The raging and ever growing popularity of social media, with its reach and depth, also gained the attention of cyber-criminals for the dissemination and distribution of malicious contents and link. In order to achieve this, they create fake and doctored profiles to send malicious messages to social media users on various platforms, leading to misinformation campaigns, fraud, spam or malware promotions. Thus it is very important that such malicious profiles are detected and remedied at the earliest possible, so that the resulting harm could be minimized. The objective of this research work is to develop a modified model for detection of malicious profiles on Twitter. In this modified model, we have identified simple and derived salient features by examining the public information available on the twitter in order to classify malicious and legitimate profiles with accuracy over 96.92%. Experiment illustrates that our modified model to detect malicious profile in social media has significant improvement over the previous work.
Social media is the most important and powerful platform for sharing information, ideas, and news almost immediately. With this, it also attracted antisocial elements for spreading and distributing rumors that is unverified information. Malicious and intended misinformation spread on social media has a severe effect on societies, people and individuals, especially in case of real-life emergencies such as terror strikes, riots, earthquakes, floods, war, etc. Thus, to minimize the harmful impact of rumor on society, it will be better to detect it as early as possible. The objective of this research and analysis is to develop a modified rumor detection model targeted for the proliferation of any malicious rumors related to any significant events. It is achieved through a binomial supervised classifier. The classifier uses a combination of explicit and implicit features to detect rumors. Our enhanced model significantly achieved it with 85.68% accuracy.
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