Online Social Networks Profiles Research Articles

Privacy Scoring over OSNs: Shared Data Granularity as a Latent Dimension

Privacy scoring aims at measuring the privacy violation risk of a user over an online social network (OSN) based on attribute values shared in the user’s OSN profile page and the user’s position in the network. Existing studies on privacy scoring rely on possibly biased or emotional survey data. In this study, we work with real-world data collected from the professional LinkedIn OSN and show that probabilistic scoring models derived from the item response theory fit real-world data better than naive approaches. We also introduce the granularity of the data an OSN user shares on her profile as a latent dimension of the OSN privacy scoring problem. Incorporating data granularity into our model, we build the most comprehensive solution to the OSN privacy scoring problem. Extensive experimental evaluation of various scoring models indicates the effectiveness of the proposed solution.

Automatic Identification of Fake Profiles in Online Social Networks

In the modern era, the social life of individuals has shifted towards becoming connected with online informal communities. These communities have revolutionized the way we pursue our social life, making it easier to connect with them and keep up with their updates. However, with the rapid growth of these communities, there has been a rise in issues such as false profiles and online bullying. There is currently no practical solution to address these issues. To address this issue, we have developed a system with which the programmed identification of false profiles is feasible and efficient. This system uses order methods such as support vector machine, nave bayes and decision trees to classify the profiles into false or true classes. As this is a software-based recognition method, it can be easily connected to online informal organizations that have a large number of profiles whose profiles cannot be physically inspected. In today’s world, users are bombarded with unnecessary information during surfing, which are posted by false users. Research has shown that 20% to 40% of profiles in social networks like Facebook are fake profiles. This detection of fake profiles leads to solution using frameworks. Keywords: Online Social Network, Fake Profiles, Classification, Natural Language Processing.

An Enhanced Scammer Detection Model for Online Social Network Frauds Using Machine Learning

The prevalence of online social networking increase in the risk of social network scams or fraud. Scammers often create fake profiles to trick unsuspecting users into fraudulent activities. Therefore, it is important to be able to identify these scammer profiles and prevent fraud such as dating scams, compromised accounts, and fake profiles. This study proposes an enhanced scammer detection model that utilizes user profile attributes and images to identify scammer profiles in online social networks. The approach involves preprocessing user profile data, extracting features, and machine learning algorithms for classification. The system was tested on a dataset created specifically for this study and was found to have an accuracy rate of 94.50% with low false-positive rates. The proposed approach aims to detect scammer profiles early on to prevent online social network fraud and ensure a safer environment for society and women’s safety.

Identification of Forged Profiles in Online Social Networks Using Machine Learning and NLP

Abstract: Many people today use social networking sites as a part of their everyday lives. They create their own profiles on the social network platforms every day, and they interact with others regardless of their location and time. In addition to providing users with advantages, social networking sites also present security concerns to them and their information to them. We need to classify the social network profiles of the users to figure out who is encouraging threats on social networks. From the classification, we can figure out which profiles are genuine and which are fake. As far as detecting fake profiles on social networks is concerned, we currently have different classification methods. However, we must improve the accuracy of detecting fake profiles in social networks. We propose the use of a machine learning algorithm and Natural Language Processing (NLP) technique in this paper so as to increase the detection rate of fake profiles. This can be achieved using Support Vector Machines (SVM) and Naïve Bayes algorithms.

An across online social networks profile building approach: Application to suicidal ideation detection

With the spreading use of online social networks (OSNs) such as Facebook, Twitter and Youtube, most people find that writing about their feelings and sharing their preferences and thoughts in social media is actually easier then articulating them in real life. Specifically, these networks are increasingly associated with different social phenomena such as diseases, depression and even suicide. In this context, most people who have suicidal ideations and are active in OSNs give signals of their intentions. It is therefore essential to increase efforts working on the detection of profiles whose owners have suicidal ideations as an attempt to prevent suicide. For that purpose, in this paper we propose a new method that automatically detects suicidal users through their created profiles in OSNs. Our contribution consists in considering profiles from multiple data-sources and detecting suicidal users based on their available shared content across OSNs. We extract several types of features from the posting content of users to build a complete profile that contribute to high suicidal user prediction. We employ supervised machine learning techniques to distinguish between suicidal and non-suicidal profiles. Our experiments on a dataset, which consists of persons who had died by suicide, demonstrate the feasibility of identifying user profiles from multiple data-sources in revealing suicidal profiles.

A Novel Approach to the Measurement of Bourdieusian Social Capital within Institutional Pages and Profiles

We present in this paper a novel approach for measuring Bourdieusian Social Capital (BSC) within Institutional Pages and Profiles. We analyse Facebook's Institutional Pages and Twitter's Institutional Profiles. Supported by Pierre Bourdie's theory, we search for directions to identify and capture data related to sociability practices, i. e. actions performed such as Like, Comment and Share. The system of symbolic exchanges and mutual recognition treated by Pierre Bourdieu is represented and extracted automatically from these data in the form of generalized sequential patterns. In this format, the social interactions captured from each page are represented as sequences of actions. Next, we also use such data to measure the frequency of occurrence of each sequence. From such frequencies, we compute the effective mobilization capacity. Finally, the volume of BSC is computed based on the capacity of effective mobilization, the number of social interactions captured and the number of followers on each page. The results are aligned with Bourdieu's theory. The approach can be generalized to institutional pages or profiles in Online Social Networks.

A novel spam detection technique for detecting and classifying malicious profiles in online social networks

Online social networks (OSNs) are utilized by millions of people from the entire world to communicate with others through Facebook and Twitter. The removal of fake accounts will increase the efficiency of the protection in OSNs. The construction of the OSN model has the nodes and the links to identify the fake profiles on Twitter. This paper proposes a novel technique to detect spam profiles and the proposed classifier is to classify the profile images from the dataset. The malicious profile detection technique is used to identify the fake profiles with the concept of a Twitter crawler that implements the extraction of data from the profile. The feature set analysis has been implemented with the feature related analysis. The user behavior detection utilizes the adjacent matrix to measure the similarity values within the friend’s profiles. The multi-variant Support Vector Machine classifier is developed for efficient classification with the kernel function. The proposed technique is compared with the well-known techniques of ECRModel, ISMA and DeepLink that the detection rate is 2.5% higher than the related techniques, the computation time is 220 s lesser than the related techniques and the proposed technique has 3.1% higher accuracy.

Identification of user profiles in online social networks: a combined approach with face recognition

This paper suggests improving the previously existing method of identifying user profiles in different online social networks by adding face recognition results to the model. It is assumed that the method will become more stable for identifying people with the same name, city and age. It will help to find more user profiles in different online social networks, which will improve the estimation of their personal characteristics. Evaluating user personality traits is one of the key tasks in protecting employees of enterprises and companies from social engineering attacks.

A semi-automated forensic investigation model for online social networks

Investigating the online social network profiles of victims, suspects, and witnesses are now part of almost every legal investigation, either it involves a criminal offense, financial fraud, or domestic lawsuit. However, investigating online social networks (OSN) is a technically complicated process that becomes more challenging due to the legal issues of privacy and authentication. Completely manual investigative methods are not feasible for OSN investigations due to the immense size and heterogeneity of social networks. However, the existing models for digital forensic investigation are not supporting automated or semi-automated forensic investigation processes. Furthermore, they are not addressing the fundamental differences and specific requirements of online social networks. The model presented in this work incorporates the robust features of standard investigation models and proposes a digital forensic investigation process model that explicitly addresses the necessities of OSN investigations. This work is addressing the issues of automating the forensic collection and analysis processes, defining crime scene boundaries, and outlining reasonable iterative collection procedures in online social network forensic investigation. This work is evaluated using a case study and is compared with existing practices and standards.

Social Media Fake Account Detection for Afan Oromo Language using Machine Learning

A social networking service serves as a platform to build social networks or social relations among people who, share interests, activities, backgrounds, or real life connections. A social network service is generally offered to participants who registers to this site with their unique representation (often a profile) and one’s social links. Most social network services are web-based and provide means for users to interact over the Internet. (M. Smruthi, , February 2019).Online social networking sites became an important means in our daily life. Millions of users register and share personal information with others. Because of the fast expansion of social networks, public may exploit them for unprincipled and illegitimate activities. As a result of this, privacy threats and disclosing personal information have become the most important issues to the users of social networking sites. The intent of creating fake profiles have become an adversary effect and difficult to detect such identities/malicious content without appropriate research. The current research that have been developed for detecting malicious content, primarily considered the characteristics of user profile. Most of the existing techniques lack comprehensive evaluation. In this work we propose new model using machine learning and NLP (Natural Language Processing) techniques to enhance the accuracy rate in detecting the fake identities in online social networks. We would like to apply this approach to Facebook by extracting the features like Time, date of publication, language, and geo position. (Srinivas Rao Pulluri1, A Comprehensive Model for Detecting Fake Profiles in Online Social Networks, 2017) DOI: 10.7176/NMMC/90-01 Publication date: May 31 st 2020

Social Media Fake Account Detection for Afan Oromo Language using Machine Learning

A social networking service serves as a platform to build social networks or social relations among people who, share interests, activities, backgrounds, or real life connections. A social network service is generally offered to participants who registers to this site with their unique representation (often a profile) and one’s social links. Most social network services are web-based and provide means for users to interact over the Internet. (M. Smruthi, , February 2019).Online social networking sites became an important means in our daily life. Millions of users register and share personal information with others. Because of the fast expansion of social networks, public may exploit them for unprincipled and illegitimate activities. As a result of this, privacy threats and disclosing personal information have become the most important issues to the users of social networking sites. The intent of creating fake profiles have become an adversary effect and difficult to detect such identities/malicious content without appropriate research. The current research that have been developed for detecting malicious content, primarily considered the characteristics of user profile. Most of the existing techniques lack comprehensive evaluation. In this work we propose new model using machine learning and NLP (Natural Language Processing) techniques to enhance the accuracy rate in detecting the fake identities in online social networks. We would like to apply this approach to Facebook by extracting the features like Time, date of publication, language, and geo position. (Srinivas Rao Pulluri1, A Comprehensive Model for Detecting Fake Profiles in Online Social Networks, 2017) DOI: 10.7176/NMMC/90-01 Publication date: May 31 st 2020

Browser Simulation-based Crawler for Online Social Network Profile Extraction

The rapid proliferation and extensive use of online social networks (OSNs) like Facebook, Twitter, Instagram, etc., has attracted the attention of academia and industry, since these networks store massive information in them. But, acquiring data from these OSNs, which is a prerequisite for conducting any research on them, is a daunting task, which can be because of privacy concerns on one hand and complexity of underlying technologies of these complex networks, on the other. This paper presents the design and implementation of a crawler based on browser simulation for extraction of Facebook users profile data while preserving the privacy. The breadth-first-search (BFS) algorithm approach was also adopted for sampling of around 0.235 million Facebook users. Though the main purpose of this work is the design of a crawler still, the results have been briefly presented in terms of various social network metrics and analysed from different aspects of privacy.

Browser simulation-based crawler for online social network profile extraction

Browser simulation-based crawler for online social network profile extraction

Fake Profile Identification in Online Social Networks

There is a tremendous increase in technologies these days.. Mobiles are becoming smart. Technology is associated with online social networks which has become a part in every one’s life in making new friends and keeping friends , their interests are known easier. But this increase in networking online make many problems like faking their profiles, online impersonation having become more and more in present days. Users are fed with more unnecessary knowledge during surfing which are posted by fake users. Researches have observed that 20% to 40% profiles in online social networks like facebook are fake profiles. Thus this detection of fake profiles in online social networks results into solution using frameworks.

TACASHI: Trust-Aware Communication Architecture for Social Internet of Vehicles

The Internet of Vehicles (IoV) has emerged as a new spin-off research theme from traditional vehicular ad hoc networks. It employs vehicular nodes connected to other smart objects equipped with a powerful multisensor platform, communication technologies, and IP-based connectivity to the Internet, thereby creating a possible social network called Social IoV (SIoV). Ensuring the required trustiness among communicating entities is an important task in such heterogeneous networks, especially for safety-related applications. Thus, in addition to securing intervehicle communication, the driver/passengers honesty factor must also be considered, since they could tamper the system in order to provoke unwanted situations. To bridge the gaps between these two paradigms, we envision to connect SIoV and online social networks (OSNs) for the purpose of estimating the drivers and passengers honesty based on their OSN profiles. Furthermore, we compare the current location of the vehicles with their estimated path based on their historical mobility profile. We combine SIoV, path-based and OSN-based trusts to compute the overall trust for different vehicles and their current users. As a result, we propose a trust-aware communication architecture for social IoV (TACASHI). TACASHI offers a trust-aware social in-vehicle and intervehicle communication architecture for SIoV considering also the drivers honesty factor based on OSN. Extensive simulation results evidence the efficiency of our proposal, ensuring high detection ratios >87% and high accuracy with reduced error ratios, clearly outperforming previous proposals, known as RTM and AD-IoV.

Automatic Detection of Fake Profiles in Online Social Networks

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.

Detection of Fake and Cloned Profiles in Online Social Networks

Online Social Network (OSN) is a platform to build social relations with others who share similar personal or career interests or real-life connections. OSNs allow users to share their views, likes, comments, opinions, photos, videos etc with other users in the network. As the popularity of OSNs are increasing day by day, the threats related to them are also increasing. Fake and Cloned profiles have become a severe security issue in social networks. Profile Cloning is an act of identity theft of existing user's profile credentials to create duplicate profiles. This cloned profile is misused for defaming legitimate profile owner. They even launch phishing attacks, harvest sensitive user information, stalking or spread viruses to other users. Fake profiles are created to carry out malicious activities and online social crimes. So, a detection method has been proposed which can effectively detect cloned and fake profiles in Online Social Networks.

OSSINT - Open Source Social Network Intelligence: An efficient and effective way to uncover “private” information in OSN profiles

Online Social Networks (OSNs), such as Facebook, provide users with tools to share information along with a set of privacy controls preferences to regulate the spread of information. Current privacy controls are efficient to protect content data. However, the complexity of tuning them undermine their efficiency when shielding contextual information (such as the social network structure) that many users believe being kept private.In this paper, we demonstrate the extent of the problem of information leakage in Facebook. In particular, we show the possibility of inferring, from the network “surrounding” a victim user, some information that the victim set as hidden. We developed a system, named OSSINT (Open Source Social Network INTelligence), on top of our previous tool SocialSpy, that can infer hidden information of a victim profile and retrieve private information from public one. OSSINT retrieves the friendship network of a victim and shows how it is possible to infer additional private information (e.g., personal user preferences and hobbies). Our proposed system OSSINT goes extra mile about the network topology information, i.e., predicting new friendships using the victim’s friends of friends network (2-hop of distance from the victim profile), and hence possibly deduce private information of the full Facebook network. OSSINT correctly improved the previous results of SocialSpy predicting an average of 11 new friendships with peaks of 20 new friends. Moreover, OSSINT, for the considered victim profiles demonstrated how it is possible to infer real-life information such as current city, hometown, university, supposed being private.

A personalized travel recommender system utilizing social network profile and accurate GPS data

Recent developments in internet technologies have created a massive demand for online services with the rapidly growing users. Travel recommender systems have been embraced by many researchers due to recent developments and significant requirements in the e-tourism domain. Generating personalised recommendations with minimal interactions is a key challenge and predicting personalised list of locations with the available ratings alone cannot achieve effective recommendations. To address this issue, we develop an intelligent real-time user-specific travel recommender system (IRTUSTRS) through incorporating users' social network profile and current location by exploiting global positioning system (GPS) data for travel recommendation generation. The proposed IRTUSTRS approach helps end users through enhanced travel recommendations with improved accuracy. The experimental evaluation portrays the improved performance of IRTUSTRS over baseline approaches. The presented work helps to understand the performance of recommender systems by utilising online social network profile of users with the current location through the GPS data.

A personalised travel recommender system utilising social network profile and accurate GPS data

Recent developments in internet technologies have created a massive demand for online services with the rapidly growing users. Travel recommender systems have been embraced by many researchers due to recent developments and significant requirements in the e-tourism domain. Generating personalised recommendations with minimal interactions is a key challenge and predicting personalised list of locations with the available ratings alone cannot achieve effective recommendations. To address this issue, we develop an intelligent real-time user-specific travel recommender system (IRTUSTRS) through incorporating users' social network profile and current location by exploiting global positioning system (GPS) data for travel recommendation generation. The proposed IRTUSTRS approach helps end users through enhanced travel recommendations with improved accuracy. The experimental evaluation portrays the improved performance of IRTUSTRS over baseline approaches. The presented work helps to understand the performance of recommender systems by utilising online social network profile of users with the current location through the GPS data.