scholarly journals Short Messages Spam Filtering Combining Personality Recognition and Sentiment Analysis

2017 ◽  
Vol 25 (Suppl. 2) ◽  
pp. 175-189 ◽  
Author(s):  
Enaitz Ezpeleta ◽  
Iñaki Garitano ◽  
Urko Zurutuza ◽  
José María Gómez Hidalgo
Keyword(s):  
Sentiment Analysis ◽  
Online Social Networks ◽  
Security And Privacy ◽  
Short Message ◽  
Growing Up ◽  
Detection Systems ◽  
Automatic Text Classification ◽  
Novel Method ◽  
Personality Recognition ◽  
Short Messages

Currently, short communication channels are growing up due to the huge increase in the number of smartphones and online social networks users. This growth attracts malicious campaigns, such as spam campaigns, that are a direct threat to the security and privacy of the users. While most researches are focused on automatic text classification, in this work we demonstrate the possibility of improving current short messages spam detection systems using a novel method. We combine personality recognition and sentiment analysis techniques to analyze Short Message Services (SMS) texts. We enrich a publicly available dataset adding these features, first separately and after in combination, of each message to the dataset, creating new datasets. We apply several combinations of the best SMS spam classifiers and filters to each dataset in order to compare the results of each one. Taking into account the experimental results we analyze the real inuence of each feature and the combination of both. At the end, the best results are improved in terms of accuracy, reaching to a 99.01% and the number of false positive is reduced.

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

2020 ◽  
Vol 10 (1) ◽  
pp. 275
Author(s):  
Monther Aldwairi ◽  
Loai Tawalbeh
Keyword(s):  
Online Social Networks ◽  
High Speed ◽  
Mobile Apps ◽  
Error Resilience ◽  
Mobile Technologies ◽  
Security And Privacy ◽  
Private Institutions ◽  
Online Groups ◽  
Detection Systems ◽  
Social Graphs

The recent advances in communication and mobile technologies made it easier to access and share information for most people worldwide. Among the most powerful information spreading platforms are the Online Social Networks (OSN)s that allow Internet-connected users to share different information such as instant messages, tweets, photos, and videos. Adding to that many governmental and private institutions use the OSNs such as Twitter for official announcements. Consequently, there is a tremendous need to provide the required level of security for OSN users. However, there are many challenges due to the different protocols and variety of mobile apps used to access OSNs. Therefore, traditional security techniques fail to provide the needed security and privacy, and more intelligence is required. Computational intelligence adds high-speed computation, fault tolerance, adaptability, and error resilience when used to ensure security in OSN apps. This research provides a comprehensive related work survey and investigates the application of artificial neural networks for intrusion detection systems and spam filtering for OSNs. In addition, we use the concept of social graphs and weighted cliques in the detection of suspicious behavior of certain online groups and to prevent further planned actions such as cyber/terrorist attacks before they happen.

Sentiment Analysis by using deep learning and Machine learning Techniques: A Review

2021 ◽  
Vol 10 (2) ◽  
pp. 754-761
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Online Social Networks ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Growing Up ◽  
Specific Subject ◽  
Learning Techniques ◽  
Day By Day

More and more individuals are now using online social networks and resources throughout this day and age to not only interact and to communicate but also for sharing their views, experiences, ideas, impression about anything. The analysis of sentiments is the identification and categorization of these views to evaluate public opinions on a specific subject, question, product, etc. Day by day, the relevance of sentiment analysis is growing up. Machine learning is an area or field of computer science where, without being specifically programmed, computers can learn. Deep learning is the part of machine learning and deals with the algorithm, which is most widely used as Neural network, neural belief, etc., in which neuronal implementations are considered. For sentiment analysis, it compares their performance and accuracy so then it can be inferred that deep learning techniques in most of the cases provide better results. The gap in the precision of these two approaches, however, is not as important enough in certain situations, and so it is best to apply and use the machine learning approaches and methods because these are simpler in terms of Implementation

A Tracking System with Location-Based Delivering for Reducing Short Message Transmissions

2013 ◽  
Vol 284-287 ◽  
pp. 2682-2686
Author(s):  
Yi Hsuah Yeh ◽  
Yuan Cheng Lai ◽  
Jian Wei Lin ◽  
Ching Neng Lai ◽  
Hui Chuan Weng
Keyword(s):  
Tracking System ◽  
Dynamic Threshold ◽  
Short Message ◽  
Location Prediction ◽  
Location Accuracy ◽  
Message Service ◽  
Novel Method ◽  
Actual Location ◽  
Moving Speed ◽  
Short Messages

Combining Short Message Service (SMS) and Global Position System (GPS), this paper proposes a novel method, called Location-based Delivering (LBD), and further develops a realistic system to tracking target's moving. LBD can reduce the number of short message transmissions while maintaining location accuracy within an acceptable range. LBD mainly adopts two proposed techniques: location prediction and dynamic threshold. Location prediction utilizes the current target's location, moving speed, bearing to predict its next location. When the distance between the predicted location and the actual location exceeds a threshold, the target sends a short message of the actual location to the tracker for updating. According to the target’s moving speed, dynamic threshold dynamically adjusts the threshold in order to balance the location accuracy and the amount of short messages. Experiment results show that LBD indeed outperforms other methods because it sends the least number of short messages and also maintains the satisfactory location accuracy.

Fuzzy based feature engineering architecture for sentiment analysis of medical discussion over online social networks

2021 ◽  
pp. 1-13
Author(s):  
C S Pavan Kumar ◽  
L D Dhinesh Babu
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Media ◽  
Sentiment Analysis ◽  
Membership Function ◽  
Online Social Networks ◽  
Learning Model ◽  
Feature Engineering ◽  
Machine Learning Model ◽  
Social Media Platforms

Sentiment analysis is widely used to retrieve the hidden sentiments in medical discussions over Online Social Networking platforms such as Twitter, Facebook, Instagram. People often tend to convey their feelings concerning their medical problems over social media platforms. Practitioners and health care workers have started to observe these discussions to assess the impact of health-related issues among the people. This helps in providing better care to improve the quality of life. Dementia is a serious disease in western countries like the United States of America and the United Kingdom, and the respective governments are providing facilities to the affected people. There is much chatter over social media platforms concerning the patients’ care, healthy measures to be followed to avoid disease, check early indications. These chatters have to be carefully monitored to help the officials take necessary precautions for the betterment of the affected. A novel Feature engineering architecture that involves feature-split for sentiment analysis of medical chatter over online social networks with the pipeline is proposed that can be used on any Machine Learning model. The proposed model used the fuzzy membership function in refining the outputs. The machine learning model has obtained sentiment score is subjected to fuzzification and defuzzification by using the trapezoid membership function and center of sums method, respectively. Three datasets are considered for comparison of the proposed and the regular model. The proposed approach delivered better results than the normal approach and is proved to be an effective approach for sentiment analysis of medical discussions over online social networks.

scholarly journals Privacy and Security Issues in Online Social Networks

2018 ◽  
Vol 10 (12) ◽  
pp. 114 ◽  
Author(s):  
Shaukat Ali ◽  
Naveed Islam ◽  
Azhar Rauf ◽  
Ikram Din ◽  
Mohsen Guizani ◽  
...  
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Virtual Communities ◽  
Service Providers ◽  
Security And Privacy ◽  
Security Issue ◽  
Sensitive Data ◽  
Privacy And Security ◽  
Security Issues ◽  
Social Sphere

The advent of online social networks (OSN) has transformed a common passive reader into a content contributor. It has allowed users to share information and exchange opinions, and also express themselves in online virtual communities to interact with other users of similar interests. However, OSN have turned the social sphere of users into the commercial sphere. This should create a privacy and security issue for OSN users. OSN service providers collect the private and sensitive data of their customers that can be misused by data collectors, third parties, or by unauthorized users. In this paper, common security and privacy issues are explained along with recommendations to OSN users to protect themselves from these issues whenever they use social media.

scholarly journals A Novel Method for Twitter Sentiment Analysis Based on Attentional-Graph Neural Network

Information ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 92 ◽  
Author(s):  
Mingda Wang ◽  
Guangmin Hu
Keyword(s):  
Neural Network ◽  
Sentiment Analysis ◽  
Presidential Election ◽  
Loss Functions ◽  
Neural Structure ◽  
Text Information ◽  
Novel Method ◽  
Structural Controllability ◽  
Graph Neural Networks ◽  
Multiple Metrics

Twitter sentiment analysis is an effective tool for various Twitter-based analysis tasks. However, there is still no neural-network-based research which takes both the tweet-text information and user-connection information into account. To this end, we propose the Attentional-graph Neural Network based Twitter Sentiment Analyzer (AGN-TSA), a Twitter sentiment analyzer based on attentional-graph neural networks. AGN-TSA fuses the tweet-text information and the user-connection information through a three-layered neural structure, which includes a word-embedding layer, a user-embedding layer and an attentional graph network layer. For the training of AGN-TSA, dedicated loss functions are designed for the structural controllability of AGN-TSA network. Experiments based on real-world dataset concerning the 2016 presidential election of America exhibit that AGN-TSA is superior under multiple metrics over several prevailing methods, with a performance boost of over 5%. The empirical settings of parameters are given based on extensive rotation experiments.

Novel Comment Spam Filtering Method on Youtube: Sentiment Analysis and Personality Recognition

2018 ◽  
pp. 228-240
Author(s):  
Enaitz Ezpeleta ◽  
Iñaki Garitano ◽  
Ignacio Arenaza-Nuño ◽  
José María Gómez Hidalgo ◽  
Urko Zurutuza
Keyword(s):  
Sentiment Analysis ◽  
Spam Filtering ◽  
Filtering Method ◽  
Personality Recognition

Access Control Models for Online Social Networks

2014 ◽  
pp. 451-484
Author(s):  
Rula Sayaf ◽  
Dave Clarke
Keyword(s):  
Social Networks ◽  
Access Control ◽  
Online Social Networks ◽  
Personal Data ◽  
Security And Privacy ◽  
Open Problems ◽  
Access Control Models ◽  
Control Models ◽  
Underlying Mechanisms ◽  
High Level

Access control is one of the crucial aspects in information systems security. Authorizing access to resources is a fundamental process to limit potential privacy violations and protect users. The nature of personal data in online social networks (OSNs) requires a high-level of security and privacy protection. Recently, OSN-specific access control models (ACMs) have been proposed to address the particular structure, functionality and the underlying privacy issues of OSNs. In this survey chapter, the essential aspects of access control and review the fundamental classical ACMs are introduced. The specific OSNs features and review the main categories of OSN-specific ACMs are highlighted. Within each category, the most prominent ACMs and their underlying mechanisms that contribute enhancing privacy of OSNs are surveyed. Toward the end, more advanced issues of access control in OSNs are discussed. Throughout the discussion, different models and highlight open problems are contrasted. Based on these problems, the chapter is concluded by proposing requirements for future ACMs.

Detecting Intrusions in Cyber-Physical Systems of Smart Cities

2019 ◽  
pp. 74-102 ◽  
Author(s):  
Ismail Butun ◽  
Patrik Österberg
Keyword(s):  
Anomaly Detection ◽  
Smart Cities ◽  
Cyber Physical Systems ◽  
Security And Privacy ◽  
Security Vulnerabilities ◽  
Physical Systems ◽  
Detection Systems ◽  
Comprehensive Survey ◽  
Privacy Violation ◽  
Detection Schemes

Interfacing the smart cities with cyber-physical systems (CPSs) improves cyber infrastructures while introducing security vulnerabilities that may lead to severe problems such as system failure, privacy violation, and/or issues related to data integrity if security and privacy are not addressed properly. In order for the CPSs of smart cities to be designed with proactive intelligence against such vulnerabilities, anomaly detection approaches need to be employed. This chapter will provide a brief overview of the security vulnerabilities in CPSs of smart cities. Following a thorough discussion on the applicability of conventional anomaly detection schemes in CPSs of smart cities, possible adoption of distributed anomaly detection systems by CPSs of smart cities will be discussed along with a comprehensive survey of the state of the art. The chapter will discuss challenges in tailoring appropriate anomaly detection schemes for CPSs of smart cities and provide insights into future directions for the researchers working in this field.

Learning to Suggest Hashtags

2019 ◽  
pp. 302-325
Author(s):  
Fahd Kalloubi ◽  
El Habib Nfaoui
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Online Social Networks ◽  
Recommendation System ◽  
Semantic Features ◽  
Short Messages

Twitter is one of the primary online social networks where users share messages and contents of interest to those who follow their activities. To effectively categorize and give audience to their tweets, users try to append appropriate hashtags to their short messages. However, the hashtags usage is very small and very heterogeneous and users may spend a lot of time searching the appropriate hashtags. Thus, the need for a system to assist users in this task is very important to increase and homogenize the hashtagging usage. In this chapter, the authors present a hashtag recommendation system on microblogging platforms by leveraging semantic features. Furthermore, they conduct a detailed study on how the semantic-based model influences the final recommended hashtags using different ranking strategies. Moreover, they propose a linear and a machine learning based combination of these ranking strategies. The experiment results show that their approach improves content-based recommendations, achieving a recall of more than 47% on recommending 5 hashtags.

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