Efficient Classifier for Detecting Spam in Social Networks through Sentiment Analysis

Abstract

Social networking services are used for communication between people to share information through internet. The unbounded growth of content and users pushes the internet technologies to certain limitations. Data mining plays a major role in the field of social network to extract relevant content from the voluminous data which is being a phenomenal task, because of its dynamic nature the participation is more complex. The major problem, users face spammer's interaction which leads to misunderstanding and inconvenience for social activities. This work concentrates on detecting the spammer actions using feature relevance analysis and applying efficient classifier. The main objective of the proposed work is to find relationship between features and classifying patterns for detecting spam message from the unwanted sites. The system applies efficient classification algorithms after feature relevance analysis for detecting spam in better way. The outcome of this project will serve for the users participating in social networks for effective purpose like business, marketing and establishing contacts.

Chinese Spam Detection Using a Hybrid BiGRU-CNN Network with Joint Textual and Phonetic Embedding

“…To prevent spam comments, several engineers are making efforts to combat spam delivery, and many companies and research groups offer a variety of spam detection systems. Filtering spam is one way to identify spam accounts based on their activity, profiles, and prior postings [4]. The follower and friend relationships between accounts are used in social networks to identify spam accounts and their communities [5,6].…”

Security techniques for intelligent spam sensing and anomaly detection in online social platforms

“…Machine learning techniques lean on electing knowledge from previously sent spam items and then use the acquired information to predict the behavior of newly received spam and classify them. The authors in [38] proposed an efficient classifier to predict and detect spammers' actions using feature relevance analysis on social network is developed Zheng et al [19] proposed an effective spammer detection system based on supervised machine learning solution. This system considered user's content and behavior features, and then applied them into the SVM for spammers classification.…”