An Effective Hybrid Text-Based Approach to Identify Fake News on

Social Media

Imtiez Fliss

and Hamza Bargougui

National School of Computer Sciences, Manouba University, Tunisia

Keywords:

Text-Based Fake News Detection, BERT (Bidirectional Encoder Representations from Transformers), Hybrid

Deep Learning Techniques.

Abstract:

Because of their low cost, simplicity of access, and quick dissemination, social media are today one of the

primary information sources for millions of people worldwide. However, this is at the expense of dubious

credibility and a large danger of being exposed to ”fake news,” which is deliberately designed to mislead

readers. In light of this, in this paper we propose a novel method for identifying bogus news based on the

text content. This method is founded on a mix of BERT (Bidirectional Encoder Representations from Trans-

formers) and deep learning techniques (Convolutional Neural Networks (CNN) and Long Short Term Memory

(LSTM)). Promising results are seen when the proposed approach is compared to other models.

1 INTRODUCTION

Today’s world is one that is constantly evolving. For

millions of individuals, social media is the best source

of information and is quickly taking over as the pre-

ferred news source. They have the power to make

decisions on matters of politics, society, and the econ-

omy, but they may also harm the reputations of people

and organizations while advancing certain persons,

teams, or organizations.

There is a ton of information available, some of it

accurate, some not. Most of the time, this data is in-

stantly shared without being thoroughly checked. In

recent years, the phenomenon of fake news (Quandt

et al., 2019; Kalsnes, 2018) has become widespread,

facilitated by social networks. There are several uses

for these false reports. Others have an impact on

political decisions and public perception of ﬁnancial

markets through altering the reputation of businesses

and institutions on the Internet. Some are developed

merely to boost clicks and trafﬁc to a website.

Therefore, it is crucial to make sure that informa-

tion is accurate, keep an eye on internet news, and

create tools to counteract fake news. For this purpose,

We can use social media deep learning (Bengio, 2019;

LeCun, 2016) and can facilitate correct analysis of in-

formation types. On the other hand, using semantic

analysis of circulating data is very interesting. This

https://orcid.org/0000-0003-2229-7004

allows us to recognize relevant information in context

and classify it accordingly.

In this context, to simplify accurate analysis of the

type of information published and to distinguish be-

tween false news and true news, the purpose of this

paper is to present a novel method based on deep

learning categorization of texts shared on social me-

dia.

This approach combines the use of BERT method

as a feature encoder with convolutional neural net-

works (CNN) (Kim, 2014) and Long Short Term

Memory (LSTM) (Allcott and Gentzkow, 2017) for

text classiﬁcation. Finally, this approach put into

practice, and its accuracy is evaluated against other

models taken from the literature.

The remainder of this paper is structured as fol-

lows. While, Section 2 introduces related work, Sec-

tion 3 presents the proposed approach for detecting

fake news. Section 4 contains experiments and dis-

cussion. Finally, some concluding remarks and per-

spectives are presented.

2 RELATED WORK

Before presenting existing fake news detection mod-

els, it is important to deﬁne what fake news is. So, we

will ﬁrst identify the exact characteristics that deﬁne

fake news. Next, we will focus on the main fake news

detection models.

374

Fliss, I. and Bargougui, H.

An Effective Hybrid Text-Based Approach to Identify Fake News on Social Media.

DOI: 10.5220/0011674400003393

In Proceedings of the 15th International Conference on Agents and Artiﬁcial Intelligence (ICAART 2023) - Volume 3, pages 374-380

ISBN: 978-989-758-623-1; ISSN: 2184-433X

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

2.1 Fake News Deﬁnition

The term ”Fake News” began to be used frequently

after the 2016 US election. According to (Allcott

and Gentzkow, 2017; Shu et al., 2017; Zhou et al.,

2019; Zhou and Zafarani, 2020), fake news are news

articles that are intentionally and veriﬁably false and

can misinform readers. There are two key character-

istics of this deﬁnition: veracity and intent. First, fake

news involves false information that can be veriﬁed

intrinsically. Second, fake news is created with the

dishonest purpose of misleading readers. This deﬁni-

tion has been widely adopted by (Klein and Wueller,

2017; Lazer et al., 2018; Mustafaraj and Metaxas,

2017; Potthast et al., 2017). According to (Ahmed

et al., 2017), the term Fake News is based on four

main components: creator, target victim, news con-

tent and social context.

• Creator: creators of fake news online can be a hu-

man being or not.

• Target victims: Depending on the objectives of the

news, targets can be students, voters, parents, el-

derly people.

• News content: News content refers to the body of

the news.

• Social context: the social context indicates how

the news are disseminated on the Internet. Social

context analysis includes the user, network anal-

ysis (how online users are involved in the news)

and analysis of the dissemination pattern.

2.2 Fake News Detection Models

There are numerous models for spotting bogus news

(Kaur et al., 2019). We focus in this section on re-

cent works looking at the detection of fake news based

on news content. In (Vivek et al., 2017), to acquire

linguistic attributes for each of the articles, authors

employed the LIWC (Linguistic Analysis and Word

Count) package. Z-score normalization was used to

normalize each feature. Then, focusing on how well

the algorithms performed for the test set, they devel-

oped a number of machine learning models based on

well-known methods like logistic regression, support

vector machines, random forests, decision trees, and

k-neighbors classiﬁer. The Support Vector Machine

algorithm outperformed the others in terms of predic-

tion accuracy.

Ahmed et al. (Ahmed et al., 2018) also exam-

ined the performance of six different algorithms that

categorize information as false or true using N-gram

traits and TF-IDF scores. The dataset included both

real and false comments on political news stories and

product evaluation comments. A model that they cre-

ated can be used in two different contexts. Their study

demonstrates that utilizing TF-IDF and SVM to rank

news articles was the most effective method.

Convolutional neural network (CNN) mod-

els have also been deployed in detecting fake

news. Authors in (Xu et al., 2020) pro-

pose a fake news detection solution to the prob-

lems occurring during an analysis of short texts

namely, CNN Text Word2vec.CNN Text Word2vec

introduces a word2vec neural network model to train

distributed word embeddings on every single word.

Authors in (Nicole et al., 2018) use the deep neu-

ral network introduced by (Kim, 2014) to learn to de-

tect fake news. It has been proven that this works

well for text classiﬁcation. All of the text’s words

are a one-hot representation, which is a sparse vector

with only one item equal to 1 to represent each word.

This network’s ﬁrst layer is a pre-trained word2vec

embedding, which converts each word into a 1,000-

dimensional representation with close distances be-

tween words with comparable meanings. the intro-

duction follows a method for identifying the patterns

in an article that are most beneﬁcial for classifying it

as fake or legitimate news. This entails returning the

network’s output to the article in order to identify the

phrases that are ”most fake” and ”most real.”

On the other hand, BERT-based models had been

successfully applied to the fake news detection task;

FakeBERT is a new technique Kaliyar et al. (Kali-

yar et al., 2021) created to identify bogus news in the

early 2020s. By merging several parallel blocks of the

single-layer CNNs with BERT, this method is effec-

tive contrarily to (De Sarkar et al., 2018) that looked

at a text sequence in a unidirectional way.

In (Verma et al., 2022), the Message Credibil-

ity (MCred) framework was proposed. This research

makes use of both local and global text semantics to

detect bogus news. The Bidirectional Encoder Rep-

resentations from Transformers (BERT) and Convo-

lutional Neural Networks (CNN) used in this frame-

work are combined to provide global text semantics

based on the relationship between words in sentences

and local text semantics based on N-gram character-

istics.

3 PROPOSED APPROACH FOR

THE DETECTION OF FAKE

NEWS

Our method focuses on taking contextual understand-

ing into account when determining whether text posts

An Effective Hybrid Text-Based Approach to Identify Fake News on Social Media

375

are fake news or true news. The text classiﬁcation

process displayed in Figure 1 serves as a major inspi-

ration for the key elements of our approach.

Figure 1: Overview of a text classiﬁcation pipeline.

Our contribution consists in the choice of adequate

models for each step.

In fact, after selecting the dataset to be used, we

proceed to the fundamental step of preprocessing the

inputted text.

We need to extract the important characteristics of

the text (Facebook post, tweets, or other) in order to

better classify it. We want to understand the context

of the words in this step. In this paper, we rely on a

document vocabulary representation that can capture

the context of a word in a document, take into account

semantic and syntactic similarity, and consider the re-

lationship with other words. As a result, we chose

BERT as a technique for the feature encoding step.

We can use deep learning to its full potential dur-

ing the classiﬁcation phase. Fake news detection

models are trained to comprehend the context of the

text and determine if the material is fake news or

not. Long or short texts are both acceptable for anal-

ysis. If the messages are brief, the CNN, a sub-

class of deep and feed-forward artiﬁcial neural net-

works, might be an excellent choice for classifying

fake news. As it shows how well it extracts regional

and position-invariant features. Another deep learn-

ing recurrent neural network model based on Long

Short Term Memory(LSTM) performs better for long

texts since it can learn the long-term reliance of the

text.

Therefore, a hybrid CNN-LSTM model is pro-

posed for the purpose of classifying fake news in this

work in order to combine the beneﬁts of CNN and

LSTM.

3.1 An Overview of the Proposed Model

Our BERT + CNN + LSTM model is composed of

seven layers illustrated in Figure 2 and detailed as fol-

lows:

1. An Input Layer: which takes the plain text as

input.

2. A Pre-Processing Layer: which takes the plain

Figure 2: The proposed LSTM Model.

text as input and the result of the pre-processing

is a batch of input sequences of ﬁxed length for

the encoder.

3. A BERT Encoder Layer: which takes the out-

put of the previous layer as input and the result

is a matrix of 128 vectors represents a text such

that each vector of dimension 512 represents a se-

quence of text, then the output shape is (128, 512).

It is a vector representation of each word in the

text which takes into account the context and a

vector representation for each text.

4. A convolution Layer : of dimension 1 that ap-

plies a ﬁlter to the input to detect and group fea-

tures.The output shape is (128, 32)

5. A LSTM Layer: that retains long text important

information.The output shape is (32).

6. An Output Layer: which represents the classiﬁer

that takes the output of the LSTM layer and the re-

sult are two binary classes: Fake or real news.The

output shape is (2).

3.2 Text Pre-Processing Step

The ﬁrst step is the dataset preparation step which in-

cludes:

• loading a dataset which is the input to our system.

• Basic pre-processing: When pre-processing the

data, we:

– Remove punctuation marks (such as @,!)

– Remove the extra white space in the text.

• the division of the dataset into training and vali-

dation sets.

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3.3 Feature Encoding Step

Once the data has been cleaned, formal feature en-

coding methods can be applied. In general, texts and

documents are unstructured data sets. However, these

unstructured text sequences need to be converted into

structured form. The goal of text representation is to

convert pre-processed texts into a form that the com-

puter can process.

In our work, we propose to use a method in which

each word of a vocabulary is mapped to a real vec-

tor considering its context to facilitate subsequently

the detection of fake news. In this context, we pro-

pose to use the Bidirectional Encoder Representations

from Transformers (BERT) (Devlin et al., 2018). It

is a language representation model that uses encoder-

decoder architecture. The general structure of these

neural networks processes language in sequence and

the input passes through a layer called an “encoder”

where the words are communicating information with

each other for the model to generate a semantic struc-

ture. For the model to perform a task such text classi-

ﬁcation, the ﬂow of information then passes through

a “decoder” where it performs the reverse operation

while it recovers the information from the “encoder”.

This type of neural network structure is known as

Transformers (Gillioz et al., 2020) in NLP. The BERT

model has two versions. The version we use in

this work is BERTBASE with 12 layers with hidden

layer sizes of 768 and 12 attention layers.The param-

eters are approximately 110M. Self-attention layers

use dot-product similarity scores to augment each to-

ken’s representation with information from other to-

kens that are similar or contextually informative. Cru-

cially, self-attention layers are fully parallels because

there is no recursiveness. BERT produces contextual-

ized word embeddings where the vector for the word

is computed based on the context in which it appears.

BERT differs from its predecessors (pre-trained

NLP models) in the way it is pre-trained. This pre-

training is unsupervised. BERT has been pre-trained

using a self-monitoring technique on a sizable corpus

of multilingual data. This indicates that it is an au-

tomated process that generates input and labels from

texts that have merely been pre-trained on raw texts

without any sort of human labeling. BERT is pre-

trained on a large dataset consisting of texts from En-

glish Wikipedia pages (2500 million words) as well as

a set of books (800 million words). This pre-training

is done in two steps:

• Step 1: MLM or Masked Language Modeling

Language Modeling is a common NLP task that

consists of predicting the next word given the be-

ginning of the sentence. The principle of Masked

Language Modeling is to predict ”masked” to-

kens from the other tokens in the sequence.The

model selects a sentence at random from the in-

put, masking 15% of words, and then executes

the entire sentence while predicting the words that

were hidden. This is different from conventional

endogenous neural networks (RNN), which often

only observe one word at a time, and from iso-

lated models like the GPT (Generative Pre-trained

Transformer), which internally conceals tokens in

the future. This allows the model to pick up on

the sentence’s dual representation.

• Step 2: NSP or Next Sentence Prediction This

step consists of predicting whether or not a cer-

tain sequence A is followed by a certain sequence

B. The NSP step allows BERT to understand the

inter-dependencies between the sentences that fol-

low each other. This enriches the ﬁnal represen-

tation of the text. Using two masked sentences

as input, the model is trained to connect the two

sentences. It occasionally corresponds to subse-

quent sentences in the original text, but not al-

ways. The model should then foretell whether

two sentences will follow one another. As a result,

the model has acquired knowledge about how lan-

guages are internally represented in the training

organization, which can be used to extract func-

tions useful for subsequent work. For instance,

if we have a data set with labeled sentences, we

can train using functions from BERT-models gen-

erated as input.

The principle of using BERT is quite simple: it is

”already” pre-trained on a large quantity of data, we

modify it for a precise task and then (re)train it with

our own data. This allows BERT, in addition to its

main function of text modelling in text classiﬁcation

tasks, to classify texts thanks to ﬁne-tuning (Zhang

et al., 2020). Fine-tuning is the process of adding a

neural network to the output of BERT as needed. In

our approach, we settle for BERT as a feature encod-

ing technique.

3.4 Classiﬁcation Step

According to (Mahrishi et al., 2020), when it comes

to tackling real-world issues, deep neural networks do

better than traditional machine learning techniques. It

has been shown that neural network models are capa-

ble of impressive performance in text classiﬁcation.

For this kind of task, the two most popular designs

are convolutional neural networks (CNN) and recur-

rent neural networks (RNN). In this paper, we propose

combining the beneﬁts and advantages of both archi-

tectural models to develop a new model for text clas-

An Effective Hybrid Text-Based Approach to Identify Fake News on Social Media

377

siﬁcation. The convolutional neural network applies

a non-linear activation function to the convolutional

operation’s output, and after pooling the data for clas-

siﬁcation, it uses a full connection layer. Filter, also

referred to as the kernel function, is the fundamental

component of convolutional operation.

The Long Short Term Memory network (LSTM),

on the other hand, is a unique kind of recurrent neural

network (RNN) that has the capacity to learn long-

term dependencies. Cell state, the central component

of the LSTM, allows for the addition or deletion of

information from cells and the selective letting of in-

formation via the door mechanism. Remember gate,

input gate, and output gate are the three gates that

make up an LSTM. The input gate chooses what data

should be updated to the cell state after the forget gate

has decided which data should be removed from the

cell state. The cell state is updated after establishing

these two locations. The network’s ultimate output is

determined by the output gate.

In this instance, the LSTM layer keeps track of

crucial text information (especially long ones). The

classiﬁcation process is completed by making a ﬁnal

determination regarding the text’s authenticity using

the output from the LSTM layer and the information

that was retained.

4 RESULTS

We will ﬁrst present the dataset we used before re-

vealing the ﬁndings.

4.1 Dataset

To validate our approach, we used the dataset

WELFake proposed in (Verma et al., 2021). It is a

dataset of 72,134 news articles, 35,028 of which are

true and 37,106 of which are false. The authors com-

bined four popular news datasets (Kaggle, McIntire,

Reuters, and BuzzFeed Political) to prevent classiﬁer

overﬁtting and to provide more text data for better ML

training.

The dataset has four columns: serial number (be-

ginning at 0), title (about the text news heading), text

(about the news content), and label (0 = fake and 1

= real). The distribution of real and fake news is de-

picted in Figure 3. The dataset includes both lengthy

and concise texts. The distribution of the news word

count is shown in Figure 4.

70% of the data were used for training, 30% were

used for testing, and 30% of the training set was used

for validation.

Figure 3: Distribution of real and fake news in the dataset.

Figure 4: Distribution of the news word count in the dataset.

We start by preprocessing the dataset before mov-

ing on to feature encoding and determining whether

or not the data is fake news. We outline the experi-

mental setup and ﬁndings in the section that follows.

4.2 Experimental Settings

In this experiment, we use the following parameters.

• Number of Filters: 32 ﬁlters of size 8

• Number of Units: 32

• Activation Function: Softmax

• Optimizer: Adamw provided by TensorFlow

• Loss Function: cross-entropy. The cross-entropy

compares the model’s prediction with the label

which is the true probability distribution. The

cross-entropy goes down as the prediction gets

more and more accurate. It becomes zero if the

prediction is perfect.

• The Evaluation Metric: accuracy. Accuracy is

the fraction of predictions our model got right.

• Number of Epochs: 5 (We use Stochastic Gradi-

ent Descent to get the best value of epochs).

• Batch Size: 16

• Learning Rate: 3e

−5

• Drop Out: 0.1

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4.3 The Obtained Experimental Results

The model based on BERT+CNN+LSTM was trained

over 5 epochs, obtaining an accuracy of 1 and a loss

of 2.4586e-4 on the training set, and an accuracy of 1

and a loss of 5.7634e-4 on the validation and test sets.

Figure 5 shows the accuracy and loss curve for the

model evaluated on the training and validation sets.

Figure 5: Evaluation of the BERT + CNN + LSTM model.

We note that the loss is minimal and the accuracy

value is interesting.

4.4 Discussion

To validate the obtained experimental results, we

tested the same dataset with other models. In Table

1, we sum up the accuracy of our model with other

models of literature.

Table 1: Comparison to existing works.

Reference Model %Accuracy

(Vivek et al., 2017) LinguisticAnalysis+SVM 87

(Ahmed et al., 2018) TF-IDF+SVM 92

(Nicole et al., 2018) DNN 93.50

(Kaliyar et al., 2021) BERT+CNN 98.9

(Verma et al., 2022) BERT+CNN+N-gram 99.01

Our model BERT+CNN +LSTM 100

We ﬁnd that our model surpasses all existing con-

ventional methods, including TF-IDF for feature en-

coding and machine learning models for classiﬁca-

tion, like Ahmed et al. (Ahmed et al., 2018) who,

with their best method, attained an accuracy of 92%.

Additionally, our model outperforms recent meth-

ods that use BERT for feature encoding and neural

network models for classiﬁcation, such as those pro-

posed by Kaliyar et al. (Kaliyar et al., 2021) and

Verma et al. (Verma et al., 2022), who respectively

achieved accuracies of 98.90% and 99.01% .

5 CONCLUSION

In this paper, we propose developing a text mining

and learning-based approach for detecting fake news.

As a result, we proposed a method for classifying

texts as true or false that combines CNN and LSTM

deep learning with the BERT text feature encoding

technique. When compared to other models, the ex-

perimental results of the proposed technique are ex-

tremely promising.

To identify fake news in this study, we relied

solely on the text’s content. We intend to expand

on this approach in future works by taking into ac-

count additional factors such as the source (the origi-

nal author), the source’s characteristics (false proﬁles,

robots, malicious people, etc.), and so on.

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