scholarly journals Spam Detection Framework using ML Algorithm

2020 ◽  
Vol 8 (6) ◽  
pp. 5326-5329
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Social Networking ◽  
Information Sharing ◽  
Detection System ◽  
Reliable Data ◽  
Machine Learning Algorithms ◽  
Spam Detection ◽  
Function Type ◽  
Use Of Social Media

The current use of social media has created incomparable amounts of social data, as it is a cheap and popular information sharing communication platform. Nowadays, a huge percentage of people depend on the accessible material on social networking in their choices (e.g. comments and suggestions about a subject or product). This feature on exchanging knowledge with a wide number of users has quickly prompted social spammers to exploit the network of confidence to distribute spam messages and support personal forums, advertising, phishing, scams and so on. Identifying these spammers and spam material is a hot subject of study, and while large amounts of experiments have recently been conducted to this end, so far the methodologies are only barely able to identify spam feedback, and none of them demonstrates the value of each derived function type. In this study, we have suggested a machine learning-based spam detection system that determines whether or not a specific message in the dataset is spam using a set of machine learning algorithms. Four main features have been used; including user-behavioral, user-linguistic, reviewbehavioral and review-linguistic, to improve the spam detection process and to gather reliable data

scholarly journals Public Trolling Detection on Twitter Using Machine Learning

2021 ◽  
Vol 9 (VII) ◽  
pp. 3097-3100
Author(s):  
Miss. Pooja Dilip Dhotre
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Social Network ◽  
Social Networking ◽  
Social Networking Sites ◽  
Machine Learning Algorithms ◽  
The People ◽  
The World ◽  
Added Benefit ◽  
Current Popularity

Social media websites are among the internet's most far-reaching digital sites. Billions of social network users exist Users' frequent interactions with social networking sites, like Twitter, have a widespread and sometimes unfortunate effect on day-to-day life. Social networking sites make it easy for large amounts of unwanted and unrelated information to spread around the world. Twitter is a popular micro blogging service where users connect with others with similar interests. Because of the current popularity of Twitter, it is vulnerable to public shaming. Recently, Twitter has emerged as a rich source of human-generated information, with the added benefit of connecting you with customers and enabling two-way communication. It is generally accepted that when someone posts a comment in an occurrence, it is likely to humiliate the victim. The fact that shaming users' follower counts increase faster than that of the people who don't use shame is interesting. Using machine learning algorithms, users will be able to identify disrespectful words, as well as the overall negativity of those words, which is displayed in a percentage.

scholarly journals Identification and Detection of Cyberbullying on Facebook Using Machine Learning Algorithms

2021 ◽  
Vol 23 (4) ◽  
pp. 1-21
Author(s):  
Nureni Ayofe AZEEZ ◽  
Sanjay Misra ◽  
Omotola Ifeoluwa LAWAL ◽  
Jonathan Oluranti
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Nearest Neighbor ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
K Nearest Neighbor ◽  
Chi Square ◽  
Social Media Platforms ◽  
Bayes Algorithm ◽  
Use Of Social Media

The use of social media platforms such as Facebook, Twitter, Instagram, WhatsApp, etc. have enabled a lot of people to communicate effectively and frequently with each other and this has enabled cyberbullying to occur more frequently while using these networks. Cyberbullying is known to be the cause of some serious health issues among social media users and creating a way to identify and detect this holds significant importance. This paper takes a look at unique features gotten from the Facebook dataset and develops a model that identifies and detect cyberbullying posts by applying machine learning algorithms (Naïve Bayes Algorithm and K-Nearest Neighbor). The project also uses a feature selection algorithm namely x2 test (Chi-Square test) to select important features which can improve the performance of the classifiers and decrease classification time. The result of this paper tends to detect cyberbullying in Facebook with a high degree of accuracy and also improve the performance of the machine learning classifiers.

scholarly journals Hoax News Classification using Machine Learning Algorithms

2019 ◽  
Vol 9 (2) ◽  
pp. 3938-3944
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Learning Algorithm ◽  
Detection System ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Stochastic Gradient Descent ◽  
Support Vector ◽  
The Impact ◽  
F Measure

Hoax news on social media has had a dramatic effect on our society in recent years. The impact of hoax news felt by many people, anxiety, financial loss, and loss of the right name. Therefore we need a detection system that can help reduce hoax news on social media. Hoax news classification is one of the stages in the construction of a hoax news detection system, and this unsupervised learning algorithm becomes a method for creating hoax news datasets, machine learning tools for data processing, and text processing for detecting data. The next will produce a classification of a hoax or not a Hoax based on the text inputted. Hoax news classification in this study uses five algorithms, namely Support Vector Machine, Naïve Bayes, Decision Tree, Logistic Regression, Stochastic Gradient Descent, and Neural Network (MLP). These five algorithms to produce the best algorithm that can use to detect hoax news, with the highest parameters, accuracy, F-measure, Precision, and recall. From the results of testing conducted on five classification algorithms produced shows that the NN-MPL algorithm has an average of 93% for the value of accuracy, F-Measure, and Precision, the highest compared to five other algorithms, but for the highest Recall value generated from the algorithm SVM which is 94%. the results of this experiment show that different effects for different classifiers, and that means that the more hoax data used as training data, the more accurate the system calculates accuracy in more detail.

scholarly journals Comparison of twitter spam detection using various machine learning algorithms

2017 ◽  
Vol 7 (1.3) ◽  
pp. 61
Author(s):  
M. Sangeetha ◽  
S. Nithyanantham ◽  
M. Jayanthi
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Information Sharing ◽  
Online Social Networks ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Spam Detection ◽  
Critical Problem ◽  
Short Text ◽  
True Blue

Online Social Networks(OSNs) have mutual themes such as information sharing, person-to-person interaction and creation of shared and collaborative content.  Lots of micro blogging websites available like Twitter, Instagram, Tumblr. A standout amongst the most prominent online networking stages is Twitter. It has 313 million months to month dynamic clients which post of 500 million tweets for each day. Twitter allows users to send short text based messages with up to 140-character letters called "tweets". Enlisted clients can read and post tweets however the individuals who are unregistered can just read them. Due to the reputation it attracts the consideration of spammers for their vindictive points, for example, phishing true blue clients or spreading malevolent programming and promotes through URLs shared inside tweets, forcefully take after/unfollow valid clients and commandeer drifting subjects to draw in their consideration, proliferating obscenity. Twitter Spam has become a critical problem nowadays. By looking at the execution of an extensive variety of standard machine learning calculations, fundamentally expecting to distinguish the acceptable location execution in light of a lot of information by utilizing account-based and tweet content-based highlights.

scholarly journals Prediction of Fake Tweets Using Machine Learning Algorithms

2021 ◽  
Author(s):  
M. Sreedevi ◽  
G. Vijay Kumar ◽  
K. Kiran Kumar ◽  
B. Aruna ◽  
Arvind Yadav
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Social Networking ◽  
Social Networking Sites ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Fake News ◽  
Internet Media ◽  
Learning Techniques ◽  
False News

Social networking sites will attract millions of users around the globe. Internet media is becoming popular for news consumption because of its ease, simple access and fast spreading of data takes to consume news from social media. Fake news on social media is making an appearance that is attracting a huge attention. This kind of situation could bring a great conflict in real time. The false news impacts extremely negative on society, particularly in social, commercial, political world, also on individuals. Hence detection of fake news on social media became one of the emerging research topic and technically challenging task due to availability of tools on social media. In this paper various machine learning techniques are used to predict fake news on twitter data. The results shown by using these techniques are more accurate with better performance.

scholarly journals An Effecient Fake News Detection System Using Machine Learning

2019 ◽  
Vol 8 (10) ◽  
pp. 3125-3129 ◽  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Language Processing ◽  
Negative Impact ◽  
Detection System ◽  
Vital Role ◽  
Machine Learning Algorithms ◽  
Easy Access ◽  
Fake News ◽  
K Nearest Neighbors

Social media plays a major role in several things in our life. Social media helps all of us to find some important news with low price. It also provides easy access in less time. But sometimes social media gives a chance for the fast-spreading of fake news. So there is a possibility that less quality news with false information is spread through the social media. This shows a negative impact on the number of people. Sometimes it may impact society also. So, detection of fake news has vast importance. Machine learning algorithms play a vital role in fake news detection; Especially NLP (Natural Language Processing) algorithms are very useful for detecting the fake news. In this paper, we employed machine learning classifiers SVM, K-Nearest Neighbors, Decision tree, Random forest. By using these classifiers we successfully build a model to detect fake news from the given dataset. Python language was used for experiments.

INCREASING ADOPTION OFSOCIAL MEDIA IN HEALTHCARE ORGANIZATIONS : OPPORTUNITIES AND CONCERNS

2016 ◽  
Vol 12 (1) ◽  
Author(s):  
Sharafat Hussain ◽  
Prof. Mohd. Abdul Azeem
Keyword(s):  
Health Care ◽  
Social Media ◽  
Social Networking ◽  
Health Care Organizations ◽  
Healthcare Providers ◽  
Healthcare Organizations ◽  
Online Questionnaire ◽  
Potential Benefits ◽  
Social Media Presence ◽  
Use Of Social Media

Adoption of social media amongst health care organizations is thriving. Healthcare providers have begun to connect with patients via social media. While some healthcare organizations have taken the initiative, numerous others are attempting to comprehend this new medium of opportunity. These organizations are finding that social networking can be an effective way to monitor brand, connecting with patients, community, and patient education and acquiring new talent. This study is conducted to identify the purpose of using social media, concerns, policy and its implementation and the overall experience of healthcare organizations with social media. To collect first hand data, online questionnaire was sent via LinkedIn to 400 US healthcare organizations and representatives out of which 117 responded and were taken further for analsysis. The results of this study confirm the thriving adoption, increased opportunities and cautious use of social media by healthcare organizations. The potential benefits present outweigh the risk and concerns associated with it. Study concluded that social media presence will continue to grow into the future and the field of healthcare is no exception.

scholarly journals An IoT-Focused Intrusion Detection System Approach Based on Preprocessing Characterization for Cybersecurity Datasets

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 656
Author(s):  
Xavier Larriva-Novo ◽  
Víctor A. Villagrá ◽  
Mario Vega-Barbas ◽  
Diego Rivera ◽  
Mario Sanz Rodrigo
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
High Performance ◽  
Learning Algorithm ◽  
Detection System ◽  
Machine Learning Algorithms ◽  
Statistical Characteristics ◽  
Detection Techniques ◽  
Traffic Characteristics ◽  
Benchmark Datasets

Security in IoT networks is currently mandatory, due to the high amount of data that has to be handled. These systems are vulnerable to several cybersecurity attacks, which are increasing in number and sophistication. Due to this reason, new intrusion detection techniques have to be developed, being as accurate as possible for these scenarios. Intrusion detection systems based on machine learning algorithms have already shown a high performance in terms of accuracy. This research proposes the study and evaluation of several preprocessing techniques based on traffic categorization for a machine learning neural network algorithm. This research uses for its evaluation two benchmark datasets, namely UGR16 and the UNSW-NB15, and one of the most used datasets, KDD99. The preprocessing techniques were evaluated in accordance with scalar and normalization functions. All of these preprocessing models were applied through different sets of characteristics based on a categorization composed by four groups of features: basic connection features, content characteristics, statistical characteristics and finally, a group which is composed by traffic-based features and connection direction-based traffic characteristics. The objective of this research is to evaluate this categorization by using various data preprocessing techniques to obtain the most accurate model. Our proposal shows that, by applying the categorization of network traffic and several preprocessing techniques, the accuracy can be enhanced by up to 45%. The preprocessing of a specific group of characteristics allows for greater accuracy, allowing the machine learning algorithm to correctly classify these parameters related to possible attacks.

Exploring fake news identification using word and sentence embeddings

2021 ◽  
pp. 1-8
Author(s):  
V.T Priyanga ◽  
J.P Sanjanasri ◽  
Vijay Krishna Menon ◽  
E.A Gopalakrishnan ◽  
K.P Soman
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Network Analysis ◽  
Supervised Machine Learning ◽  
Breeding Ground ◽  
Fake News ◽  
Data Set ◽  
Highly Correlated ◽  
Use Of Social Media ◽  
The Liar

The widespread use of social media like Facebook, Twitter, Whatsapp, etc. has changed the way News is created and published; accessing news has become easy and inexpensive. However, the scale of usage and inability to moderate the content has made social media, a breeding ground for the circulation of fake news. Fake news is deliberately created either to increase the readership or disrupt the order in the society for political and commercial benefits. It is of paramount importance to identify and filter out fake news especially in democratic societies. Most existing methods for detecting fake news involve traditional supervised machine learning which has been quite ineffective. In this paper, we are analyzing word embedding features that can tell apart fake news from true news. We use the LIAR and ISOT data set. We churn out highly correlated news data from the entire data set by using cosine similarity and other such metrices, in order to distinguish their domains based on central topics. We then employ auto-encoders to detect and differentiate between true and fake news while also exploring their separability through network analysis.

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