Cyberbullying Detection in Twitter Using Machine Learning Techniques ab 39.9 € als Taschenbuch: . Aus dem Bereich: Bücher, English, International, Gebundene Ausgaben,
Topic Detection and Classification in Social Networks ab 117.49 € als pdf eBook: The Twitter Case. Aus dem Bereich: eBooks, Sachthemen & Ratgeber, Computer & Internet,
Community Detection and Analysis of Twitter Social Data ab 39.9 EURO
Cyberbullying Detection in Twitter Using Machine Learning Techniques ab 39.9 EURO
Topic Detection and Classification in Social Networks ab 117.49 EURO The Twitter Case
Online Social Networks (OSNs) have become fundamental parts of our online lives, and their popularity is increasing at a surprising rate every day. Growing popularity of Twitter has attracted the attention of attackers who attempt to manipulate the features provided by Twitter to gain some advantage, such as driving Twitter users to other websites that they post as short URLs (Uniform Resource Locators) in their tweets.Even short URLs are also used to avoid sharing overly long URLs and save limited text space in tweets. Significant numbers of URLs shared in the OSNs are shortened URLs. Despite of its potential benefits from genuine usage, attackers use shortened URLs to hide the malicious URLs, which direct users to malicious pages. Although, OSN service providers and URL shortening services utilize certain detection mechanisms to prevent malicious URLs from being shortened, research has found that they fail to do so effectively.In this project, we developed mechanism to develop a machine learning classifier which detect malicious short URLs. And also shows comparative analysis of detection with various methods.
Human is a social animal, this line itself explains the importance of society in one's life. Society brings stability, a medium to express thoughts. Society leads to social interaction which eventually brings thoughtful minds. Humans have the intrinsic nature of analyzing and opinionating things and persons. This keen nature of humanity has emerged as a new field of analysis that is social data analysis. Internet has merged the world today and as a result, human social circles have expanded. There are various peculiar social networking sites available on the internet, some of them are on Facebook, Twitter, LinkedIn and many more. Each maintains accounts of billions of active users and the huge amount of data is being produced as a result of interactions over such sites. Hence analyzing this data is a tedious task. But analysis of such online social communities and predicting their behavior is of great importance for businesses and academics. For our research purpose, we have used Twitter as a key medium for social data. This study aims to develop a research-based application using twitter and R-tool for social data analysis.
We are living in the age of social networks, where folks are connected through social media. Examples of social media include Twitter, Facebook, LinkedIn and Myspace. Sometimes social media is not used in the intended way, which puts user data on security risks. This research thesis discusses a detection system for a special kind of security attack in which a fake user represents himself as a valid user and leaks other users' personal information. This kind of attack is known as Identity Clone Attack (ICA). We have introduced a novel technique of Clone Profiles Detection (CPD) that is a three steps approach which is based on similarity measure, IP address checking and behavioural model detection. This technique alerts a user and his friends in case of an identity clone attack and decreases the vulnerabilities of identity clone attack. Facebook graph APIs have been used for accessing Facebook users' profile for experimentation purposes. Experimental tests produced promising results.
Last few years the online social network has immensely popular in the whole world. Millions of users have used these sites as real-time communication, dynamic data sources where they can create their profiles and communicate with other users regardless of geographical location and physical limitations. In this region, these online social media have become vital and unique communication system. Online Social Media data can provide us with new innovative insights into the construction of social networks and societies, which is previously thought to be impossible regarding scale and extent. So, I am working on this issue. For cyberbullying detection using twitter data. I do my best. I am using little bit data set. In future i used huge amount of data from different sources.