# Fake News Detection Utilizing Textual Cues

Vasiliki Chouliara, Paraskevas Koukaras, Christos Tjortjis

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Vasiliki Chouliara, Paraskevas Koukaras, Christos Tjortjis. Fake News Detection Utilizing Textual Cues. 19th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Jun 2023, León, Spain. pp.393-403, 10.1007/978-3-031-34111-3_33. hal-05449784

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# Fake News Detection Utilizing Textual Cues

Vasiliki Chouliara, Paraskevas Koukaras, and Christos Tjortjis(✉)

The Data Mining and Analytics Research Group, School of Science and Technology,
International Hellenic University, Thessaloniki, Greece
{vchouliara,p.koukaras,c.tjortjis}@ihu.edu.gr

**Abstract.** Easy and quick information diffusion on the web and especially in social media has been rapidly proliferating during the past decades. As information is posted without any kind of verification of its veracity, fake news has become a problem of great influence in our information driven society. Thus, to mitigate the consequences of fake news and its propagation, automated approaches to detect malicious content were created. This paper proposes an effective framework that utilizes only the text features of the news. We evaluate several features for differentiating fake from real news and we identify the best performing feature set that maximizes performance, using feature selection techniques. Text representation features were also explored as a potential solution. Additionally, the most popular Machine Learning and Deep Learning models were tested to conclude to the model that achieves the maximum accuracy. Our findings reveal that a combination of linguistic features and text-based word vector representations through ensemble methods can predict fake news with high accuracy. eXtreme Gradient Boosting (XGB) outperformed all other models, while linear Support Vector Machine (SVM) achieved comparable results.

**Keywords:** Fake news · Text classification · Linguistic features · Word embeddings · Machine Learning (ML) · Ensemble Machine Learning (EML) · Deep Learning (DL)

## 1 Introduction

Social media have become the main source of information transmission during the past years, as most people have access to news via online channels avoiding the interaction with traditional sources of information. Social networks provide easy access and circulation of information published, but there is no verification of the news that propagate the network. Thus, social media offer a breeding ground for developing and spreading malicious content. It is apparent that people are exposed to a huge amount of fake news daily while the quality of news in general becomes questionable [1].

Fake news is unreliable content that intends to deliberately manipulate public opinion on different subjects, especially political affairs. It is of great concern though, that malevolent content seems to spread faster, compared to real news, and has greater impact on aspects of life, politics, and economy [2]. A more recent example of the huge influence of disinformation in our everyday lives was during the COVID-19 outbreak, which
made the work of health professionals more difficult, while confusing the public and placing millions of individuals at risk [3–5].

Due to the immense impact that fake news has, of critical interest is the ability to detect and distinguish fake from real news in real time. This is challenging for many reasons. First of all, through social media, content is generated and spread fast, which leads to huge amounts of information that need to be validated. Moreover, content is diverse, referring to many different subjects, which makes the task even more complex [6]. It is an established fact that people lack the ability to effectively discern fake content. Studies in social psychology and communications have manifested that human's ability to differentiate fake from real topics is slightly better than chance [7].

On the other hand, fact-checking organizations that try to combat the proliferation of fake news, have limited applicability due to time latency and the quick propagation of information in social media. In recent years, extensive research on establishing an automated framework for online fake news detection has been made, to counter the misinformation diffusion [6]. Many ML models have been proposed in association with text-relevant features (i.e., TF-IDF), text-based linguistic features, visual and social features to automatically detect malicious content adequately.

The main contributions of this work are the following: i) to perform an extensive feature set evaluation aiming at finding the best feature set able to detect fake news, and ii) to evaluate and compare several ML and Deep Learning (DL) models leading to a robust methodology that addresses the fake news classification problem using the best performing feature set.

The following is an outline of the remaining research. Section 2 provides information from the literature related to fake news. Section 3 presents the dataset, data preprocessing, as well as the feature extraction techniques that were utilized to provide a solution to the problem. Section 4 focuses on evaluating the performance of the experiments conducted. Section 5 discusses the findings. Section 6 presents the conclusions of this research and sets some future work suggestions.

# 2 Background

## 2.1 Fake News Related Areas

A plethora of scientific studies on the field of Fake News identifies a variety of false information types. Rashkin, H., et al., in their research, use two dimensions to classify fake news, the quality of the article and the intention of the writer to deceive [8]. This approach is also adopted by many other researchers. In the next section, some of the most common fake news categories are presented.

**Propaganda.** The term propaganda is used to describe the premeditated attempt to manipulate and influence public perceptions. This endeavor to affect the common belief, is achieved through the activation of strong emotions of the targeted audience, the breeding of fear and the projection of simplified ideas [9]. Propaganda is mainly utilized by political entities to impose damage to a particular political party [10]. In recent years, a new term has been widely used, the computational propaganda. The term refers to the
usage of political bots to propagate specific opinions through major social networking applications and manipulate conversations.

**Conspiracy Theories.** Refer to stories that try to interpret an event or situation that invokes a conspiracy. These stories are based on insufficient or false evidence, though they appear to be relatively widespread among citizens, as they can offer a coherent explanation of the given situation. Studies relate belief in conspiracy theories with low self-esteem, distrust in authority and political cynicism [11]. Conspiracy theories have a wide range of topics, from science, health and economy to politics.

**Rumors.** A rumor can be described as a story or a statement whose truth value is unverified [12]. This does not always mean that a rumor is a false piece of information, but rather that its veracity is unconfirmed at the time of posting. The importance and the interest of a topic is highly correlated with the spread of the rumor. Sensitive topics can be a fertile ground for rumors to be generated and spread across social media networks. A great percentage of the research aims to develop systems to detect rumors using supervised, unsupervised and hybrid methods.

**News Satire.** Satire is a form of fake news which employs exaggeration and humor to present a story. Satire news is produced to entertain the audience and not to deliberately spread misinformation. However, this type of news is perceived as credible from many people who usually don't read beyond the headlines. Horne & Adali in their research examined correlations among satire, fake and real news and they concluded that satire news is more closely related to fake than real ones [13].

## 2.2 Fake News Detection Approaches

According to many researchers the methods used for fake news detection can be classified in three categories: a) knowledge-based, b) content-based and c) social context-based.

**Knowledge-Based.** When it comes to the knowledge-based approach, one uses a procedure called fact-checking. The scope of this approach is to compare the knowledge extracted from news articles with known facts to determine its veracity. Existing fact-checking approaches can be classified as expert-oriented, crowdsourcing-oriented and automatic. Expert and crowdsourcing-oriented are manually processes that rely on domain experts or the ‘wisdom of the crowd’ respectively to annotate news articles [7, 14]. Automatic fact-checking is based on NLP and ML techniques as well as network theory and can be segmented into two stages, fact extraction and fact verification [7].

**Content-Based.** Content-based analysis tries to capture the different writing styles between legitimate users and deceivers. Though the deceivers make great efforts to present a story as credible, the style of their language, can often expose them [15]. The goal of this method is to exploit linguistic features from different levels of intricacy of the text or extract information from visual content such as images. The linguistic characteristics can be categorized as follows.

**Stylistic Features:** Try to measure the news creator's language proficiency by examining the syntax, text style and grammar of the article. Common ways of investigating
the differences in syntax is using “Bag-of-words” [6], part of speech tagging (POS) as well as counting the frequency of function words, punctuation, and sensory words [13].

*Complexity Features:* The complexity features are studied in two levels of elaborate-ness: word and sentence level. On a sentence level, the number of words per sentence and the number of clauses per sentence can be examined. Regarding the word level complexity, one can count the total number of words or use the readability indexes, which measure if a reader of a certain level of literacy could understand the text [13].

*Psychological Features:* These features try to captivate the emotions and behaviors of the creators of the news. They are based on well-researched word counts that are associated with psychological processes and sentiment analysis [13].

**Social Context-Based.** Social context analysis is the study that analyzes the characteristics of information diffusion in social media and tries to identify anomalous information. Information can be distilled by user-based features, like the registration age and the number of followers/followees [16] and post-based features that utilize the text of the post to infer the validity of news articles and obtain user stance [14]. Moreover, propagation-based features that utilize context information such as, how fake news propagates in social media platforms can be used to track down fake news [17].

## 2.3 The Impact of Concept Drift

When data interpretation changes over time, this leads to an impact on performance of previously trained models [18]. This notion is referred to as concept drift in Machine Learning (ML) literature. There are two types of drift in the news. The natural drift that can be caused because news differentiates radically over time, and the artificial drift where the writing style used in misleading news can change, as an effort to create content that is equivalent to verified news [19]. Many researchers provided a thoroughly examined survey on the impact of concept drift incorporating content-based features [18, 19]. They concluded that the performance of content-based models declined over time, but in a slower pace than they considered, which led to the deduction that hand-crafted content-based features that are not topic-specific, are robust to changes.

# 3 Research Design

The proposed methodology concerns the amelioration of the state-of-the-art techniques for detecting fake news by utilizing linguistic features and word vector features from the text of news articles. Various ML models were trained and evaluated to find the best performing one. An overview of the analysis framework presented in this paper is illustrated in Fig. 1. All steps are detailed in the following subsections.

## 3.1 Data Collection

The ISOT Fake News dataset provided by Ahmed et al. [20, 21] was used to investigate the issue of fake news detection. The dataset consists of 21,417 real and 23,481 fake news. Different types of articles on different topics are presented in the dataset, with most of them focusing on political and World news topics.
<figure><img src="image_4.png" /><figcaption>Fig. 1. Flowchart of the overall methodology outline</figcaption></figure>

## 3.2 Data Pre-processing

Raw text is an unstructured form of data that contains noisy content, and its quality can directly affect the performance of our models. Thus, data preprocessing is required in order to ensure robust results and avoid overfitting. Here, we utilized many Natural Language Processing (NLP) techniques to preprocess the ISOT dataset, like converting the articles into lowercase and removing URLs, punctuation, numbers, and stop-words that do not convey significant information. Finally, we applied tokenization and stemming to ensure dimensionality reduction. The cleaned text after pre-processing was used to create word vector representations, while the raw text was used to create the linguistic features.

## 3.3 Feature Extraction

**Linguistic Features.** Taking into consideration the works of [1, 22, 23] a linguistic-based cue of 35 features were extracted from the available dataset to investigate the fake news problem. The features explored are conceptually divided into seven categories that measure the quantity, complexity, uncertainty etc. and try to capture adequately the characteristics of deceptive and truthful language.

**Best Linguistic Features.** As a next step, we attempted to limit the dimensions of the examined dataset. The dimensionality reduction results in shorter training times and more efficient algorithms that can perform equally well with other unknown datasets. Additionally, many ML algorithms exhibit a decrease in accuracy when the number of features is significantly higher than the optimal [24]. To find the best combination of features, we investigated three approaches: a) Boruta algorithm, b) Select-From-Model algorithm and c) Recursive Feature Elimination with Cross Validation (RFECV).

All three approaches train a baseline Random Forest and calculate the importance of each feature. Then each algorithm returns the features deemed as important following a different process. The number of features returned by each method is presented in Table 1.

The reduced feature sets produced by these three methods, were tested upon the classifiers under investigation and their performance was evaluated in terms of accuracy. The best linguistic feature subset that yielded the highest accuracy scores among most of the classifiers, was obtained by the RFECV and consists of 28 variables.

Table 2 presents the optimal subset of features.
<table><caption>**Table 1.** Feature selection results</caption><thead><tr><td>Approach</td><td>Number of features</td></tr></thead><tbody><tr><td>Boruta</td><td>34</td></tr><tr><td>Select-From-Model</td><td>30</td></tr><tr><td>RFECV</td><td>28</td></tr></tbody></table>

<table><caption>**Table 2.** Best Linguistic feature set obtained by using the RFECV</caption><caption>Best Linguistic Features</caption><tbody><tr><td># words</td><td>avg noun phrase length</td><td>Emotiveness index</td></tr><tr><td># syllables</td><td>Pausality</td><td>Lexical diversity</td></tr><tr><td># sentences</td><td># short sentences</td><td>Content word diversity</td></tr><tr><td># verbs</td><td># long sentences</td><td>Redundancy</td></tr><tr><td># noun phrases</td><td>Flesch reading ease</td><td>Typographical error rate</td></tr><tr><td># big words</td><td>Sentence complexity</td><td>Rate of adjectives & adverbs</td></tr><tr><td>avg syllables per word</td><td>Modifiers</td><td># affective terms</td></tr><tr><td>avg # clauses</td><td>% other-reference</td><td>Spatio-temporal information</td></tr><tr><td>avg word length</td><td>Subjectivity</td><td></td></tr><tr><td>avg sentence length</td><td>% self-reference</td><td></td></tr></tbody></table>

**Text Representations.** A very popular approach to fake news detection is the use of text representation techniques and word embeddings. Driven by that, we conducted experiments using three text representation techniques: a) TF-IDF, b) word2vec and c) GloVe. In the TF-IDF approach, the vocabulary size was limited to the 10,000 most frequent words to overcome the dimensionality problems. Additionally, the pre-trained word2vec vectors trained on part of Google News dataset were used. The model includes a total of 3 million words and phrases in a 300-dimension vector representation. Finally, we utilized the Wikipedia 2014 pre-trained GloVe<fnref n="1" /> embeddings and tested both the 100-dimension and 300-dimension vectors.

All these text representation methods were tested upon a linear Support Vector Machine (SVM) classifier using a 10-fold cross validation. The results presented in Table 3 indicate that TF-IDF achieved the best accuracy scores and thus selected as the one that will be used in the rest of the analysis.

<footnote n="1">¹ https://nlp.stanford.edu/projects/glove/</footnote>
<table><caption>**Table 3.** SVM results using text representations</caption><thead><tr><td>Method</td><td>SVM Accuracy (%)</td></tr></thead><tbody><tr><td>TF-IDF</td><td><strong>98.8</strong></td></tr><tr><td>Word2vec</td><td>94.3</td></tr><tr><td>Glove 100 dim</td><td>92.8</td></tr><tr><td>Glove 300 dim</td><td>94.2</td></tr></tbody></table>

The best accuracy value is in bold text

# 4 Results and Evaluation

In our research, we selected the most prevailing ML algorithms found in literature related to fake news classification, to successfully evaluate the given dataset. A variety of traditional and ensemble models with different parametrization were tested. Moreover, DL models were also employed to tackle the problem of fake news detection. Subsequently, we provide some additional information about the experiments conducted.

## 4.1 Performance Results for the ML Models

Many different feature extraction techniques were investigated along with linguistic features extraction as described in Sect. 3.3. Then, the word representation features were enhanced with the best feature set, to verify the claim that linguistic features improve the model performance.

Furthermore, we performed an extensive classification algorithm study for introducing a robust model that effectively detects fake news articles, utilizing the best performing feature set. Traditional learning models (SVM, KNN, etc.) and ensemble models (Random Forest, etc.) were trained using 10-fold cross validation. Moreover, the grid search approach was implemented to determine the optimal parameters of each classifier and increase its performance.

Results of the experiments are illustrated in Table 4.

<table><caption>**Table 4.** Accuracy for different feature sets and models</caption><thead><tr><th rowspan="2">Method</th><th colspan="6">Model Accuracy (%)</th></tr><tr><th>SVM</th><th>KNN</th><th>NB</th><th>RF</th><th>GB</th><th>XGB</th></tr></thead><tbody><tr><td>All Linguistic features</td><td>94.1</td><td>89.1</td><td>69.8</td><td>96.2</td><td>97.3</td><td>97.5</td></tr><tr><td>Best Linguistic features</td><td>94.1</td><td>89.3</td><td>72.2</td><td>96.3</td><td>97.3</td><td>97.5</td></tr><tr><td>TF-IDF</td><td>98.8</td><td>90.4</td><td>92.8</td><td>98.3</td><td>98.7</td><td>99.0</td></tr><tr><td>Best Linguistics &amp; TF-IDF</td><td>99.3</td><td>94.3</td><td>92.8</td><td>98.4</td><td>99.3</td><td><strong>99.6</strong></td></tr></tbody></table>

The best accuracy value is in bold text
## 4.2 Performance Results for the Deep Learning Models

We also performed experiments with DL models, and we evaluated their performance. We tested the Convolutional Neural Network (CNN) and Long-Short Term Memory (LSTM) which are widely used in text classification problems across the research community [25]. Each DL model was trained upon multiple architectures, while some common layers were used in both models to avoid overfitting the training set. We applied Batch Normalization and Dropout to overcome overfitting.

**Convolutional Neural Network (CNN).** Many experiments were conducted using CNN to reach an optimal network. The CNN model described in Table 5, returned 96.81% accuracy and is the one with the highest performance compared to the rest of the trained CNNs.

Additionally, we examined the influence of feeding the model with pretrained word embeddings, instead of using the ones produced directly by the neural network. By utilizing word2vec and GloVe embeddings in the embedding layer of the CNN we observed degradation in accuracy.

<table><caption>**Table 5.** Best Performing CNN architecture</caption><thead><tr><th>Layer</th><th>Embedding Layer</th><th>Convolutional Layer</th><th>Global Avg Pooling</th><th>Dense Layer</th><th>Batch Normalization</th><th>Dropout</th><th>Dense Layer</th></tr></thead><tbody><tr><td>Output dimension</td><td>300</td><td>128</td><td>128</td><td>100</td><td>100</td><td>100</td><td>1</td></tr></tbody></table>

**Long-Short Term Memory (LSTM).** Another extensively used algorithm for fake news detection that was also tested here, is a variation of LSTM called Bi-directional LSTM. Various layers with different number of neurons were employed to conclude to the architecture that yielded the best results. 95.78% was the highest accuracy obtained using Bi-LSTM; its architecture is shown in Table 6.

<table><caption>**Table 6.** Best Performing LSTM architecture</caption><thead><tr><th>Layer</th><th>Embedding Layer</th><th>Bi-LSTM Layer</th><th>Dense Layer</th><th>Batch Normalization</th><th>Dropout</th><th>Dense Layer</th></tr></thead><tbody><tr><td>Output dimension</td><td>300</td><td>32</td><td>100</td><td>100</td><td>100</td><td>1</td></tr></tbody></table>
# 5 Discussion

The outcomes of this study, support the findings in the literature about linguistic features improving the performance of the classifiers when used as an enhancement to text representations and word embeddings. During the experimental process, we extracted 35 linguistic features which resulted in 97.5% accuracy produced by training an XGB classifier. Subsequently, we searched for a subset of the feature set aiming at reducing the dimensions of the dataset without lowering the accuracy. This was achieved by the RFECV algorithm that deemed 28 features as important. Again, XGB performed best in this dataset preserving the same accuracy as the one obtained by using the whole feature set.

Additionally, text representations and word embeddings were utilized as feature extraction techniques. It is worth noting that when the article's text was represented using pre-trained word embeddings like word2vec and GloVe, the accuracy scores of all models were reduced compared to the scores returned by the TF-IDF text representation method. There can be several reasons to explain these findings. One might say that it is possible that word embeddings cannot represent efficiently the news articles due to their large length. The TF-IDF method obtained 99% accuracy with an XGB method. Furthermore, the combination of the best linguistic feature set with the enhancement of TF-IDF features, reinforced the performance of all the classifiers acquiring an accuracy of 99.6%.

The experimental results on traditional and ensemble models, indicate that ensemble methods overshadowed traditional models in terms of performance. Both Random Forest and Gradient Boosting achieved high accuracy scores, while XGB surpassed all other models in all cases examined. Another remarkable observation is that linear SVM yielded quite impressive results and can compete with ensemble methods.

The results of the DL models were quite promising, despite lacking any explicit linguistic features. CNN and LSTM were trained deploying different architectures, with CNN outperforming LSTM with an accuracy of 96.8%.

# 6 Conclusions and Future Work

The objective of this paper was to propose an effective way to remedy the issue of dissemination of misinformation using linguistic cues. Several supervised classifiers were trained using linguistic features, word vector representations and their combination, with the latter scheme outshining in all models. Ensemble learners have shown the greatest performance compared to individual learners. DL algorithms were also explored as a potential solution, but they did not manage to overcome the performance of ensemble models, due to the size of the dataset. The promising results returned by the deep neural networks indicate that there is potential for further experimentation to improve performance.

In this study, content-based approaches were utilized to explore patterns in text that differentiate fake from real news, as we aimed to detect fake news in real time before this propagate to social media platforms. For that reason, a possible object of future research could be to employ meta-data about the source and the author of the
news, as well as information about the diffusion of the news in social media platforms. Additional information could also be exploited by images and videos included in the articles, employing Transfer learning and pre-trained models. Another future direction involves experimentation with more open access datasets for testing the generalization of the results.

**Acknowledgements.** This research is co-financed by Greece and the European Union (European Social Fund-SF) through the Operational Program "Human Resources Development, Education and Lifelong Learning 2014-2020" in the context of the project "Support for International Actions of the International Hellenic University", (MIS 5154651).

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