Spam Detection In Online Social Networks Using Feed Forward Neural Network
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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.
First International Conference On Sustainable Technologies For Computational Intelligence
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Author : Ashish Kumar Luhach
language : en
Publisher: Springer Nature
Release Date : 2019-11-01
First International Conference On Sustainable Technologies For Computational Intelligence written by Ashish Kumar Luhach and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-01 with Technology & Engineering categories.
This book gathers high-quality papers presented at the First International Conference on Sustainable Technologies for Computational Intelligence (ICTSCI 2019), which was organized by Sri Balaji College of Engineering and Technology, Jaipur, Rajasthan, India, on March 29–30, 2019. It covers emerging topics in computational intelligence and effective strategies for its implementation in engineering applications.
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;
A Learning Approach To Spam Detection Based On Social Networks
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Author : Ho-Yu Lam
language : en
Publisher:
Release Date : 2007
A Learning Approach To Spam Detection Based On Social Networks written by Ho-Yu Lam and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with Machine learning categories.
Advances In Parallel Computing Algorithms Tools And Paradigms
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Author : D.J. Hemanth
language : en
Publisher: IOS Press
Release Date : 2022-11-23
Advances In Parallel Computing Algorithms Tools And Paradigms written by D.J. Hemanth and has been published by IOS Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-23 with Computers categories.
Recent developments in parallel computing for various fields of application are providing improved solutions for handling data. These newer, innovative ideas offer the technical support necessary to enhance intellectual decisions, while also dealing more efficiently with the huge volumes of data currently involved. This book presents the proceedings of ICAPTA 2022, the International Conference on Advances in Parallel Computing Technologies and Applications, hosted as a virtual conference from Bangalore, India, on 27 and 28 January 2022. The aim of the conference was to provide a forum for the sharing of knowledge about various aspects of parallel computing in communications systems and networking, including cloud and virtualization solutions, management technologies and vertical application areas. The conference also provided a premier platform for scientists, researchers, practitioners and academicians to present and discuss their most recent innovations, trends and concerns, as well as the practical challenges encountered in this field. More than 300 submissions were received for the conference, from which the 91 full-length papers presented here were accepted after review by a panel of subject experts. Topics covered include parallel computing in communication, machine learning intelligence for parallel computing and parallel computing for software services in theoretical and practical aspects. Providing an overview of recent developments in the field, the book will be of interest to all those whose work involves the use of parallel computing technologies.
Artificial Intelligence Applications And Innovations
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Author : Lazaros Iliadis
language : en
Publisher: Springer
Release Date : 2018-05-22
Artificial Intelligence Applications And Innovations written by Lazaros Iliadis and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-05-22 with Computers categories.
This book constitutes the refereed proceedings of the 14th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2018, held in Rhodes, Greece, in May 2018. The 42 full papers and 12 short papers were carefully reviewed and selected from 88 submissions. They are organized in the following topical sections: social media, games, ontologies; deep learning; support vector machines; constraints; machine learning, regression, classification; neural networks; medical intelligence; recommender systems; optimization; learning, intelligence; heuristic approaches, cloud; fuzzy; and human and computer interaction, sound, video, processing.
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
Detection Of Suspicious Urls In Online Social Networks Using Supervised Machine Learning Algorithms
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Author : Mohammed Fadhil Zamil Al-Janabi
language : en
Publisher:
Release Date : 2018
Detection Of Suspicious Urls In Online Social Networks Using Supervised Machine Learning Algorithms written by Mohammed Fadhil Zamil Al-Janabi 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.
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.