Received 31 December 2023, accepted 20 January 2024, date of publication 23 January 2024, date of current version 1 February 2024.

Digital Object Identifier 10.1109/ACCESS.2024.3357661

Utilizing Ensemble Learning for Detecting
Multi-Modal Fake News
MUHAMMAD LUQMAN 1, MUHAMMAD FAHEEM 2, (Member, IEEE),
WAHEED YOUSUF RAMAY3, MALIK KHIZAR SAEED 4, AND MAJID BASHIR AHMAD5
1Department of Computer Science, Northwestern Polytechnical University, Xi’an 710072, China
2School of Technology and Innovations, University of Vaasa, 65200 Vaasa, Finland
3Department of Computer Science, Air University, Multan 60000, Pakistan
4Department of Computer Sciences, COMSATS University Islamabad, Vehari 61000, Pakistan
5School of Software and Microelectronics, Northwestern Polytechnical University, Xi’an 710072, China

Corresponding author: Muhammad Faheem (muhammad.faheem@uwasa.fi)

ABSTRACT The spread of fake news has become a critical problem in recent years due extensive use of
social media platforms. False stories can go viral quickly, reaching millions of people before they can be
mocked, i.e., a false story claiming that a celebrity has died when he/she is still alive. Therefore, detecting
fake news is essential for maintaining the integrity of information and controlling misinformation, social
and political polarization, media ethics, and security threats. From this perspective, we propose an ensemble
learning-based detection of multi-modal fake news. First, it exploits a publicly available dataset Fakeddit
consisting of over 1 million samples of fake news. Next, it leverages Natural Language Processing (NLP)
techniques for preprocessing textual information of news. Then, it gauges the sentiment from the text of each
news. After that, it generates embeddings for text and images of the corresponding news by leveraging Visual
Bidirectional Encoder Representations from Transformers (V-BERT), respectively. Finally, it passes the
embeddings to the deep learning ensemble model for training and testing. The 10-fold evaluation technique
is used to check the performance of the proposed approach. The evaluation results are significant and
outperform the state-of-the-art approaches with the performance improvement of 12.57%, 9.70%, 18.15%,
12.58%, 0.10, and 3.07 in accuracy, precision, recall, F1-score, Matthews Correlation Coefficient (MCC),
and Odds Ratio (OR), respectively.

INDEX TERMS Ensemble learning, convolutional neural network, multi-modal fake news, classification,
boosted CNN, bagged CNN.

I. INTRODUCTION
The concept of fake news is not new. Its roots existed long
ago in our society. It refers to false information which can be
disseminated to mislead or deceive the Public. For example,
fake news about COVID-19 vaccines could discourage people
from getting vaccinated, leading to increased rates of illness
and death. In the past, every kind of distinct material
was considered fake news, like satires, conspiracies, news
manipulation, and click-bait. However, fake news is now
becoming jargon [1] and has a huge impact on the critical

The associate editor coordinating the review of this manuscript and

approving it for publication was Donato Impedovo .

events happening in our society, e.g., spreading fake news
(false stories) on social media was very concerning in US
presidential election 2016 [2].

Fake news can spread quickly through social media and
other online platforms. It can have serious consequences,
such as causing panic, influencing elections, and eroding
public trust in legitimate news sources. Individuals need
to distinguish real news and critically evaluate sources
of information before sharing or responding to them.
Additionally, news organizations and social media platforms
are responsible for combating the spread of fake news by
fact-checking and removing false content. The surveys show
that about 70% of Americans use social media as a source

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M. Luqman et al.: Utilizing Ensemble Learning for Detecting Multi-Modal Fake News

of news and circulating information [3]. The accessibility of
news and information on the Internet is very low-cost and
convenient. However, spreading fake news on these carriers is
straightforward and effortless [4]. Fake news can lead to false
assumptions that drastically affect our society. Consequently,
it is critical to design an automated fake news detection
system.

Many researchers are actively developing new and better
methods for identifying and combating the spread of misin-
formation. Some of the key research areas and trends in this
field include deep learning approaches, e.g., Convolutional
Neural Network (CNN); linguistic features, e.g., sentiment
analysis, topic modeling, and stylometric analysis; source-
based approaches, e.g., analyzing the domain name, social
media presence, or history of the news source, and ensemble
approaches, e.g., combining linguistic, source-based, and
deep learning models to create a more robust and accurate
detection system. Although recent research has identified
the issues of the said problem and proposed different
solutions, e.g., pre-trained language models have shown their
effectiveness in alleviating feature engineering efforts, such
as Bidirectional Encoder Representations from Transformers
(BERT) [5], OpenAI GPT [6], and Elmo [7], however; the
problem requires significant performance improvement.

From this perspective, this paper proposes an ensemble
learning-based detection of multi-modal fake news (ELD-
FN). It first exploits a publicly available dataset Fakeddit,
a novel multi-modal dataset consisting of over 1 million sam-
ples from multiple categories of fake news. Second, it lever-
ages Natural Language Processing (NLP) techniques for
preprocessing textual information of news. Third, it gauges
the sentiment from the text of each news. Fourth, it generates
embeddings for text and images of the corresponding news
by leveraging V-BERT [8], respectively. Finally, it passes
the embeddings to the deep learning ensemble model for
training and testing. The 10-fold evaluation technique is used
to check the performance of ELD-FN. The evaluation results
are significant and outperform the state-of-the-art approaches
with the performance improvement of 12.57%, 9.70%,
18.15%, 12.58%, 0.10, and 3.07 in accuracy, precision,
recall, F1-score, Matthews Correlation Coefficient (MCC),
and Odds Ratio (OR), respectively.

The main contributions made in this paper are as follows.

• The proposed approach integrates news sentiment as
a crucial feature and employs ensemble learning to
identify multi-modal fake news.

• It is evident from the evaluation results that ELD-FN
is significant and outperforms the baseline approaches
with the performance improvement of 12.57%, 9.70%,
18.15%, 12.58%, 0.10, and 3.07 in accuracy, precision,
recall, F1-score, MCC, and OR, respectively.

The organization of the rest of the paper is as follows.
Section III describes the details of ELD-FN. Section IV
describes the evaluation methods for ELD-FN, obtained
results, and their threats to validity. Section II discusses the

research background. Section V summarizes the paper and
suggests future work.

II. RELATED WORK
Although extensive research on fake news detection has been
performed [9], [10], [11], [12], [13], [14], [15], [16], [17],
[18], [19], [20], [21], [22], [23], most research is conducted
on textual data or uni-modal features. However, two most
relevant researches [24], [25] proposed deep learning-based
solutions for detecting fake news. The proposed approach
(ELD-FN) differs from baseline approaches as it does not
work for the multi-modal features but also considers the
sentiments involved in the textual information of news.

Most of the state-of-the-art fake news classification
approaches can be categorized as follows: 1) fake news
classification approaches for single-modality and 2) fake
news classification approaches for multi-modality.

A. FAKE NEWS CLASSIFICATION APPROACHES FOR
SINGLE-MODALITY
The fake news classification approaches for single-modality
can be further divided into two categories based on the
text/image features.

1) SINGLE-MODALITY BASED CLASSIFICATION
APPROACHES USING TEXTUAL FEATURES
Textual features can be divided into generic and latent
categories. Usually, traditional machine learning algorithms
utilize Generic textual features. These algorithms analyze
text based on linguistic levels such as lexicon, syntax,
discourse, and semantics. Previous research has compiled
a detailed table summarizing these features [10]. However,
Latent textual features consist of the embeddings extracted
from textual data of news at the word, sentence, or document
level. Latent vectors are constructed from the textual news
data. Furthermore, these latent vectors are used as input for
classifiers, i.e., SVM.

Recurrent neural networks (RNNs) are potent in modeling
and analyzing sequential data. For example, Ma et al. used
RNNs to capture relevant information over time by learning
hidden layer representations [11]. Meanwhile, Chen et al.
proposed a CNN-based approach for the classification [12].
Moreover, a novel technique Attention-Residual Network
(ARC) is introduced to acquire long-range features. Ma et al.
introduced a Generative Adversarial Network (GAN)-based
model that employs a Generator network based on Gated
Recurrent Units (GRU) to generate contentious instances.
Furthermore, a Discriminator network based on RNNs is
designed to identify essential features [13].

RNN-based models have proven very effective in classify-
ing fake news detection datasets. However, the RNN-based
models prioritize the recent input sequence, and the essential
features may be located at the end of the sequence. Yu et
al. proposed a CNN-based approach that resolves this issue.
The proposed technique does not prioritize recent input
sequences. This approach applies feature extraction based

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on the relationship of the essential features [14]. Vaibhav
and Hovy utilize a graphical approach for classifying news
articles [15]. For this purpose, they used Graph Neural
Networks, such as Graph Convolutional Networks (GCN)
and Graph Attention Networks (GAT), to create graph
embeddings for fake news detection.

Wu et al. utilize multi-task learning techniques to classify
and detect fake news. Moreover, the stance classification
task optimizes shared layers concurrently, improving news
representations [16]. Cheng et al. utilized LSTM model
to classify the textual news data. They used a variational
autoencoder to extract essential textual features at the tweet-
level text.

Some researchers have assumed that complex and
multi-dimensional news are not accessible initially. The
accessibility of only text-based news depends on the popular-
ity [17]. Qian et al. developed a text-based model that utilizes
word/sentence level data from legitimate papers to produce
user feedback for early detection [18]. This addressed the
scarcity of user reviews as an auxiliary source of information.
For example, Qian et al. proposed an approach for generating
user feedback on the text. Such feedback was along with
word/sentence level information from real articles for the
classification process [18]. Giachanou et al. investigated the
influence of emotional cues in the proposed model. They
propose an LSTM model that integrates emotional signals
extracted from claim texts to differentiate between true and
false news [19].

2) SINGLE-MODALITY BASED CLASSIFICATION
APPROACHES USING IMAGE FEATURES
As multimedia becomes more prevalent in social networks,
news now contains text and visual information such as images
and videos that convey rich meaning. However, textual
feature-based approaches face challenges in effectively
capturing visual information because of the heterogeneity
between text and image data. Consequently, many researchers
have proposed image-based approaches for detecting fake
news.

Classical image-based models utilized basic fundamental
numerical features of images [20], [26], such as image
count, popularity [27], and type to identify fake news. For
impaired images, complex forensics features were extracted.
Furthermore, post and user-based features are integrated to
identify fake news [28]. However, it was evident that basic
numerical features are inadequate to describe complex visual
information of the news images.

Deep learning models such as CNNs have proven effective
in capturing visual features in news images. Many researches
have shown that feature extraction from CNN models can be
used in visual recognition tasks to generate generic image
representation [29].
Building on the success of CNNs, recent studies have

utilized pre-trained deep CNNs like VGG19 [30], [31] to
obtain generic visual representations [32], [33]. Researchers

suggested multi-domain visual neural models to capture the
inherent traits of fabricated news images more effectively.
These multi-domain models merged frequency and pixel
domain visual data to differentiate between genuine and
fabricated news based on visual characteristics [34]. Poor
quality is a common trait in fake news images. The poor
quality feature and image semantics are visible in frequency
and pixel domains. However, the quality feature is extracted
by CNNmodel, and the semantics of the images are extracted
by CNN-RNN model.

B. FAKE NEWS CLASSIFICATION APPROACHES FOR
MULTI-MODALITY
Word-based and Image-based information are both important
in detecting fake news. As social networks often contain
both types of information, combining them can improve per-
formance. This section discusses the different multi-modal
approaches for fake news detection, categorized based on the
different perspectives they adopt.

1) PROBLEMS IN MULTI-MODALITY
Several studies have explored using visual information to
complement textual information in detecting fake news.
These studies typically use text-based and image-based
encoders to extract textual and visual features, respectively.
Furthermore, these feature vectors construct an overall
feature vector for each news. For example, Wang et al.
proposed Event classification as an additional task to enhance
the generalizing ability of themodel for event-invariant multi-
modal features [32]. Other researchers, such as Singhal et al.,
use a combination of text-based and image-based features.
They utilize BERT and XLNet pre-trained models for
encoding text-based and image-based data, respectively [35].
However, these approaches are proven to be limited in
effectively detecting multi-modal fake news because of their
ability to capture complex cross-modal correlations. More
advanced multi-modal techniques are needed to improve the
performance of fake news detection.

2) FLEXIBILITY IN MULTI-MODALITY
Some studies have recognized that irrelevant images are a
common characteristic of multi-modal fake news and have
focused on measuring the consistency between the text and
visual components in detection. One approach by Zhou and
Zafarani [36] used an image captioning model to generate
sentences from images and then measured the similarity
between those sentences and the original text. However,
this approach was constrained by the discrepancies that
existed between the training data of the image captioning
model and the real news corpus. Another approach by
Xue et al. projected the visual and textual features into a
shared feature space and computed the similarities between
resulting multi-modal features. However, they encountered
difficulties capturing multi-modal inconsistencies because of
the semantic gap between the two types of features [37].

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Ghorbanpour et al. [38] proposed the Fake-News-Revealer
(FNR) method, which uses a Vision-transformer [39] and
BERT [5] to extract image and text features respectively. The
model extracted textual and visual features separately and
determined their similarities by loss.

3) IMPROVEMENT IN MULTI-MODALITY
Several researchers have proposed different approaches for
fake news detection using multi-modal data. Jin et al. utilized
an RNN model and applied an attention mechanism to com-
bine information extracted from textual, visual, and social
context data [40]. Zhang et al. [41] used a multi-channel
CNN with an attention mechanism to combine multi-modal
information, while Song et al. [42] proposed the co-attention
transformer to model the bidirectional enhancement between
images and text. Qian et al. developed a Hierarchical
Multi-modal Contextual Attention Network (HMCAN),
which was designed to collectively capture multi-modal
context data and the hierarchical semantics of text [43].
Wu et al. introduced the Multi-modal Co-Attention Network
(MCAN) that extracts spatial-domain and frequency-domain
features from the image and text, and fuses visual and
textual features using multiple co-attention layers [44].
Other researchers have also utilized Graph Convolutional
Networks (GCN) and entity-centric cross-modal interaction
to model the relationship between word-based and image-
based objects. Finally, Zhang et al. and Laura et al. proposed
a BERT-based multi-modal model to encode text-based and
image-based information. The model effectively captures the
interplay between text and images and employs contrastive
learning to enhance multi-modal representations. [24], [45]
integrated visual entities to enhance the comprehension of
high-level semantics in news images and to model the
inconsistencies and mutual enhancements of multi-modal
entities [22].
In summary, when performing multi-modal fake news

detection, there are three important inductive biases to con-
sider when examining text-image correlations. Firstly, images
provide additional information to the text, highlighting the
need for multi-modal. Secondly, problems between text
and images can serve as a potential signal for detecting
fake news using multiple modalities. Finally, text-based and
image-based data can improve performance by identifying
essential features.

III. METHODOLOGY
A. OVERVIEW
The overview of ELD-FN is depicted in Fig. 1. The following
are the main steps of ELD-FN.

1) First, the publicly available multi-modal dataset
(Fakeddit) is collected from Google Drive.1

2) Next, it leverages NLP techniques, e.g., tokenization,
stop-word removal, lowercase conversion, and lemma-
tization, for preprocessing textual information of news.

1https://fakeddit.netlify.app/, accessed on 15-01-2023.

3) Then, it computes the sentiment from the text of each
news.

4) After that, it generates embeddings for text and images
of the corresponding news by leveraging V-BERT,
respectively.

5) Finally, it passes the embeddings to the deep learning
ensemble model for training and testing.

B. PROBLEM DEFINITION
A news n from a set of multi-modal dataset of news N can be
represented as follows:

n =< t, i, s > (1)

where, t is the textual information of n, i is the image of n,
and s is an assigned status to n whether n is fake or true.

The ELD-FN suggests the status of new news as either ture
or false, where ture represents that the news is real and false
represents that the news is fake. Consequently, the automatic
classification of a new news n could be defined a mapping f :

f : n→ c

c ∈ {ture, false} , n ∈ N (2)

where, c is a suggested status from a news status set (ture,
false).

C. PREPROCESSING
The news may contain inappropriate and unnecessary text,
e.g., English stop-words. Such information is considered
an overhead for the machine learning classification algo-
rithms because of processing time and memory utilization.
Therefore, preprocessing of news text is essential for the
performance of ELD-FN tomake it fast andmemory efficient.
We perform the following preprocessing steps to clean the
text of news.

1) TOKENIZATION
Text tokenization breaks down a piece of text into smaller
units called tokens. Tokens are individual words, phrases,
or other meaningful text elements, which can be analyzed and
processed further.

2) SPECIAL CHARACTER REMOVAL
The text of news may contain special characters, e.g.,
semicolon (;). This step removes the special characters from
the list of tokens.

3) STOP-WORD REMOVAL
English text contains meaningless words that are used to
make sentences meaningful, called stop-words. This step
removes stop-words from the working list.

4) SPELL CORRECTION AND LOWERCASE CONVERSION
This step identifies and corrects the spelling mistakes from
the working list of tokens of news.

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FIGURE 1. The overview of ELD-FN.

5) LEMMATIZATION
The lemmatization step converts higher-degree and compara-
tive words into their lower-degree words, e.g., lemmatization
converts the word darker into dark.
We exploit Python Natural Toolkit (NLTK)2 for the

preprocessing of news. The preprocessed news can be
represented as follows:

n′ =< t ′, i, s > (3)

t ′ =< t1, t2, . . . , tn > (4)

where, t ′ = t1, t2, . . . , tn are the tokens from the text of n after
preprocessing.

D. SENTIMENT ANALYSIS
Sentiment analysis is a NLP technique that involves identi-
fying and extracting subjective information from text, i.e.,
opinions, attitudes, emotions, and sentiments towards a
particular topic. It automatically classifies the polarity of a
text as positive, negative, or neutral. We exploit TextBlob
API3 for the computation of sentiment analysis. The news
(mentioned in Eq. e3) after sentiment computation can be
represented as follows:

n′ =< v, t ′, i, s > (5)

where, v is the sentiment of n′.

E. FEATURE MODELING
This step passes the preprocessed text and images from the
multi-modal dataset to V-BERT to generate the embeddings.
V-BERT is an extension of the BERT model that combines
the power of the BERT model with a visual grounding mech-
anism, allowing it to understand the relationship between the
text and the visual information in an image. This is achieved

2https://www.nltk.org/, accessed on 15-01-2013.
3https://textblob.readthedocs.io/en/dev/, accessed on 15-01-2023.

by combining a region-based visual feature extractor with
the BERT model, where each image region is encoded into
a vector using a CNN. These visual features are concatenated
with the input text, and the resulting sequence is fed into
the BERT model. During training, V-BERT is optimized to
minimize a joint loss function. This allows Visual BERT
to learn language and vision representations in a unified
framework and capture the complex interactions between the
two modalities. The layers/steps involved in ELD-FN for
identifying fake/real news.

1) BERT SHARED LAYER
For the news text, the BERT shared layer is implemented
using a pre-trained Seq2Seq model [8]. The fine-tuning
learning process is required and indispensable to achieve bet-
ter results. To improve its efficiency, separate BERT-shared
layers are adopted for model-to-model textual features.
The output of news text feature extractor OTBERT can be
represented as follows:

OTBERT = BERT T (XT ) (6)

where, BERT T is the relevant BERT-shared layer modeling
for news text and XT is the input representation of textual
data.

2) IMAGE EMBEDDING LAYER
For the news image, Faster-RCNN model [8] is applied to
extract features from the image. The detected objects may
provide visual contexts of the whole picture and be linked to
specific terms through detailed region details. We also add a
position embedding feature to images by encoding the object
location. The output of the image feature extractorOTBERT can
be represented as follow:

OIBERT = BERT I (X I ) (7)

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where, BERT I is the relevant BERT-shared layer modeling
for images, and X I is the input representation of images.

3) PRE-FEATURE EXTRACTION
The BERT-shared layer is strong enough for feature extrac-
tion. It includes a pre-feature extractor to enhance the
ability of BERT to learn semantic characteristics. Pre-
feature extractor consists of the Position-wise Convolution
Transformation (PCT) and the Multi-Head Self-Attention
(MSA) layer.

4) MULTI-MODAL FEATURE CONCATENATION
After extracting the latent features of text and image, these are
concatenated together to obtain the desired multi-modal fea-
ture representations. The multi-modal concatenated features
Of can be represented as follows:

OfBERT = OTBERT + O
I
BERT (8)

F. ENSEMBLE MODEL
Bagging and boosting [46] are two approaches to ensemble
machine learning models. We applied both approaches with
CNN and LSTMmodels. Four different architectures (bagged
CNN, bagged LSTM, boosted CNN, boosted LSTM) of
ensemble machine learning models have experimented using
bagging and bootstrap aggregating to predict the fake/real
news. Note that bagged CNN is the proposed ensemble model
as it yields the other mentioned ensemble architectures. The
predictions through different architectures are made using
Algorithm 1.

IV. EVALUATION
This section constructs the research questions to evaluate
ELD-FN, explains the exploited dataset, defines the metrics
and evaluation process, and reports the findings and threats
to validity.

A. RESEARCH QUESTIONS (RQs)
The following research questions are investigated to evaluate
ELD-FN.
• RQ1:Does ELD-FNoutperform the baseline approaches?
• RQ2: Does news sentiment influence the identification
of fake news?

• RQ3: Does preprocessing influence the identification of
fake news?

• RQ4:Does ELD-FN outperform other classifiers regard-
ing identifying fake news?

The RQ1 compares the ELD-FN with the baseline
approaches [24], [25] names as FakeNED and MultiFND in
the rest of this paper. The reason to select these approaches
as baseline approaches is that both are recently proposed
approaches, closely related to our work and exploited the
same dataset.

The RQ2 investigates the influence of news sentiment to
detect fake news. It evaluates whether positive news will
likely be considered true or vice versa.

Algorithm 1 Ensemble Model
1: procedure Ensemble Model
2: Input: XtT+1, α g

b
,∫ g

b
h (σ,W

g
b
h , b

g
b
h )· · ·

∫ g
b
N g
b

(σ,W
N
g
b
g
b

, b
N
g
b
g
b

)

3: Initialize: ŷt+1, h← 2

4: X
∫ t
1 (σ,W t

1,b
t
1)

−−−−−−−→ y
5: while h ≤ N

g
b do

6: ŷtT+1← ŷtT+1 + α
g
b
∫ g

b
h (σ,W

g
b
b , b

g
b
h ,XtT+1)

7: h = h+ 1
8: end while
9: Output: ŷtT+1
10: end procedure
where, XtT+1 is the feature set at time instances,
α g
b

are Weights of bagging or boosting,∫ g
b
h (σ,W

g
b
h , b

g
b
h )· · ·

∫ g
b
N g
b

(σ,W
g
b
N g
b

, b
g
b
N g
b

) are set of ensembled

bagged or boosted models, ŷtT+1 is the output of the
ensembled model, X is the feature set, Y is the instance of
the output, α is the activation function, and W

g
b
b are Weights

of bagging or boosting models.

The RQ3 examines the impact of preprocessing the news
text to detect fake news.

The RQ4 investigates the impact of different deep-learning
classification algorithms on ELD-FN. We analyze the
ELD-FN and other deep learning approaches to evaluate the
performance of ELD-FN

B. DATASET
The description of the exploited dataset of fake newsFakeddit
is presented in Table 1 which is public (available online4).
Nakamura et al. [47] collected the data from a social news
and discussion website Reddit. It consists of over 1 million
pieces of news (1,063,106) from 22 subreddits. It is classified
in three different ways: 2-way, 3-way, and 6-way. The dataset
samples with 6-way classification are represented in Fig. 2.
Out of the total samples, 59.12% (628,501) and 40.48%
(527,049) are fake and real news, correspondingly. However,
only 64.25% (682,966) samples are multi-modal. Note that
we only use the multi-modal data samples with 2-way
classification to evaluate the proposed approach. Moreover,
Fig. 3 and Fig. 4 represent the wordcloud (most common
words in the dataset) and frequency of thewords, respectively.

C. PROCESS
This section explains the evaluation process of ELD-FN.
After performing the preprocessing and feature modeling
as mentioned in Section III, a 10-fold cross-validation
technique is applied to train and test ELD-FN. The reason for
considering 10-fold cross-validation is that it helps avoid data
biasness and reduces the variance in performance estimation

4https://github.com/entitize/fakeddit, accessed on 15-01-2023.

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FIGURE 2. Dataset Example with 6-way Classification [47].

TABLE 1. Description of fakeddit dataset.

FIGURE 3. Word cloud - most common words in details.

that might be observed with a single train-test split [48].
The dataset’s total multi-modal news N are broken down
into ten (10) slices Ci, where i = 1, 2, . . . , 10. For each
cross-validation, the slices of N are selected that are not
from Ci as training samples (Nt ) and news from Ci as testing
samples (Nv).
A bit-by-bit evaluation process for ith cross-validation is

as follows: 1) All news Nt from N but Ci are extracted
and combined; 2) an ensemble deep learning classifier is
trained on Nt ; 3) a CNN classifier is trained on Nt ; 4)
a LSTM classifier is trained on Nt ; 5) baseline classifiers
are trained on Nt ; 6) we predict whether each news
from the testing samples Ci is real or fake; and 7) the
below-mentioned evaluation metrics are computed for each
classifier.

D. METRICS
We train and test the deep learning classifiers to evaluate
the performance of ELD-FN. We select the most accepted

FIGURE 4. Minimun and maximum words.

metrics (accuracy, precision, recall, and f1-score) for this
purpose. Furthermore, we compute theMCC andOR to check
the effectiveness of the classifiers. The selected metrics can
be presented as follows:

accuracy =
TP+ TN

TP+ TN + FP+ FN
(9)

precision =
TP

TP+ FP
(10)

recall =
TP

TP+ FN
(11)

f 1− score =
2 ∗ precision ∗ recall
pecision+ recall

(12)

MCC =
TP ∗ TN−FP ∗ FN

√
(TP+ FP)(TP+ FN )(TN + FP)(TN + FN )

(13)

OR =
TP/FP
FN/TN

(14)

where, TP and TN are the numbers of correctly predicted
news as real and fake, respectively. Similarly, FP and FN are
the numbers of incorrectly predicted news as real and fake,
respectively.

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TABLE 2. Performance of ELD-FN and baseline approaches.

E. RESULTS
1) RQ1: COMPARISON OF ELD-FN AGAINST BASELINE
APPROACHES
Table 2 and Fig. 5 present the evaluation metrics for three
different approaches (ELD-FN, FakeNED, MultiFND) based
on their accuracy, precision, recall, F1-score, MCC, and
OR. The results advised that the average values of these
metrics for ELD-FN, FakeNED, and MultiFND are (88.83%,
93.54%, 90.29%, 91.89%, 0.49, and 17.02), (89.25%,
91.12%, 87.54%, 89.29%, 0.45, and 15.78), and (78.91%,
85.27%, 76.42%, 80.60%, 0.39, and 13.95), respectively.

The f1-score distribution of cross-validation for ELD-
FN, FakeNED, and MultiFND are presented in Fig. 6.
A beanplot is a visualization that displays a continuous
variable’s distribution across different groups. The beanplot
compares the f1-score distributions by plotting one bean
for each approach. Across a bean, the width of the bean
represents the density of the data, with wider beans indicating
higher density.

The following observations are made from Table 2, Fig. 5,
and Fig. 6.

• ELD-FN has the accuracy (88.83%) and highest pre-
cision (93.54%), indicating that it has the highest
percentage of correctly classified instances and true
positive instances.

• ELD-FN has the highest recall (90.29%) and F1-score
(91.89%), indicating that it has the highest ability
to correctly identify positive instances and achieve a
balance between precision and recall.

• ELD-FN also has the highest MCC (0.49) and OR
(17.02), indicating a better correlation between pre-
dicted and actual classifications and higher odds of event
occurrence than FakeNED and MultiFND. The average
results of MCC (0.49 > 0.45 > 0.39) > 0 and OR
(17.02 > 15.78 > 13.95) > 1 are true for ELD-FN and
confirm its effectiveness.

• The minimum f1-score of ELD-FN is higher than the
maximum f1-scores of FakeNED andMultiFND (shown
in Fig. 6).

To validate the significant difference in the means of
performance (f1-score) for all iterations of ELD-FN, Fak-
eNED, and MultiNED, we perform a single-factor Analysis
of Variance (ANOVA). ANOVA is a statistical method used
to test whether there is a significant difference in the means of
three or more independent groups or samples. It is conducted
on Excell with its default settings and presented in Fig. 7.
It suggests that F > Fcric and p-value < (α = 0.05) are

true for f1-score, and the factor (using different approaches)
significantly differs in f1-score.

Moreover, we utilize two re-sampling methods, over-
sampling and under-sampling to tackle the class imbal-
ance within the dataset. Over-sampling involves generating
additional samples for the minority class through Ran-
domOverSampler, while under-sampling entails removing
surplus records from the majority class in imbalanced
datasets using RandomUnderSampler. The findings reveal
that employing under-sampling results in accuracy, precision,
recall, and F1-score values of 86.12%, 92.54%, 88.76%,
and 90.61%, respectively. However, it’s important to note
that under-sampling diminishes the number of majority class
samples, leading to a loss of information. Consequently,
the performance of both majority and minority classes in
the fine-tuned BERT model declines when under-sampling
is applied. Likewise, utilizing the over-sampling technique
yields accuracy, precision, recall, and F1-score values of
90.26%, 94.37%, 91.88%, and 93.11%, respectively. This
enhancement is attributed to BERT being exposed to a
larger dataset, enabling it to learn meaningful patterns more
effectively.

The preceding analysis concluded that ELD-FN outper-
forms the baseline approaches in detecting fake news.

2) RQ2: INFLUENCE OF SENTIMENT ON ELD-FN
The evaluation results of ELD-FNwith andwithout sentiment
analysis are presented in Table 3 and Fig. 8. The evaluation
results of ELD-FN for different settings of sentiment
(enable/disable) based on their accuracy, precision, recall, F1-
score, MCC, and OR are (88.83%, 93.54%, 90.29%, 91.89%,
0.49, and 17.02) and (88.12%, 90.38%, 89.98%, 90.17%,
0.49, and 17.02), respectively.

From Table 3 and Fig. 8, it is observed that Disabling sen-
timent (i.e., textual features only) brings out the significant
difference in precision from 93.54% to 90.38% and f1-score
from 91.89% to 90.17%. However, MCC and OR remain the
same.

Table 5 represents the relationship between sentiment and
news. It presents that 65.84% of negative news are real,
whereas only 34.16% of the positive news are real. However,
73.71% of negative news are fake, whereas only 26.29%
of the positive news are fake. It means the possibility of
spreading fake news is 180.37% = (73.71% - 26.29%) /
26.29%, if the news is negative. For example, if a fake news
article portrays a political figure negatively, it can contribute
to a negative sentiment towards that figure among the public
and will propagate quickly.

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M. Luqman et al.: Utilizing Ensemble Learning for Detecting Multi-Modal Fake News

FIGURE 5. Performance of ELD-FN and baseline approaches.

FIGURE 6. Distribution of f-measure.

The preceding analysis concluded that sentiment and fea-
tures are critical for detecting fake news and disabling either
would significantly reduce the performance of ELD-FN.

3) RQ3: INFLUENCE OF PREPROCESSING ON ELD-FN
The evaluation results of ELD-FN with and without prepro-
cessing are presented in Table 4 and Fig. 9. The evaluation
results of ELD-FN for different settings of preprocessing
(enable/disable) based on their accuracy, precision, recall, F1-
score, MCC, and OR are (88.83%, 93.54%, 90.29%, 91.89%,
0.49, and 17.02) and (88.49%, 92.95%, 90.11%, 90.50%,
0.49, and 17.02), respectively.

From Table 4 and Fig. 9, it is observed that disabling
preprocessing brings out the significant difference in accu-
racy from 88.83% to 88.12%, precision from 93.54% to
92.95%, recall from 90.29 to 90.11, and f1-score from
91.89% to 90.50%. However, MCC and OR remain the
same.

The preceding analysis concluded that text preprocessing
and features are critical for detecting fake news and

FIGURE 7. ANOVA analysis on performance comparison.

disabling either would significantly reduce the performance
of ELD-FN.

4) RQ4: COMPARISON OF ELD-FN AGAINST OTHER
CLASSIFIERS
We select off-the-shelf deep learning classifiers (CNN and
LSTM), the most widely adopted and well-known. Note
that the preprocessed text, their sentiment, and feature
embeddings are given as input to the selected classifiers for
comparative analysis. We set hyper-parameters’ values as
dropout = 0.2, recurrent_dropout = 0.2, loss function =
binary-crossentropy, and activation = sigmoid for ELD-FN
and both baseline approaches.

Table 6 and Fig. 10 present the evaluation metrics for ELD-
FN, CNN, and LSTM based on their accuracy, precision,
recall, F1-score, MCC, and OR. The results advised that the
average values of these metrics for ELD-FN, FakeNED, and
MultiFND are (88.83%, 93.54%, 90.29%, 91.89%, 0.49, and
17.02), (86.73%, 92.56%, 85.81%, 89.06%, 0.48, and 16.97),

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M. Luqman et al.: Utilizing Ensemble Learning for Detecting Multi-Modal Fake News

TABLE 3. Influence of sentiment on ELD-FN.

TABLE 4. Influence of preprocessing on ELD-FN.

FIGURE 8. Influence of sentiment on ELD-FN.

and (86.51%, 90.22%, 86.19%, 88.21%, 0.48, and 16.92),
respectively.

The following observations are made from Table 5 and
Fig. 10.

• ELD-FN outperforms CNN and LSTM. The perfor-
mance enhancement of ELD-FN upon CNN in accuracy,
precision, recall, f1-score, MCC, and OR is 2.42%,
1.06%, 5.22%, 3.18%, 0.01, and 0.05, respectively.
However, the performance enhancement of ELD-FN
upon LSTM in accuracy, precision, recall, f1-score,
MCC, and OR is 2.68%, 3.68%, 4.76%, 4.17%, 0.01,
and 0.10, respectively.

• ELD-FN performs better than LSTM because LSTM
requires short text and performs sequential processing,
which is unnecessary in our case. In contrast, CNN is
proven efficient for long text and works better to extract
local invariant features.

The preceding analysis concluded that ELD-FN outper-
forms other classifiers in detecting fake news.

F. THREATS TO VALIDITY
The probability of incorrect labeling of news is the first
threat to construct validity. This research assumes that the

TABLE 5. Relation between sentiment and news.

assigned labels by Nakamura et al. [47] are correct. However,
incorrect labeling of data may cause the productivity
of ELD-FN.

The choice of assessment metrics of ELD-FN is another
threat to construct validity. The chosen metrics for detecting
news are the most accepted in the literature for the
classification task.

The choice of the sentiment analysis repository is the first
threat to internal validity. The chosen repository III-E has
been public and has good results in computing sentiment.
Exploiting other repositories may cause the productivity of
ELD-FN.

ELD-FN, FakeNED, and MultiFND coding is the second
threat to internal validity. The coding and the produced
results of ELD-FN, FakeNED, and MultiFND are verified to
mitigate the threat. However, unknown errors may cause the
productivity of ELD-FN.

The hyper-parameters setting of ELD-FN is the third threat
to internal validity. The hyper-parameters setting for ELD-FN

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M. Luqman et al.: Utilizing Ensemble Learning for Detecting Multi-Modal Fake News

FIGURE 9. Influence of preprocessing on ELD-FN.

TABLE 6. Comparison of ELD-FN against other classifiers.

FIGURE 10. Comparison of ELD-FN against other classifiers.

is mentioned in Section IV-E4. The change in settings may
cause the productivity of ELD-FN.

V. CONCLUSION AND FUTURE WORK
Automatic fake news detection is crucial to avoid spreading
false information that can have serious consequences, ranging
from reputational damage to social and political unrest.
In some cases, fake news can even incite violence and

lead to harm or loss of life. Therefore, the ability to
automatically identify and flag false information can help
mitigate the threats of fake news. From this perspective, this
paper proposes an ensemble deep learning-based detection
of fake news. The proposed approach leverages NLP
techniques for preprocessing textual information of news,
computes the sentiment from the text of each news, generates
embeddings for text and images of the corresponding news

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M. Luqman et al.: Utilizing Ensemble Learning for Detecting Multi-Modal Fake News

by leveraging V-BERT, and passes the embeddings to the
deep learning ensemble model for training and testing. The
evaluation results significantly outperform the state-of-the-
art approaches in identifying fake news.

In future, we would like to investigate the need to adapt
detection algorithms to new types of media. Fake news
is not limited to text-based content, and algorithms must
be able to detect false information in images, videos, and
audio as well. Moreover, we are interested in improving
the interpretability of detection algorithms. Current methods
often rely on opaque deep learningmodels, making it difficult
to understand how decisions are being made. Future work
could focus on developing more transparent models or tools
that help users understand how algorithms arrive at their
conclusions.

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MUHAMMAD LUQMAN received the bachelor’s
degree in computer science from the University
of Gujrat, Pakistan, in 2017, and the master’s
degree in computer science from Northwestern
Polytechnical University, China. He is currently a
young Scholar in the field of computer science.
His research interests include wide spectrum,
primarily focusing on cutting-edge fields, such
as artificial intelligence, deep learning, and data
mining.

MUHAMMAD FAHEEM (Member, IEEE)
received the B.Sc. degree in computer engineering
from the Department of Computer Engineering,
University College of Engineering and Tech-
nology, Bahauddin Zakariya University, Multan,
Pakistan, in 2010, the M.S. degree in computer
science from the Faculty of Computer Science
and Information Systems, Universiti Teknologi
Malaysia (UTM), Johor Bahru, Malaysia, in 2012,
and the Ph.D. degree in computer science from

the Faculty of Engineering, UTM, in 2021. From 2012 to 2014, he was
a Lecturer with the COMSATS Institute of Information and Technology,
Pakistan. From 2014 to 2022, he was also an Assistant Professor with the
Department of Computer Engineering, Abdullah Gul University, Turkey.
He is currently a Researcher with the School of Computing (Innovations
and Technology), University of Vaasa, Vaasa, Finland. He has authored
several papers in refereed journals and conferences. His research interests
include cybersecurity, blockchain, artificial intelligence, smart grids, and
smart cities. He served as a reviewer for numerous journals in IEEE, Elsevier,
Springer, Wiley, Hindawi, and MDPI.

WAHEED YOUSUF RAMAY received the Ph.D.
degree from the University of Science and Tech-
nology Beijing (USTB) China. He is currently an
Assistant Professor with Air University. His aca-
demic and clinical focus is the use of algorithms
(deep learning, machine learning, and big data
analysis), advanced text analysis techniques, and
sentiment analysis.

MALIK KHIZAR SAEED received the B.S. degree
in information technology from the University
of Gujrat, Gujrat, Pakistan, in 2013, and the
M.S. degree in computer science from COMSATS
University Islamabad, Vehari Campus, Pakistan.
He is currently working as a Visiting Lecturer
at COMSATS University Islamabad. His research
interests include machine learning, deep learning,
and artificial intelligence. He is also interested
in classification-related tasks using different ML
approaches.

MAJID BASHIR AHMAD received the master’s
degree in computer science from COMSATS Uni-
versity Islamabad, Pakistan, in 2014, and the M.S.
degree in computer science from The University
of Lahore, Pakistan, in 2019. He is currently
a Research Scholar in the field of computer
science. His research interests include artificial
intelligence, machine learning, and data mining.

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