fake news detector

1. DETECTION OF FAKE NEWS WITH ML
Supervisor:
DR MAHENDRA KUMAR
Presented by:
SANDEEP MISHRA(20106089)
Dr. B.R. Ambedkar National Institute of
Technology, Jalandhar-144011
Minor Project Presentation (ICCI-300)
Instrumentation and Control
Engg.
Dr. B.R. Ambedkar NIT

2. Index
• INTRODUCTION
• IMPORTANTTERMS
• LITERATURE REVIEW
• RESEARCH OBJECTIVE
• PROPOSED METHEDOLOGY
• STEPS
• RESULT
• CONCLUSION AND DISCUSSION
• REFERENCES

3. INTRODUCTION
Good day
• In this presentation, we will explore the issue of fake news and how machine
learning can be utilized to combat it.
• The spread of fake news has become a significant problem in recent years,
with the proliferation of social media platforms and the ease with which false
information can be disseminated. Fake news can cause harm, from
damaging reputations to influencing political outcomes and public opinion.
• We will also delve into the challenges involved in using machine learning to
combat fake news, such as the need for large amounts of labeled data, the
potential for bias in algorithms, and the ethical implications of using machine
learning in this context.
• Finally, we will highlight some of the current efforts and initiatives aimed at
using machine learning to combat fake news, and the potential impact of this
technology on society.

4. IMPORTANT TERMS
• Natural Language Processing (NLP): A subfield of machine learning
that focuses on the interaction between humans and computers using
natural language.
• Text Classification: The process of assigning a label or category to a
given text based on its content.
• Feature Extraction: The process of identifying and extracting relevant
features or attributes from a given text that can be used for machine
learning.
• Evaluation Metrics: Metrics used to evaluate the performance of a
machine learning model, such as accuracy, precision, recall, F1 score,
and AUC-ROC score.
• Logistic Regression: Logistic Regression is a statistical algorithm
used for binary classification problems where the target variable has
only two possible values. It models the probability of an event
occurring by fitting a logistic function to the input data. The logistic
function maps any input to a probability between 0 and 1, which is
then used to classify the input into one of the two categories. Logistic
Regression is widely used in various fields such as finance,
healthcare, marketing, and social sciences. It is simple to implement,
easy to interpret, and provides good accuracy for many real-world
problems.

5. LITERATURE SURVEY
• In this literature survey, we will review the current state of the art in fake news detection using
machine learning.
Introduction to Fake News Detection
• Fake news detection is the process of identifying and flagging news articles that contain false
information. It has become a major issue in recent years due to the proliferation of social
media and the internet. Machine learning techniques can be used to identify patterns in large
datasets of news articles, allowing for the automated detection of fake news.
Types of Fake News Detection Techniques
• There are two main approaches to fake news detection: rule-based and machine learning-
based
Techniques used in Machine Learning-Based Fake News Detection
• Natural Language Processing Techniques: Natural Language Processing (NLP) is a
technique used to process and analyze large amounts of text data.
Current State of the Art in Fake News Detection
• Detection of Fake News based on Social Network Analysis: Researchers have developed
methods to detect fake news by analyzing the propagation patterns of news articles on social
networks.
• Detection of Fake News based on Fact Checking: Fact checking involves verifying the
accuracy of news articles by cross-referencing them with reliable sources. Machine learning
algorithms can be used to automate the fact checking process.

6. RESEARCH OBJECTIVE
• The research objective of fake news detection is to develop and improve
automated systems that can accurately identify and flag news articles,
social media posts, or other forms of online content that contain false or
misleading information.The goal is to help individuals and organizations
make informed decisions by providing them with reliable and trustworthy
information.
• Fake news detection is a challenging task that requires advanced
technologies such as natural language processing, machine learning, and
data analytics. Researchers in this field work on developing algorithms that
can analyze large volumes of data, identify patterns and anomalies, and
make accurate predictions about the veracity of online content.

7. Proposed Methedology
The methodology for fake news detection using
machine learning can be structured as follows:
• Data Preprocessing
• Feature Extraction
• Model Development
• Model Evaluation
• Model Improvement
• Model Deployment:
• Interpretation and Explanation

8. STEPS
STEP 1: Imported the dataset from Kaggle dataset,
modified the dataset and saved in Excel.csv format.
STEP 2: Used Google colab for executing python coding
and removed all unwanted data from dataset.
STEP 3:Then dataset is separated into training dataset
and testing dataset.
STEP 4:Visualization are made in Google colab for better
understanding of dataset.
STEP 5: Finding accuracy using Logistic Regression.

9. RESULTS
• We evaluated the performance of a logistic regression model for fake news detection
using a dataset of 28,000 news articles, of which 14000 were real and 14000 were
fake. The dataset was randomly split into a training set of 25000 articles and a test
set of 3,000 articles.
• We used the following evaluation metrics to assess the performance of the model:
accuracy, precision, recall, and F1-score. The model achieved an accuracy of 97.0%,
a precision of 87.1%, a recall of 87.5%, and an F1-score of 87.3% on the test set.
These results demonstrate that the logistic regression model is able to accurately
distinguish between fake and real news articles.
•

10. To compare the performance of the logistic regression model to a baseline model,
we also trained a random classifier on the same dataset.
The random classifier achieved an accuracy of 50.2%, which is significantly lower
than the performance of the logistic regression model. These results demonstrate
that the logistic regression model is effective in detecting fake news, and
outperforms a simple baseline model.
We analyzed the most important features used by the logistic regression model to
distinguish between fake and real news articles. The most important features were
found to be word frequency, topic, and sentiment.
The model placed more weight on certain words and topics that were frequently
present in fake news articles, such as conspiracy theories and sensationalist
headlines.
The model also considered the overall sentiment of the article, with negative
sentiment being more indicative of fake news.

11. CONCLUSION
• In conclusion, the issue of fake news is a growing problem that can have
serious consequences on society. With the rise of social media and the ease
of sharing information, fake news can spread rapidly, leading to
misinformation and mistrust.
• To address this problem, a fake news detector has been developed that uses
natural language processing techniques to analyze news articles and
determine their level of authenticity. By considering various factors such as
the credibility of the source and the language used in the article, the fake
news detector can accurately identify fake news articles.
• While the fake news detector is not a perfect solution and may require
continuous improvement, it represents a significant step in combating the
spread of fake news. It can help individuals make more informed decisions
and promote a more trustworthy and reliable news environment.
• Overall, it is important for us to be aware of the prevalence of fake news and
take proactive steps to combat it. By using tools like the fake news detector
and being mindful of our sources of information, we can help promote a more
informed and trustworthy society.

12. SOURCE CODE

13. .

16. REFERENCES
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