on their complementary attributes in terms of text types (full articles with average length of about 400 words vs. short statements with an average length of just under 20 words) and the granularity of the veracity labels (binary labels based on source selection and six-way classification based on ratings by politifac.com editors). both datasets are sufficiently large for training deep models. the isot dataset consists of 40,000+ real and fake news articles collected from real-world sources between 2016 and 2017. the real (truthful) news articles were obtained by crawling articles from reuters.com. the fake news articles were collected from unreliable websites that were flagged by politifact.com, a fact-checking organization in the usa, and wikipedia. the isot dataset contains articles on a variety of topics with a focus on political and world news topics (see ## automated text analysis the raw texts from the two datasets were automatically analyzed using cocogen, a computational tool that implements a sliding window technique to calculate within-text distributions of feature scores (see recently published papers that use this tool, @xcite @xcite . in contrast to the standard approach implemented in other tools for automated text analysis that rely on aggregate scores representing the average value of a feature in a text, the sliding-window approach generates a series of measurements representing the 'local' distributions of scores. a sliding window can be conceived of as a window of size ws, which is defined by the number of sentences it contains. the window is moved across a text sentence-by-sentence, computing one value per window for a given feature. the series of measurements faithfully captures a typically non-uniform distribution of features within a text and is referred here to as a 'contour'. 1 to compute the value of a given feature in a given window m (w(m)), a measurement function is called for each sentence in the window and returns a fraction (wn m /wd m ). cocogen uses the stanford corenlp suite @xcite for performing tokenization, sentence splitting, part-of-speech tagging, lemmatization and syntactic parsing (probabilistic context free grammar parser @xcite ). in its current version, cocogen supports a total 154 of features that fall into six categories: (1) features of syntactic complexity (@xmath1, (2) features of lexical density, sophistication and variation (@xmath2, (3) information-theoretic features (@xmath3, (4) register-based n-gram frequency features (@xmath4, (5) liwc-style (linguistic inquiry and word count) features (@xmath5. a brief overview of the features and their short descriptions are provided in in which @xmath11, y 2 , . . . , y n ), @xmath12, ŷ2 , . . . , ŷn ) is the output vector of the sigmoid layer rounded to closest integer. we tuned all hyperparameters on the validation set using a grid search over sets of optimizers @xmath13, adagrad, rmsprop}, learning rates @xmath14.01, 0.001, 0.0001} and normalization methods @xmath15, min-max}. the optimal hyperparameter combinations are provided in to determine the relative importance of the language features groups, we conducted feature ablation experiments. classical forward or backward sequential selection algorithms that proceed by sequentially adding or discarding features require a quadratic number of model training and evaluation in order to obtain a feature ranking @xcite . in the context of neural network models, training a quadratic number of models can become prohibitive. to alleviate this problem, we used an adapted version of the iterative sensitivity-based pruning algorithm proposed by @xcite . this algorithm ranks the features based on a 'sensitivity measure' @xcite and removes the least relevant variables one at a time. the classifier is then retrained on the resulting subset and a new ranking is calculated over the remaining features. this process is repeated until all features are removed. in this fashion, rather than training n(n+1) 2 models required for sequential algorithms, the number of models trained is reduced to n m , where m is the number of features or feature groups that can be removed at each step. we report the results obtained after the removal of a single feature group at each step. at step t, a neural network model m t is trained on the training set. the training set at step t consists of instances with feature groups f @xmath16, f 2 , . . . , f dt } where f 1 , . . . f dt are the remaining feature groups at the current step, whose importance rank is to be determined. we define x t as the test set with feature set f t and x i t as the same dataset as x t except we set the i th feature f i of each instance within the dataset to its average. furthermore, we define g(x) as the classification accuracy of m t,n for a dataset x. the sensitivity of a feature group f i at step t is obtained from: then we set the rank for feature f î: in the end, feature f î is dropped from f t and the corresponding columns in training and test dataset are also dropped simultaneously: ## results the performance metrics of the classification models for both datasets (global accuracy, precision and recall) are presented in of the delta scores revealed some interesting patterns. for example, real news articles and claims are characterized by (1) relatively higher lexical diversity (as measured by type-token ratio features), (2) stronger reliance of multiword sequences from the news and academic register (measured by registerbased n-gram frequency measures), (3) greater phrasal syntactic complexity (as measured, e.g., by the number of complex nominals per clause) and (4) more frequent use or word from particular domains, such as work, money, power or word classes, such as preposition and quantifiers. in contrast, fake news are characterized by (1) greater syntactic complexity (as measured by, e.g. by the number of clauses per sentence), (2) frequent use of multiword sequences form the domain of fiction, (3) higher lexical sophistication scores (as measured in terms of relatively infrequent words) and (4) a strong reliance on personal pronouns, adverbs and emotion words. while limitations of space preclude an in-depth discussion, these results demonstrate that the use of interpretable features can provide new insights and knowledge about the characteristics of fake news and explain "why" a piece of news was detected as fake news see @xcite for a discussion of explainable fake news detection. ## conclusion and future work in recent years, there is a growing recognition of the need to move away from black-box models towards white-box models for solving practical problems, in particular in the context of critical industries, including healthcare, criminal justice, and news @xcite . this is due to the fact that human experts in a given application domain need both accurate but also understandable models (loyola-gonzalez, 2019). in this paper, we have made a contribution to this development in the domain of fake news detection. we have demonstrated that models trained on human interpretable features in combination with deep learning classifiers can compete with black box models based on word embeddings. in the future we intend to extend this work in two directions: first, we plan to apply our approach to fake news detection in german whose research still lags far behind that available for english. second, we also plan to apply our approach to the detection of rumours and conspiracy theories to tackle and combat the ongoing covid-19 infodemic.