The document summarizes a presentation on challenges and future scope in fake news detection. It discusses how researchers have used various machine learning and deep learning techniques like SVM, logistic regression, neural networks to detect fake news. However, deep learning approaches have provided better results. It also notes limitations in existing approaches and need for hybrid models combining different techniques. The presentation outlines challenges like exploring unused data parameters, developing hybrid approaches, enabling real-time identification, detecting fake news across languages and tracking user credibility.

1. Scopus Index Springer International conference
On Recent Evolution Drives and e-vehicles
(REEDeV-2022)
Paper ID -48 (Track 3)
Challenges and future scope in fake news
detection: A survey
Presented By: Mr. Nachiket A. Rathod ,HVPM COET, Amravati
Guided By : Dr. Prabhakar L. Ramteke, HVPMCOET, Amravati
Date: 16th September 2022
Venue: ST. VINCENT PALLOTI College of Engineering &Technology , Nagpur

2. Abstract:
 Since the turn of the millennium, more people are
interacting and sharing content on social media.
 People now rely heavily on social media as a source of
news and information.
 Researchers have determined that fake news is a big
problem that requires attention.
 In this paper, we analysed a number of studies that
looked at the methods used by authors to identify fake
news.
 We supplied a tabular format that included all of the
reviewed papers' methods, datasets, and projected
futures. Approaches and difficulties are noted from a
literature review
Presented By: Mr. Nachiket Rathod

3. Introduction
• In today’s world, social media users produce huge amounts
of data in a day [1]. The majority of data produced and
exchanged online using social media is news [2]. Because of
the increased use of social media, users are more likely to
spread fake news. Fake news is material that has been
released with the goal of deceiving readers.
• On social media, fake news spreads quickly because it is
produced in a way that makes it seem like a real, reliable
piece of information. Such news is disseminated with the
intention of gaining financial and political advantage and
has a chance to influence public opinion.
• The circulation of fake content is rising in order to gain
popularity on social media and divert attention away from
real issues. Fake news has a severe negative effect on
society, which leads to an imbalanced news ecosystem.
Presented By: Mr. Nachiket Rathod

4. Literature survey
Reference Method Datasets Used Research Gap
[6] DNN FNC-1 Re-evaluate feature and pre-
processing
[7] NB, SVM, LR LIAR Deep Learning Neural
Network approach would be
promising
[8] BERT
InferSent
Fakeddit Metadata and comment data
can be used from datasets for
tracking users credibility
[9] Novel Hybrid deep
learning model
ISOT &
FA-KES
DNN should be implemented
on model
[10] Ensemble based Deep
learning model
LIAR Model need to test on large
datasets
[11] DNN PolitiFact
&
BuzzFeed
Perform text-based
categorization of news stories
in real-time.
Presented By: Mr. Nachiket Rathod

5. Literature survey
Reference Method Datasets Used Research Gap
[12] Classifiers: BayesNet,
Logistic, RandomTree,
NaiveBayes
Facebook, Forex and
Reddit.
Test effectiveness against the
new datasets.
[13] Ensemble Approach: NB,
DT, SVM & NN
News Trends, Kaggle
and Reuters
Real-time fake news detection
on social media platforms
[14] FNDNet model Kaggel Multiclass fake news detection
problem.
[15] KNN, Random Forest,
Gaussian Naïve Bayes
and SVM.
TwitterBR,
FakeBrCorpus,
FakeNewsData1,
FakeOrRealNews and
btvlifestyle.
Fake news detection from
social platform in other
languages
[16] Machine learning
ensemble approach.
Kaggle and ISOT Real-time identification of
false news in videos
Presented By: Mr. Nachiket Rathod

6. Literature survey
Reference Method Datasets Used Research Gap
[17] NLP, Machine Learning
and deep learning
Kaggle Implement model on new large
datasets
[18] LSTM & GRU neural
network
ISOT & GRaFN Develop model to distinguish
those sub-categories.
[19] Deep Learning based
SPOT approach
Twitter Extending SPOT approach by
selecting the useful feature over
massive features using feature
selection methods.
[20] CNN model Kaggle Apply the model to huge
datasets and include various
information in various contexts
[21] Neural Network TI-CNN and Fake
News Corpus
To collect more data from a
recent period of time.
Presented By: Mr. Nachiket Rathod

7. Literature survey
Reference Method Datasets Used Research Gap
[22] RNN & LSTM, Clickbait Corpus
2017
To extract pair of headline and
body for identifying relationship
with knowledge base
[23] Classification model FakeNewsNet &
PolitiFact
To implement model on newly
developed state of art algorithms
[24] DeepNet model Fakeddit &
BuzzFeed
To take into account temporal
data for efficient false news
identification.
[25] FakeBERT Model FakeBERT To submit an application for the
multi-class and binary real-world
fake news datasets.
[26] CNN, LSTM , Bi-LSTMCollection of articles
in Philippines.
To implement model on other
datasets
Presented By: Mr. Nachiket Rathod

8. Literature survey
Reference Method Datasets Used Research Gap
[27] Bi-RNN ISOT To combine other models like
CNN, GRU and LSTM with
Proposed Model
[28] Bi-MPM Fact DB Compare with other models
[29] DNN, ANN, CNN and
RNN.
LIAR & Kaggle Implement model with other
datasets
Presented By: Mr. Nachiket Rathod

9. Approaches for Fake News Detection
• The researchers have employed a variety of techniques for detecting fake
news. A machine learning approach is used by researchers, as seen in the
literature review. Support vector machines, logistic regression, Naive Bayes,
Random Forest, K-Nearest Neighbor, and other machine learning methods
During a literature review, the application of DL algorithms for fake news
detection, such as CNN, recurrent neural networks, deep neural networks, long
short-term memory, and bi-directional LSTM, was noted.
• The results and accuracy achieved by the machine learning approach have
limitations; that’s the reason most researchers shifted to the deep learning
approach, which gave better results as compare to Machine Learning
approach. Different ensemble models as well as a hybrid approach are also
being developed, which consist of combinations of different machine learning
algorithms and deep learning algorithms.
Presented By: Mr. Nachiket Rathod

10. Challenges
• Exploring unused parameters in Multimodal
datasets
• Hybrid approach
• Real Time Identification
• Detection of false information in several languages
• Tracking a user’s credibility
Presented By: Mr. Nachiket Rathod

11. Conclusion
• The world needs to address the serious problem of fake news
since it could have negative effects. The various research
techniques and datasets are displayed in tabular form, along
with the potential applications of all papers under
consideration.
• As evidenced by the meticulous examination of multiple
publications in the fake news field, there is a definite need for
study on the detection of false news using deep learning
algorithms on big multimodal datasets that include text,
pictures, and video. Real-time false news detection using
deep learning models on text, video, and image data is still a
relatively new field of study in academia.
Presented By: Mr. Nachiket Rathod

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Presented By: Mr. Nachiket Rathod

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Presented By: Mr. Nachiket Rathod

15. Thank You !
Presented By: Mr. Nachiket Rathod