Fake News Detection‑Classify Articles as Real or Fake Using NLP
N. Bhuvaneswari, K. S. Karthik and S. Naveen and B. Jayaprakash
Department of Computer Science and Engineering, Nandha Engineering College, Erode, Tamil Nadu, India
Keywords: Digital Media Credibility, TF‑IDF, Word Embeddings, Support Vector Machine (SVM), Logistic Regression,
Naive Bayes, LSTM, Transformers, Misinformation Detection, Fake News Detection, Natural Language
Processing (NLP), Machine Learning, Deep Learning, Text Preprocessing, TF- IDF.
Abstract: The objective of fake news detection using NLP is to classify news as real or fake. In order to achieve this, a
labeled dataset of both accurate and fake news articles is paramount. This text is pre-processed (text cleaning,
stop word removal, and normalization). Next, it will be transformed into numerical representations using
methods like word embeddings, TF-IDF or Bag-of-Words. In addition, to various machine learning models
such as Naive Bayes, SVM, and Logistic Regression, deep learning approaches such as transformers and
LSTMs are evaluated to enhance precision. We use standard benchmark datasets to evaluate the performance
of the system in distinguishing between real and fake news. This research increases the credibility of digital
media as it helps in the automated detection of disinformation.
1 INTRODUCTION
Given the harmful impact of false information on
politics, society and business the growing recognition
that fake news is a major issue in the digital age.
Misinformation that is deliberately presented as
news in conducted to misinform or persuade the
populace is called news fake. To conquer these crises,
advanced technologies are needed, and Natural
Language Processing (NLP) is a viable solution, as it
enables computers to understand, recognize, and
analyze human language. By employing NLP
methods, one can develop a system that can
autonomously ascertain the veracity of news stories
as real or false based on the candidature of the text.
Extracting such key features like word frequencies;
sentence structures and usages; and contextual
patterns are important for classifying news items.
Text data is transformed into numeric vectors
through the different techniques like Bag-of-Words,
TF-IDF and word embeddings for feeding in the
model written above. Various algorithms (including
Logistic Regression, Support Vector Machines
(SVM), Naive Bayes with deep neural network
architectures (such as LSTMs, transformers) improve
accuracy.
NLP (natural language processing), along with
machine learning, are used to enhance the accuracy
of false news detection programs that help improve
the trustworthiness of digital media and limit the
spread of false information. It creates such automated
mechanisms, and therefore where to enjoy reliable
news as well as the source of that information is
avoided.
2 RELATED WORKS
(1) It appears you are referring to the two-
dimensional journal paper, "Fake News Detection
Using Deep Learning and Natural Language
Processing," from M. Al-Alshaqi et al. from 2025. If
you’re interested in other works on this topic with
similar themes, here are a wealth of research work
revolving around fake news detection, deep learning,
and natural language processing (NLP). Which seems
to be referring to the paper "Classifying Fake News
Articles Using Natural Language Processing and
Machine Learning" by M. Kula et al. (2019). You're
referring to the work ' Natural Language Processing
Based Online Fake News Detection Challenges – A
Detailed Review' by S. Kaur and S. Kumar, 2020.
You might be referring to paper called Fake News
Detection Using NLP byA Kumar et al. This study
focuses on NLP techniques that potentially can be
used to detect fake news. 2021 Using NLP
techniques with logistic regression model can be 2024
2021 analyze elements that can convincingly be 2020
562
Bhuvaneswari, N., Karthik, K. S., Naveen, S. and Jayaprakash, B.
Fake News Detectionâ
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SClassify Articles as Real or Fake Using NLP.
DOI: 10.5220/0013902000004919
Paper published under CC license (CC BY-NC-ND 4.0)
In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies (ICRDICCT‘25 2025) - Volume 3, pages
562-567
ISBN: 978-989-758-777-1
Proceedings Copyright © 2025 by SCITEPRESS – Science and Technology Publications, Lda.

2022 FORMULATION. Some relevant work in this
area has focused on combining machine learning
algorithms, including Logistic Regression with NLP
techniques for fake news detection. A. Kumar et al.
(2021). This paper probably explores how both
machine learning (ML) algorithms and natural
language processing (NLP) techniques are combined
for the effective detection of fake news. This
includes the paper “AI-Assisted Deep NLP-Based
Approach for Prediction of Fake News from Social
Media Users” By A. Kumar et. al (2023). This is
probably how deep learning techniques applied with
NLP and integrated can have such effect on the
document. by A. Kumar et al. (2025). And so on This
paper may describe the state-of-the-art techniques,
issues and limitations with respect to the automatic
detection of fake news using Natural Language
Processing (NLP) A. Kumar et al. (2021). So time to
predict a paper which is specific that studies the uses
of machine learning (ML) and natural language
processing (NLP) techniques in detecting fake news
in social media platforms. Here depart more of the
related works regarding machine learning, analytic
and fake news detection or production discusses
recent advances in algorithms and methodologies for
Natural Language Processing (NLP) to track and
counter this kind of disinformation. explain the usage
of document embeddings in fake news detection
focusing on the ability to represent entire articles
distinguishing between both types of news.
2.1 Traditional Machine Learning
Approaches
The original research studies involved in fake news
detection used standard machine-learning models
such as Logistic Regression, Support Vector
Machines (SVM), Decision Trees, and Naïve Bayes.
These models utilize engineered text features such as
word frequency, term presence, and grammatical
structures for the classification of fake news and real
news. For instance, Potthast et al. (2018) analyzed
lexical and stylistic differences between fake news
and real news stories and found fake news to use
overblown language and emotional appeals.
2.2 Feature Engineering and NLP-
Based Approaches
Fake news classification is one of the essential parts
of feature engineering. Methods like Bag-of-Words
(BoW), Term Frequency-Inverse Document
Frequency (TF-IDF), and n-grams have been used by
scientists to convert text-based data into numerical
features. Other works include using sentiment
analysis and readability scores to find manipulative
language in fake news articles. Work such as Rashkin
et al. (2017) argues that fake news uses emotionally
manipulative words to mislead readers.
2.3 Deep Learning and Neural
Network Strategies Classify
In the past few years deep learning has made a rapid
advancement in detecting fake news. Models such as
RNNs, LSTMs, and transformers like BERT and GPT
have been leveraged to recognize fake news by
capturing contextual patterns in textual data. Wang et
al. (2020) proposed a LSTM- based approach to
analyze word sequences and word dependencies with
a greater performance than traditional models. Also,
Zhou et al. (2021) used transformers to encourage
feature learning and context understanding and
outperformed previous methods on benchmark data.
2.4 Hybrid Approaches and
Multimodal Detection
Recent studies have shown the combined of different
methods to improve detection. Hybrid models merge
these two approaches (Machine Learning and Deep
Learning) in order to take advantage of hand-
engineered features as well as automatic feature
extraction. Combined attention-based models have
also been explored where information in terms of data
(images, videos and user interactions) is provided for
classification. Jin et al. (2022) underscores the
strength of combining text-based and image- based
models to identify misinformation in social media.
3 DATASET COLLECTION AND
PRE: PROCESSING
A well- labeled dataset containing both real and fake
news articles is the first step for the purpose of fake
news detection. A number of benchmark datasets that
can be downloaded publicly have been leveraged in
research. Popular datasets include the LIAR Dataset,
which consists of short claims tagged as true, half-
true, or false using sources such as Politifact; Fake
News Net, a largescale dataset of fake and real news
articles and post metadata including user engagement
and publisher credibilit
A. ISOT Fake News Dataset, comprising news
articles categorized as real or fake from legitimate
news websites and fictitious sites; and BuzzFeed and
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Facebook Fake News Dataset, representing fact-
checked news articles that were shared over social
media platforms. Aside from these datasets,
researchers can gather data through scraping online
news sources, social media sites, or factchecking
sites such as Snopes and FactCheck. org. To avoid
bias in the model, it is important to have a balanced
number of real and fake news articles in the dataset.
Additional information for categorization might be
gleaned from metadata like publication date, author,
and social media engagement metrics.
B. Data Pre-processing Once the dataset is
collected, it undergoes pre-processing to clean and
structure the textual content for analysis. Raw news
articles contain noise such as punctuation, stop words,
special characters, and inconsistent formatting, which
can affect model performance. The key pre-
processing steps include text cleaning, where special
characters, punctuation, and numbers are removed,
and text is converted to lowercase to ensure
uniformity. Additionally, HTML tags and URLs from
web-scraped content are eliminated. Stop word
removal is performed using libraries like NLTK and
SpaCy to filter out common words such as "the," "is,"
and "and," which do not contribute significant
contextual information. Tokenization and
lemmatization are then applied to split text into
individual words or phrases and convert words into
their base form (e.g., "running" → "run"), improving
the model’s ability to understand word variations. To
make text machine-readable, text vectorization
methods such as Bag-of-Words (BoW), Term
Frequency-Inverse Document Frequency (TF-IDF),
and word embeddings like Word2Vec, GloVe, or
transformer-based embeddings like BERT are used to
capture contextual meaning. Another crucial step is
handling class imbalance, where techniques like
oversampling the minority class, undersampling the
majority class, or using Synthetic Minority Over-
sampling Technique (SMOTE) help ensure balanced
training data. Effective pre-processing enhances
model efficiency, reduces noise, and allows machine
learning models to extract relevant patterns for
accurate fake news classification, ultimately
improving the reliability of automated
misinformation detection systems.
4 PROPOSED METHODOLOGY
One of the significant applications of Natural
Language Processing (NLP) is fake news detection,
tasked with classifying news articles as fake or real.
The prevalence of misinformation on any digital
media is increasing, making the development of an
automatic system to evaluate the authenticity of news
content very high important. The steps followed in
our methodology are data collection, preprocessing,
feature extraction, model training, and testing with a
good and effective classification system.
The process begins with Data collection, where a
diverse dataset of real and fake news articles are
collected from trusted sources. Datasets usually
publicly available such as LIAR, FakeNewsNet, or
Kaggle datasets consist of labeled samples of news
that must serve as a base for training and testing.
These datasets include news headlines, contents and
meta data which enable the model to learn the
language patterns of false as well as true news. While
news stories are probably biased towards certain
issues, such as data covering various sources and
categories are useful in improving the generalizability
of the model. After data is collected, preprocessing is
done to clean and normalize text.
This operation keeps only essential parts like
punctuation, stopwords, special characters and
HTML tags and converts text into a normalized form,
mostly sometimes lower case. Text is split into words
or phrases using tokenization, and lemmatization or
stemming is used to reduce words to their root words.
These process help eliminate redundancy and
increase feature extraction, only useful parts of text
contribute towards the classification model.
Therefore, in order to transform the raw text into
numerical values readable by the machine learning
models, the first thing is to do a feature extraction.
Traditional methods such as Term Frequency-Inverse
Document Frequency (TF- IDF) and Bag of Words
(BoW) are commonly used to represent the
importance and frequency of words in a document.
More advanced approaches that use word
embeddings (e.g., Word2Vec, GloVe, contextual
embeddings with transformer models like BERT) that
capture the meaning between words. In addition to
text-based features, metadata such as source
credibility, writing style, and sentiment could be
additional sources of information used to help the
model differentiate between real and fake news.
Trained on machine learning and deep learning
models perform classification to identify patterns
over the extracted features. Other option classifier
logistic regression, SV, and Random forest seem to
work well in previous research. However, more
recent deep learning networks like Long-Short Term
Memory (LSTM) architectures, Convolutional
Neural Networks (CNN) and BERT with
Transformer architecture have been shown to
achieve superior performance on most NLP
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problems.
These models can learn contextual and sequential
relationship sintextand can show differences at fine-
grained linguistic levels between true and false news.
A hybrid Approach use of multiple models can
improve overall accuracy and resiliency. The model
is learned from a labeled dataset, where it is able to
decide whether the given news article article is real
or fake based on the features that were extracted. All
the possible methods like oversampling and data
augmentation can be invoked to counter a class
imbalance problem in training so that the model does
not lean towards any appreciated class.
Regularization techniques like dropout and batch
normalization are also used to prevent overfitting
and allow them model to improving their ability to
generalize to new data. Compared to previous
models, hyperparameter optimization is performed
to derive properties such as learning rate, batch size,
activation functions, etc.
The model is assessed in the same usual way we
do so, by computing accuracy, precision, recall, and
f1-score. Also, a confusion matrix reveals the
classification performance of the model and where it
could fail, such as false positives or false negatives.
Methods that validate performance based on mean k
based cross-validation ensure that the performance of
the model is independent of a particular dataset Split
thus cementing reliability. Once performance gaps
have been identified, tuning the model using
additional data, or more advanced techniques such as
transfer learning, can improve its accuracy even
further. When the model, with a satisfactory accuracy
is achieved, it can be deployed as a web-based
application or added within social media sites to
verify and flag the fake news in real-time. The system
can be trained to read news articles, classify them as
real or fake, and return justifications based on critical
linguistic features. Also Leveraging fact-checking
sources and mechanisms for user feedback can
further fine-tune the model, ensuring it learns and
evolves continually to detect fake news effectively.
Figure 1 shows the flow of detection.
Lastly, the proposed approach uses NLP
techniques to classify news articles as real or fake,
which presents a growing challenge due to
disinformation. The proposed system will be able to
extract news from various articles and able to classify
news into true and false by combining data
preprocessing, feature extraction, machine learning,
and evaluation methodologies. Improvements may
include better deep learning algorithms, utilizing
external fact checking resources, and developing
multilingual fake news tools to combat
disinformation worldwide.
Figure: 1. Flow of detection.
5 EXPERIMENTAL RESULTS
The model for detecting fake news was tested with a
benchmark dataset, like LIAR or FakeNewsNet,
consisting of both real and false news articles. The
dataset was split into training and test sets, with 80%
for training and 20% for testing. A variety of
machine learning and deep learning models were
tried, such as Logistic Regression, Support Vector
Machines (SVM), Random Forest, Long Short-Term
Memory (LSTM) networks, and BERT.
Tuned all models based on accuracy, precision,
recall, and F1-score. The accuracy for the traditional
models like Logistic Regression and SVM was
around 80%, and Random Forest had a marginal
improvement with an accuracy of 83%. However,
deep learning models like LSTM and BERT
outperformed traditional methods, achieving 87%
accuracy and 92% accuracy, respectively. Its high
performance is attributed to its ability to understand
the contextual relations in text.
The analysis of the confusion matrix established
that the model was able to clearly separate real and
fake news with little false positives and false
negatives. Cross-validation established the model's
solidity to make it perform on unseen data. In
summary, the experimental results show that NLP
models, in this case, transformer models such as
BERT, greatly enhance fake news classification
accuracy, hence suitable for application in the real
world. Figure 2 gives the accuracy comparison.
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Figure 1: Comparison of model accuracy.
6 CONCLUSIONS
The rising dissemination of fabricated news is a great
challenge to society, shaping public opinion and
decision- making. The project proved the efficacy of
Natural Language Processing (NLP) in automatically
identifying news stories as authentic or fabricated
through machine learning and deep learning
methods. The methodology included data gathering,
preprocessing, feature extraction, model training, and
evaluation, which ensured a systematic approach to
detecting fake news.
Misinformation Out of Machine Learning: From
our experimental results, typical machine learning
models (Logistic Regression, SVM and Random
Forest) deliver fairly accurate partition between
true/false news. While conventional methods,
especially LSTM networks and BERT, provided
better performance, deep learning models were able
to capture certain contextual relationships and detect
complex patterns inherent in language. BERT
registered the highest accuracy of 92% (the first time
that a transformer- based models were proven to
excel in NLP tasks).
These research results emphasize the
effectiveness of NLP techniques, and highlight the
benefit of reducing reliance on human-based fact-
checking systems by overcoming the limitations of
traditional fact-checking methods, allowing for
identifying false news in a very large corpus. In
addition, the performance of the model was improved
by using advanced feature extraction such as word
embeddings, and sentiment analysis. By designing
the framework that captures news data in an elaborate
way, HAVENT could exploit the news data to ensure
the model would be capable of handling all sort of
topics that news has reported on, which allows the
model to be useful in real life applications.
Despite these successes, challenges remain —
such as handling misinformation in many languages,
including new subtle patterns of bias, and adapting to
the evolution of fake news techniques. These deep
learning architectures can be further improved, and
future systems can incorporate different external fact-
checking sources, develop multilingual detection of
fake news, etc. Additionally, we can implement our
model as a web-based application or browser
extension that can help users with news credibility
assessment in real time.
In conclusion, this study demonstrates the
potential of NLP-based models in the fight against
misinformation. Through continuous improvement of
detection algorithms and the integration of real-time
verification mechanisms, we can create an
information ecosystem that is more credible and
reliable, helping to combat the ramifications of fake
news on our society.
REFERENCES
Al-Alshaqi, M., et al. (2025). "Fake News Detection Using
Deep Learning and Natural Language Processing."
IEEE Conference Publication.
Ghadiri, Z., et al. (2022). "Automated Fake News Detection
Using Cross-Checking with Reliable Sources." arXiv
preprint.
Granmo, O.-C., et al. (2024). "Explainable Tsetlin Machine
Framework for Fake News Detection with Credibility
Score Assessment." IEEE Access.
He, L., Hu, S., & Pei, A. (2023). "Debunking
Disinformation: Revolutionizing Truth with NLP in
Fake News Detection." arXiv preprint.
Holtzman, A., et al. (2023). "Neural Text Generation: A
Practical Guide." IEEE Transactions on Neural
Networks and Learning Systems.
Kaur, S., & Kumar, S. (2020). "Natural Language
Processing Based Online Fake News Detection
Challenges – A Detailed Review." IEEE Conference
Publication.
Kula, M., et al. (2019). "Classifying Fake News Articles
Using Natural Language Processing and Machine
Learning." IEEE Conference Publication.
Kumar, A., et al. (2021). "Fake News Detection Using
Natural Language Processing and Logistic Regression."
IEEE Conference Publication.
Kumar, A., et al. (2021). "Fake News Detection Using
Machine Learning and Natural Language Processing."
IEEE Conference Publication.
Kumar, A., et al. (2021). "A Survey on Role of Machine
Learning and NLP in Fake News Detection on Social
Media." IEEE Conference Publication.
Kumar, A., et al. (2023). "AI-Assisted Deep NLP- Based
Approach for Prediction of Fake News from Social
Media Users." IEEE Journals & Magazine.
Kumar, A., et al. (2025). "Fake News Detection Using
NLP." IEEE Conference Publication.
ICRDICCT‘25 2025 - INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION,
COMMUNICATION, AND COMPUTING TECHNOLOGIES
566

Kumar, A., et al. (2025). "A Comprehensive Survey on
Automatic Detection of Fake News Using Natural
Language Processing: Challenges and Limitations."
IEEE Conference Publication.
Mihalcea, R., & Strapparava, C. (2024). "The Role of
Linguistic Features in Automatic Deception Detection."
IEEE Transactions on Affective Computing.
Nakov, P., et al. (2024). "Detecting Fake News on Social
Media: Challenges and Opportunities." IEEE Internet
Computing.
Shu, K., et al. (2024). "Combating Disinformation on Social
Media: A Data Mining Perspective." IEEE
Transactions on Knowledge and Data Engineering.
Singh, J., et al. (2024). "LingML: Linguistic- Informed
Machine Learning for Enhanced Fake News
Detection." arXiv preprint.
Truică, C.-O., & Apostol, E.-S. (2023). "It's All in the
Embedding! Fake News Detection Using Document
Embeddings." arXiv preprint.
Wong, K.-F. (2024). "Natural Language Processing
Techniques for Fake News Detection." IEEE
Conference Publication.
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