Cognitive maps of social features enable flexible inference in social networks

2021 ◽  
Vol 118 (39) ◽  
pp. e2021699118
Author(s):  
Jae-Young Son ◽  
Apoorva Bhandari ◽  
Oriel FeldmanHall
Keyword(s):  
Social Networks ◽  
Computational Models ◽  
Cognitive Maps ◽  
Machine Learning Algorithms ◽  
Open Questions ◽  
Feature Spaces ◽  
The Social ◽  
Social Features ◽  
Feature Based ◽  
Statistical Relationships

In order to navigate a complex web of relationships, an individual must learn and represent the connections between people in a social network. However, the sheer size and complexity of the social world makes it impossible to acquire firsthand knowledge of all relations within a network, suggesting that people must make inferences about unobserved relationships to fill in the gaps. Across three studies (n = 328), we show that people can encode information about social features (e.g., hobbies, clubs) and subsequently deploy this knowledge to infer the existence of unobserved friendships in the network. Using computational models, we test various feature-based mechanisms that could support such inferences. We find that people’s ability to successfully generalize depends on two representational strategies: a simple but inflexible similarity heuristic that leverages homophily, and a complex but flexible cognitive map that encodes the statistical relationships between social features and friendships. Together, our studies reveal that people can build cognitive maps encoding arbitrary patterns of latent relations in many abstract feature spaces, allowing social networks to be represented in a flexible format. Moreover, these findings shed light on open questions across disciplines about how people learn and represent social networks and may have implications for generating more human-like link prediction in machine learning algorithms.

scholarly journals Link Prediction in Facebook using Web Scrapping and Deep Learning Techniques

2021 ◽  
Vol 10 (1) ◽  
pp. 194-197
Keyword(s):  
Social Networks ◽  
Deep Learning ◽  
Link Prediction ◽  
Machine Learning Techniques ◽  
Feature Engineering ◽  
Internet Technologies ◽  
Learning Techniques ◽  
The Social ◽  
Feature Based ◽  
Day By Day

As Internet technologies develop continuously social networks are getting more popular day by day. People are connected with each other via virtual applications. Using the Link Prediction in social networks more people get connected, may be they are friends, may be work together at the same workplace and may be their education are. Machine learning techniques are used to analyze the link between the nodes of the network and also create a better link prediction model through deep learning. The objective of this research is to measure the performance using the different techniques to predict link between the social networks. Using deep learning, feature engineering can be reduced for link prediction. In this research, the feature based learning is used to predict the link for better performance. Dataset is obtained by scraping the profile of Facebook users and they are used along with the random forest and graph convolution neural network to measure the performance of link prediction in social networks.

scholarly journals The Social Cloud for Public eResearch

2021 ◽  
Author(s):  
◽  
Koshy John
Keyword(s):  
Social Networks ◽  
Cloud Computing ◽  
Computational Models ◽  
Social Engineering ◽  
Functional System ◽  
Distributed Computation ◽  
Computational Time ◽  
The Novel ◽  
The Social ◽  
Social Cloud

<p>Scientific researchers faced with extremely large computations or the requirement of storing vast quantities of data have come to rely on distributed computational models like grid and cloud computing. However, distributed computation is typically complex and expensive. The Social Cloud for Public eResearch aims to provide researchers with a platform to exploit social networks to reach out to users who would otherwise be unlikely to donate computational time for scientific and other research oriented projects. This thesis explores the motivations of users to contribute computational time and examines the various ways these motivations can be catered to through established social networks. We specifically look at integrating Facebook and BOINC, and discuss the architecture of the functional system and the novel social engineering algorithms that power it.</p>

scholarly journals The Social Cloud for Public eResearch

2021 ◽  
Author(s):  
◽  
Koshy John
Keyword(s):  
Social Networks ◽  
Cloud Computing ◽  
Computational Models ◽  
Social Engineering ◽  
Functional System ◽  
Distributed Computation ◽  
Computational Time ◽  
The Novel ◽  
The Social ◽  
Social Cloud

<p>Scientific researchers faced with extremely large computations or the requirement of storing vast quantities of data have come to rely on distributed computational models like grid and cloud computing. However, distributed computation is typically complex and expensive. The Social Cloud for Public eResearch aims to provide researchers with a platform to exploit social networks to reach out to users who would otherwise be unlikely to donate computational time for scientific and other research oriented projects. This thesis explores the motivations of users to contribute computational time and examines the various ways these motivations can be catered to through established social networks. We specifically look at integrating Facebook and BOINC, and discuss the architecture of the functional system and the novel social engineering algorithms that power it.</p>

scholarly journals An efficient hybrid system for anomaly detection in social networks

Cybersecurity ◽  
2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Md. Shafiur Rahman ◽  
Sajal Halder ◽  
Md. Ashraf Uddin ◽  
Uzzal Kumar Acharjee
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Network ◽  
Anomaly Detection ◽  
Research Area ◽  
Machine Learning Algorithms ◽  
Security And Privacy ◽  
Support Vector ◽  
The Social ◽  
Wide Range

AbstractAnomaly detection has been an essential and dynamic research area in the data mining. A wide range of applications including different social medias have adopted different state-of-the-art methods to identify anomaly for ensuring user’s security and privacy. The social network refers to a forum used by different groups of people to express their thoughts, communicate with each other, and share the content needed. This social networks also facilitate abnormal activities, spread fake news, rumours, misinformation, unsolicited messages, and propaganda post malicious links. Therefore, detection of abnormalities is one of the important data analysis activities for the identification of normal or abnormal users on the social networks. In this paper, we have developed a hybrid anomaly detection method named DT-SVMNB that cascades several machine learning algorithms including decision tree (C5.0), Support Vector Machine (SVM) and Naïve Bayesian classifier (NBC) for classifying normal and abnormal users in social networks. We have extracted a list of unique features derived from users’ profile and contents. Using two kinds of dataset with the selected features, the proposed machine learning model called DT-SVMNB is trained. Our model classifies users as depressed one or suicidal one in the social network. We have conducted an experiment of our model using synthetic and real datasets from social network. The performance analysis demonstrates around 98% accuracy which proves the effectiveness and efficiency of our proposed system.

scholarly journals Identification of user’s credibility on twitter social networks

2021 ◽  
Vol 24 (1) ◽  
pp. 554
Author(s):  
Faraz Ahmad ◽  
S. A. M. Rizvi
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Nearest Neighbours ◽  
The Social ◽  
Class Labelling ◽  
Social Media Platforms ◽  
Media Platform ◽  
The Given

<p>Twitter is one of the most influential social media platforms, facilitates the spreading of information in the form of text, images, and videos. However, the credibility of posted content is still trailed by an interrogation mark. Introduction: In this paper, a model has been developed for finding the user’s credibility based on the tweets which they had posted on Twitter social networks. The model consists of machine learning algorithms that assist not only in categorizing the tweets into credibility classes but also helps in finding user’s credibility ratings on the social media platform. Methods and results: The dataset and associated features of 100,000 tweets were extracted and pre-processed. Furthermore, the credibility class labelling of tweets was performed using four different human annotators. The meaning cloud and natural language understanding platforms were used for calculating the polarity, sentiment, and emotions score. The K-Means algorithm was applied for finding the clusters of tweets based on features set, whereas, random forest, support vector machine, naïve Bayes, K-nearest-neighbours (KNN), J48 decision tree, and multilayer perceptron were used for classifying the tweets into credibility classes. A significant level of accuracy, precision, and recall was provided by all the classifiers for all the given credibility classes.</p>

scholarly journals Towards Inferring Influential Facebook Users

Computers ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 62
Author(s):  
Suleiman Ali Alsaif ◽  
Adel Hidri ◽  
Minyar Sassi Hidri
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Network ◽  
Exploratory Analysis ◽  
Machine Learning Algorithms ◽  
Statistical Tools ◽  
Social Actors ◽  
Graph Modeling ◽  
The Social ◽  
Facebook Pages

Because of the complexity of the actors and the relationships between them, social networks are always represented by graphs. This structure makes it possible to analyze the effectiveness of the network for the social actors who are there. This work presents a social network analysis approach that focused on processing Facebook pages and users who react to posts to infer influential people. In our study, we are particularly interested in studying the relationships between the posts of the page, and the reactions of fans (users) towards these posts. The topics covered include data crawling, graph modeling, and exploratory analysis using statistical tools and machine learning algorithms. We seek to detect influential people in the sense that the influence of a Facebook user lies in their ability to transmit and disseminate information. Once determined, these users have an impact on business for a specific brand. The proposed exploratory analysis has shown that the network structure and its properties have important implications for the outcome of interest.

scholarly journals Detection of Fake Profiles on Twitter Using Hybrid SVM Algorithm

2021 ◽  
Vol 309 ◽  
pp. 01046
Author(s):  
Sarangam Kodati ◽  
Kumbala Pradeep Reddy ◽  
Sreenivas Mekala ◽  
PL Srinivasa Murthy ◽  
P Chandra Sekhar Reddy
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Online Social Networks ◽  
Personal Data ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Reduction Techniques ◽  
Svm Algorithm ◽  
The Social ◽  
False News

Establishing and management of social relationships among huge amount of users has been provided by the emerging communication medium called online social networks (OSNs). The attackers have attracted because of the rapid increasing of OSNs and the large amount of its subscriber’s personal data. Then they pretend to spread malicious activities, share false news and even stolen personal data. Twitter is one of the biggest networking platforms of micro blogging social networks in which daily more than half a billion tweets are posted most of that are malware activities. Analyze, who are encouraging threats in social networks is need to classify the social networks profiles of the users. Traditionally, there are different classification methods for detecting the fake profiles on the social networks that needed to improve their accuracy rate of classification. Thus machine learning algorithms are focused in this paper. Therefore detection of fake profiles on twitter using hybrid Support Vector Machine (SVM) algorithm is proposed in this paper. The machine learning based hybrid SVM algorithm is used in this for classification of fake and genuine profiles of Twitter accounts and applied the dimension reduction techniques, feature selection and bots. Less number of features is used in the proposed hybrid SVM algorithm and 98% of the accounts are correctly classified with proposed algorithm.

scholarly journals How to Improve Customer Engagement in Social Networks: A Study of Spanish Brands in the Automotive Industry

2021 ◽  
Vol 16 (7) ◽  
pp. 3269-3281
Author(s):  
Luis Matosas-López ◽  
Alberto Romero-Ania
Keyword(s):  
Social Networks ◽  
Automotive Industry ◽  
Machine Learning Algorithms ◽  
Customer Engagement ◽  
Activity Data ◽  
The Social ◽  
Commercial Purpose ◽  
Regression Techniques ◽  
Publication Time ◽  
Number Of Publications

The objective of this research is to identify to what extent volumes, components, time slots, and publication topics improve customer engagement with Spanish automotive brands through social networks. The study considers thirteen brands and the total number of publications created by them in 2020 (23,670 publications) on the social network Twitter. Applying machine learning algorithms followed by multiple linear regression techniques, the authors examine how the variables previously mentioned affect a customer engagement indicator developed for this purpose. The results reveal that while publication components (links, mentions, and hashtags) and the publication time slot do not affect customer engagement, the volume of retweets made by the brand and publications on customer experience topics (without a direct commercial purpose) significantly improve the customer engagement indicator. The authors conclude that customer engagement in social networks can only be improved by conducting exhaustive analyses of activity data for these platforms. However, such analyses must not be done via generic multisector analyses, which only generate superficial and inapplicable knowledge, but rather through detailed studies for each sector.

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