Paper Title

Author1, ABC2

1 The Superior University,City, Country

email@gmail.com

Abstract. The devastating effect of spreading fake news related to politics, health, and cusxxxxxx.

Keywords: Fake news detection, machine learning, classification, social media, Twitter

Introduction

With the invention xxxx There is much hidden information inside comments, text, images, and videos, and it wasn’t easy to find human behavior (Lauw et al., 2010).

Nowadays, human behavior is analyzed by a social network analysis (SNA) which is the qualitative and quantitative analysis of a social media network (Scott, 2017). Currently, two main approaches are used for SNA, namely ML and DL. In ML techniques, convenient features like user, content, URL, and network-based (Boididou et al., 2018) are generated from selected datasets to form a master feature vector (MFV). These MFVs train ML classifiers to build classification models (Ishtiaq et al., 2019). This classification model is evaluated using different evaluation metrics with a test dataset. However, the discriminative features can enhance the performance of ML techniques. DL techniques were introduced to overcome the fatigue feature generation and biasness on feature types (Ajao et al., 2018).

This type of news is intentionally written to misguide the reader because a typical reader has no tool to differentiate between true and false information (Shu et al., 2017). Most of the studies in Fake news detection used twitter-based datasets. Twitter is a microblog on which people communicate using a short text (140 characters) called a tweet. This short text can include a link to a website. Twitter users follow other users, and this follower can see all the posts of the followee s timeline. New tweets can be created or retweet the existing tweets. Every tweet has its text, number of likes, and number of retweets(responses). It’s easy to access its public data using series of authentications and APIs. It has more than 327 million monthly active users. It gained recognition over the years as a powerful and quicker news broadcasting platform. All the organizations, political parties, celebrities, and even government offices have their Twitter accounts to share news quickly.

Proposed Methodology

At the very first stage, three

Figure 1. Proposed Research Methodology

Datasets for Fake News Detection

Three datasets are used in this study. Detail is given below:

Dataset1: This

. It also has the same column as in dataset two with the same number of features.

Data Processing

Preprocessing involves the process of removing unnecessary noise and meaningless data from collected raw data. This raw data contains high-level noise, like complex vocabulary, slang words, emoticons, sparsity, grammatical and spelling mistakes. For better fake news

, there is a challenge to tackle abbreviations and acronyms that need to convert into meaningful words. Word tokenization with Python NLTK, Mila tokenizer, NLTK word tokenizer(Vijayarani & Janani, 2016) tools can be used for tokenization

Machine Learning Techniques for Fake News Detection

ML is a field of artificial intelligence where the system can learn from features and improve based on

crete data, highly saleable with the number of predictors and data points, highly effective for realtime prediction, and not sensitive for irrelevant features. However, NB cannot learn the relationship between the elements and cause data distribution and scarcity (Aggarwal et al., 2012). NB assumes that the text is generated by a parametric model and utilizes training data to compute Bayes-optimal estimates of the model parameters. With these approximations, it categorizes generated test data(McCallum & Nigam, 1998). NB classifier can deal with an Arbitrary Number of continuous or categorical independent features, and a high dimensional density estimation task is reduced to one-dimensional kernel density estimation (Buczak et al., 2015).

Evaluation Metrics

In this step, the classifier's performance is measured to evaluate whether it classifies data right or wrong. The accuracy of the classifier can be calculated by calculating

True Positive (TP) is used to measure the exact prediction

True Negative (TN) is a measure in which the classifier predicts it is true, but in actual it's not true.

False Positive (FP) is the inaccurate prediction of an object

False Negative (FN) is an instance that is not precisely predicted.

These four members from the confusion matrix for binary classification as shown in Table 1.

Table 1. Confusion Matrix

Actual Instances
Yes	No
Predicted Instances
Yes	TP	FN
No	FP	TN

The performance metrics used to evaluate the machine as mentioned above learning classifiers are presented in Table 2.

Table 2. Performance evaluation metrics

Metric	Description	Formula
Accuracy	Accuracy is how much the data is labelled correctly. It computes total appropriately known occurrences.
Precision	It is the ratio between correct optimistic guesses over total positive guesses. It minimizes mistakes in guessing positive labels.
Recall	It's the ratio between correct positive guesses overall positive labels. The other name is an actual positive rate (TPR).

Experimental Setup

This section presents the experimental setup of the construction of the fake news detection models using the extracted features and three machine learning algorithms. An extensive set of experiments was performed to measure the constructed models' performance and compare them with baseline fake news detection models. To evaluate the performance of the constructed affected news detection models, experiments were performed systematically in three different settings. All experiments were conducted in Jupyter Notebook (Python 3.8.5) environment on a PC with 64-bit Windows 10 operating system, 8GB RAM and Intel(R) Core(TM) i5-3210M CPU.

Experimental Results

This section presents the results of all the experiments conducted in this study. The results are offered based on three experimental settings where three machine learning algorithms, including Decision Tree (J48), Naïve Bayes (NB) and Passive-Aggressive (PA), were trained and evaluated on three different fake news datasets.

Results of Experimental Setting I

In this experiment, three classifications, as mentioned earlier, were applied on a publicly available dataset1. The dataset was divided into various ratios (30-70, 40-60, 50-50, 60-40, 70-30) for classifier training and testing and to validate the robustness of each classifier. The experimental results revealed that a passive-aggressive algorithm achieved the best performance on all data ratios. The best performance was obtained 93.70% when the dataset was divided by 70-30% ratio (70% data was used for training and 30% data was used for testing). Moreover, the decision tree and Naïve Bayes also achieved reasonably good classification accuracy by 81% and 83%, respectively, as shown in Table 3.

e.

Table 3. Classification Results on dataset 3

Accuracy	Recall	Precision
Data size	PA	J48	NB	PA	J48	NB	PA	J48	NB
30-70	99.6	99.8	98.5	99.7	99.9	98.6	99.6	99.8	98.5
40-60	99.7	99.8	98.3	99.7	99.8	98.7	99.7	99.8	98.8
50-50	99.8	99.8	98.7	99.7	99.8	98.6	99.7	99.8	98.7
60-40	99.8	100	98.8	99.8	99.8	98.6	99.9	99.8	98.9
70-30	99.9	99.9	98.8	99.8	99.8	98.7	99.8	100	98.8

Discussion

This section presents the theoretical analysis of the fake news classifiers employed in this research. This research reveals that the proposed features couple with machine learning classifiers can detect fake news with an accuracy between 79% and 99%. The results show that the proposed features with a training and testing ratio of 70-30% provided the maximum accuracy using the passive-aggressive machine learning algorithm on all three datasets. The possible reason for the excellent performance of the passive-aggressive algorithm is that it can handle large scale of data coming from various social media applications like Twitter and Facebook, etc. Moreover, as this unstructured data contains slang words, emoji’s, emoticons, punctuation marks, and spelling mistakes, preprocessing techniques were applied to remove this unwanted text and improve the classification accuracy. Furthermore, the features extracted performed better because of the stemming and tokenization practices.

Conclusion

In this study, we presented a method to classify fake news from social media plat-forms. As shown in the result section, our classification method gave 99% accurate accuracy, precision, and recall. The passive-aggressive classifier produced excellent results as compared to the decision tree and naïve Bayes classifiers. For future work, we will use real-time data for this purpose.

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