Spam Detection On Online Social Media Networks

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Spam Detection On Online Social Media Networks

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Author : Vijay Sharma
language : en
Publisher:
Release Date : 2023-01-12

Spam Detection On Online Social Media Networks written by Vijay Sharma and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-01-12 with Health & Fitness categories.

With the rise of social media, people started sharing opinions or experiences, exchanging ideas, providing suggestions etc. by using different means of online communication. We, the human beings, like to take the opinions or suggestions of our loved ones for taking decisions in our day-to-day activities. Nowadays, online opinions are playing a central role in people decision-making process. When it comes to online scenario, these opinions can be expressed in different forms on different online environments - people use online social networks like Facebook to express their feelings in the form of a post; micro-blogging websites like Twitter provide people a way to interact and share their thoughts globally in the form of a tweet; users write reviews about products or services on review platforms to share their experiences; people taking advice or leaving comment on a blog network; discussion forums provide rich content to people to have discussions on different topics of interest in the form of question/answer posts or comments;

Spam Detection In Online Social Networks Using Feed Forward Neural Network

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Author : munish sabharwal
language : en
Publisher:
Release Date : 2018-10-03

Spam Detection In Online Social Networks Using Feed Forward Neural Network written by munish sabharwal and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-10-03 with categories.

Social Networks define a path for consumers to continue contact with their friends. Social Networks' is increasingly the popularity allows of them to accumulator huge amounts of PI (Personal Information) about their consumers. Unhappily, spam information wealth as-well-as its simple to access consumers information can attract attackers class concentration. That's why social networks have been attacked by spammers while there has been a various work to identify and repair them.

Efficient Spam Detection Across Online Social Networks

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Author : Hailu Xu
language : en
Publisher:
Release Date : 2016

Efficient Spam Detection Across Online Social Networks written by Hailu Xu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with Online social networks categories.

Online Social Networks (OSNs) have become more and more popular in the whole world recently. People share their personal activities, views, and opinions among different OSNs. Simultaneously, social spam appears more frequently and in various formats throughout popular OSNs. As big data theory receives much more attention, it is expected that OSNs will have more interactions with each other shortly. This would enable a spam link, content or profile attack to easily move from one social network like Twitter to other social networks like Facebook. Therefore, efficient detection of spam has become a significant and popular problem. This paper focuses on spam detection across multiple OSNs by leveraging the knowledge of detecting similar spam within an OSN and using it in different OSNs. We chose Facebook and Twitter for our study targets, considering that they share the most similar features in posts, topics, and user activities, etc. We collected two datasets from them and performed analysis based on our proposed methodology. The results show that detection combined with spam in Facebook show a more than 50% decrease of spam tweets in Twitter, and detection combined with spam of Twitter shows a nearly 71.2% decrease of spam posts in Facebook. This means similar spam of one social network can significantly facilitate spam detection in other social networks. We proposed a new perspective of spam detection in OSNs.

Information Quality In Online Social Media And Big Data Collection

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Author : Mahdi Washha (doctorant en informatique).)
language : en
Publisher:
Release Date : 2018

Information Quality In Online Social Media And Big Data Collection written by Mahdi Washha (doctorant en informatique).) and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.

The popularity of OSM is mainly conditioned by the integrity and the quality of UGC as well as the protection of users' privacy. Based on the definition of information quality as fitness for use, the high usability and accessibility of OSM have exposed many information quality (IQ) problems which consequently decrease the performance of OSM dependent applications. Such problems are caused by ill-intentioned individuals who misuse OSM services to spread different kinds of noisy information, including fake information, illegal commercial content, drug sales, mal- ware downloads, and phishing links. The propagation and spreading of noisy information cause enormous drawbacks related to resources consumptions, decreasing quality of service of OSM-based applications, and spending human efforts. The majority of popular social networks (e.g., Facebook, Twitter, etc) over the Web 2.0 is daily attacked by an enormous number of ill-intentioned users. However, those popular social networks are ineffective in handling the noisy information, requiring several weeks or months to detect them. Moreover, different challenges stand in front of building a complete OSM-based noisy information filtering methods that can overcome the shortcomings of OSM information filters. These challenges are summarized in: (i) big data; (ii) privacy and security; (iii) structure heterogeneity; (iv) UGC format diversity; (v) subjectivity and objectivity; (vi) and service limitations In this thesis, we focus on increasing the quality of social UGC that are published and publicly accessible in forms of posts and profiles over OSNs through addressing in-depth the stated serious challenges. As the social spam is the most common IQ problem appearing over the OSM, we introduce a design of two generic approaches for detecting and filtering out the spam content. The first approach is for detecting the spam posts (e.g., spam tweets) in a real-time stream, while the other approach is dedicated for handling a big data collection of social profiles (e.g., Twitter accounts).

Mining Content And Relations For Social Spammer Detection

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Author : Xia Hu (Ph.D.)
language : en
Publisher:
Release Date : 2015

Mining Content And Relations For Social Spammer Detection written by Xia Hu (Ph.D.) and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with Computer networks categories.

Social networking services have emerged as an important platform for large-scale information sharing and communication. With the growing popularity of social media, spamming has become rampant in the platforms. Complex network interactions and evolving content present great challenges for social spammer detection. Different from some existing well-studied platforms, distinct characteristics of newly emerged social media data present new challenges for social spammer detection. First, texts in social media are short and potentially linked with each other via user connections. Second, it is observed that abundant contextual information may play an important role in distinguishing social spammers and normal users. Third, not only the content information but also the social connections in social media evolve very fast. Fourth, it is easy to amass vast quantities of unlabeled data in social media, but would be costly to obtain labels, which are essential for many supervised algorithms. To tackle those challenges raise in social media data, I focused on developing effective and efficient machine learning algorithms for social spammer detection.I provide a novel and systematic study of social spammer detection in the dissertation. By analyzing the properties of social network and content information, I propose a unified framework for social spammer detection by collectively using the two types of information in social media. Motivated by psychological findings in physical world, I investigate whether sentiment analysis can help spammer detection in online social media. In particular, I conduct an exploratory study to analyze the sentiment differences between spammers and normal users; and present a novel method to incorporate sentiment information into social spammer detection framework. Given the rapidly evolving nature, I propose a novel framework to efficiently reflect the effect of newly emerging social spammers. To tackle the problem of lack of labeling data in social media, I study how to incorporate network information into text content modeling, and design strategies to select the most representative and informative instances from social media for labeling. Motivated by publicly available label information from other media platforms, I propose to make use of knowledge learned from cross-media to help spammer detection on social media.

Mining Content And Relations For Social Spammer Detection

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Author : Xia Hu
language : en
Publisher:
Release Date : 2015

Mining Content And Relations For Social Spammer Detection written by Xia Hu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with Machine learning categories.

Social networking services have emerged as an important platform for large-scale information sharing and communication. With the growing popularity of social media, spamming has become rampant in the platforms. Complex network interactions and evolving content present great challenges for social spammer detection. Different from some existing well-studied platforms, distinct characteristics of newly emerged social media data present new challenges for social spammer detection. First, texts in social media are short and potentially linked with each other via user connections. Second, it is observed that abundant contextual information may play an important role in distinguishing social spammers and normal users. Third, not only the content information but also the social connections in social media evolve very fast. Fourth, it is easy to amass vast quantities of unlabeled data in social media, but would be costly to obtain labels, which are essential for many supervised algorithms. To tackle those challenges raise in social media data, I focused on developing effective and efficient machine learning algorithms for social spammer detection. I provide a novel and systematic study of social spammer detection in the dissertation. By analyzing the properties of social network and content information, I propose a unified framework for social spammer detection by collectively using the two types of information in social media. Motivated by psychological findings in physical world, I investigate whether sentiment analysis can help spammer detection in online social media. In particular, I conduct an exploratory study to analyze the sentiment differences between spammers and normal users; and present a novel method to incorporate sentiment information into social spammer detection framework. Given the rapidly evolving nature, I propose a novel framework to efficiently reflect the effect of newly emerging social spammers. To tackle the problem of lack of labeling data in social media, I study how to incorporate network information into text content modeling, and design strategies to select the most representative and informative instances from social media for labeling. Motivated by publicly available label information from other media platforms, I propose to make use of knowledge learned from cross-media to help spammer detection on social media.

Detection Of Opinion Spam In Online Social Media

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Author : Rastogi Rastogi
language : en
Publisher: Independent Author
Release Date : 2022-12-08

Detection Of Opinion Spam In Online Social Media written by Rastogi Rastogi and has been published by Independent Author this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-12-08 with Computers categories.

With the rise of social media, people started sharing opinions or experiences, exchanging ideas, providing suggestions etc. by using different means of online communication. We, the human beings, like to take the opinions or suggestions of our loved ones for taking decisions in our day-to-day activities. Nowadays, online opinions are playing a central role in people decision-making process. When it comes to online scenario, these opinions can be expressed in different forms on different online environments - people use online social networks like Facebook to express their feelings in the form of a post; micro-blogging websites like Twitter provide people a way to interact and share their thoughts globally in the form of a tweet; users write reviews about products or services on review platforms to share their experiences; people taking advice or leaving comment on a blog network; discussion forums provide rich content to people to have discussions on different topics of interest in the form of question/answer posts or comments.

Detecting Fake News On Social Media

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Author : Kai Shu
language : en
Publisher: Springer Nature
Release Date : 2022-05-31

Detecting Fake News On Social Media written by Kai Shu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-31 with Computers categories.

In the past decade, social media has become increasingly popular for news consumption due to its easy access, fast dissemination, and low cost. However, social media also enables the wide propagation of "fake news," i.e., news with intentionally false information. Fake news on social media can have significant negative societal effects. Therefore, fake news detection on social media has recently become an emerging research area that is attracting tremendous attention. This book, from a data mining perspective, introduces the basic concepts and characteristics of fake news across disciplines, reviews representative fake news detection methods in a principled way, and illustrates challenging issues of fake news detection on social media. In particular, we discussed the value of news content and social context, and important extensions to handle early detection, weakly-supervised detection, and explainable detection. The concepts, algorithms, and methods described in this lecture can help harness the power of social media to build effective and intelligent fake news detection systems. This book is an accessible introduction to the study of detecting fake news on social media. It is an essential reading for students, researchers, and practitioners to understand, manage, and excel in this area. This book is supported by additional materials, including lecture slides, the complete set of figures, key references, datasets, tools used in this book, and the source code of representative algorithms. The readers are encouraged to visit the book website for the latest information: http://dmml.asu.edu/dfn/

Spammer Detection On Online Social Networks

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Author : Amit Anand Amlesahwaram
language : en
Publisher:
Release Date : 2013

Spammer Detection On Online Social Networks written by Amit Anand Amlesahwaram and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with categories.

Twitter with its rising popularity as a micro-blogging website has inevitably attracted attention of spammers. Spammers use myriad of techniques to lure victims into clicking malicious URLs. In this thesis, we present several novel features capable of distinguishing spam accounts from legitimate accounts in real-time. The features exploit the behavioral and content entropy, bait-techniques, community-orientation, and profile characteristics of spammers. We then use supervised learning algorithms to generate models using the proposed features and show that our tool, spAmbush, can detect spammers in real-time. Our analysis reveals detection of more than 90% of spammers with less than five tweets and more than half with only a single tweet. Our feature computation has low latency and resource requirement. Our results show a 96% detection rate with only 0.01% false positive rate. We further cluster the unknown spammers to identify and understand the prevalent spam campaigns on Twitter. The electronic version of this dissertation is accessible from http://hdl.handle.net/1969.1/148426

Online Social Networks Security

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Author : Brij B. Gupta
language : en
Publisher: CRC Press
Release Date : 2021-02-26

Online Social Networks Security written by Brij B. Gupta and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-26 with Computers categories.

In recent years, virtual meeting technology has become a part of the everyday lives of more and more people, often with the help of global online social networks (OSNs). These help users to build both social and professional links on a worldwide scale. The sharing of information and opinions are important features of OSNs. Users can describe recent activities and interests, share photos, videos, applications, and much more. The use of OSNs has increased at a rapid rate. Google+, Facebook, Twitter, LinkedIn, Sina Weibo, VKontakte, and Mixi are all OSNs that have become the preferred way of communication for a vast number of daily active users. Users spend substantial amounts of time updating their information, communicating with other users, and browsing one another’s accounts. OSNs obliterate geographical distance and can breach economic barrier. This popularity has made OSNs a fascinating test bed for cyberattacks comprising Cross-Site Scripting, SQL injection, DDoS, phishing, spamming, fake profile, spammer, etc. OSNs security: Principles, Algorithm, Applications, and Perspectives describe various attacks, classifying them, explaining their consequences, and offering. It also highlights some key contributions related to the current defensive approaches. Moreover, it shows how machine-learning and deep-learning methods can mitigate attacks on OSNs. Different technological solutions that have been proposed are also discussed. The topics, methodologies, and outcomes included in this book will help readers learn the importance of incentives in any technical solution to handle attacks against OSNs. The best practices and guidelines will show how to implement various attack-mitigation methodologies.