scholarly journals Towards Inferring Influential Facebook Users

Computers ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 62
Author(s):  
Suleiman Ali Alsaif ◽  
Adel Hidri ◽  
Minyar Sassi Hidri

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.

Cybersecurity ◽  
2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Md. Shafiur Rahman ◽  
Sajal Halder ◽  
Md. Ashraf Uddin ◽  
Uzzal Kumar Acharjee

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.


2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Nauman Ali Khan ◽  
Wuyang Zhou ◽  
Mudassar Ali Khan ◽  
Ahmad Almogren ◽  
Ikram Ud Din

Social Internet of Things (SIoT) is a variation of social networks that adopt the property of peer-to-peer networks, in which connections between the things and social actors are automatically established. SIoT is a part of various organizations that inherit the social interaction, and these organizations include industries, institutions, and other establishments. Triadic closure and homophily are the most commonly used measures to investigate social networks’ formation and nature, where both measures are used exclusively or with statistical models. The triadic closure patterns are mapped for actors’ communication behavior over a location-based social network, affecting the homophily. In this study, we investigate triads emergence in homophilic social networks. This evaluation is based on the empirical review of triads within social networks (SNs) formed on Big Data. We utilized a large location-based dataset for an in-depth analysis, the Chinese telecommunication-based anonymized call detail records (CDRs). Two other openly available datasets, Brightkite and Gowalla, were also studied. We identified and proposed three social triad classes in a homophilic network to feature the correlation between social triads and homophily. The study opened a promising research direction that relates the variation of homophily based on closure triads nature. The homophilic triads are further categorized into transitive and intransitive groups. As our concluding research objective, we examined the relative triadic throughput within a location-based social network for the given datasets. The research study attains significant results highlighting the positive connection between homophily and a specific social triad class.


Management ◽  
2019 ◽  
Author(s):  
Sana Ansari ◽  
Dalhia Mani

The field of social networks focuses on the relationships among social actors, and on patterns that emerge from the structure of the social network and its implications (Wasserman and Faust’s Social Network Analysis: Methods and Applications). Social network research argues that actors (e.g., individuals or firms) are embedded within a network of relations, and that their behavior and choices cannot be studied independent of the social relations that shape and structure behavior. Social network perspective views relations among the social actors as ties and regular patterns in relationship as structure. Ties are the relational linkages that allow flow of resources between the actors, both tangible and intangible. Multiple actors form a web of relational ties, which can be either economic, social, or political. Networks can be of different types based on the content of the relational tie between the actors. For instance, collaboration ties between actors make a collaboration network or a co-author relation between actors makes a co-authorship network. Networks can also be at different levels of analysis—for instance, an intraorganizational friendship network is at the level of individuals while a network of intercountry trade relations is at the level of country. Ties between actors can be of different strengths (for instance, friends who meet daily versus once a year) and can also be negative or positive ties (e.g., competition networks versus collaboration networks). This article summarizes the latest research on social ties and network structure by focusing on the main thematic discussions in the field: (1) networks and strategic, governance behavior; (2) workplace networks; (3) collaboration and knowledge networks; (4) networks, personality, and individual differences; (5) entrepreneurial and family business networks; and (6) networks and social media. To ensure a comprehensive review of the topic, the article used search keywords, “networks,” or “network structure,” or “social networks,” or “social ties,” and was limited to articles in the top fourteen management journals, namely: Academy of Management Journal, Strategic Management Journal, Organization Science, Management Science, American Journal of Sociology, American Sociological Review, Administrative Science Quarterly, Academy of Management Review, Journal of Management Studies, Journal of Business Venturing, and Entrepreneurship Theory and Practice. The search was further limited to the six-year period from 2014–2019, since previous articles on organizational networks and brokerage in Oxford Bibliographies have summarized the research in this domain prior to 2014.


2016 ◽  
Vol 4 (2) ◽  
pp. 44-58 ◽  
Author(s):  
Ruti Gafni ◽  
Osnat Tal Golan

The social networking revolution allows people to share their opinions with their surrounding society, enabling the ability to influence others. Large amounts of consumer reviews are posted on social networks, expressing experiences, either positive or negative, regarding products/services. These reviews are instantly distributed within a huge network of consumers, challenging the firms' managers who need to cope with that. This research study examines the phenomenon of consumers' reviews posted on social networks to measure the influence of negative reviews on the reader's buying decisions and on the firms' attitudes. This research study examines if there are differences between active users, who post and share reviews, and passive users who only read what others posted. This research study was performed merging three sources of information: (1) monitoring consumer posts on three Facebook pages during six months; (2) performing a relevant questionnaire among 201 respondents, and (3) checking the related firms' reaction to those posts. The findings revealed that potential consumers base their decisions on posted reviews; they are exposed to negative reviews that affect their purchase decisions, incoherently to the manner they use the social network (active or passive users), while the firms mostly react, in order to diminish their influence.


Author(s):  
Mohcine Kodad

This paper presents a study that contributes to the existing work on the social diffusion and interaction strategy in social media. The aim is to know the most shared post by some electronic media in the world from end to end social network, and also to know post nature of the most successful one, and the link between different kind of interaction these are main objectives of this study. Our work is also considered as a ground and a base for social network analysis researchers in all social networks in order to allow them to benefit and help in their future research work from all information collected and results found via this study. An empirical analysis using multiple methods is conducted based on 275 Facebook publications gathered from the Facebook pages of 5 electronics journals the best one in its original country represented 5 countries in the world. This contribution discovered a set of important information and it is also projected to confirm hypothesis addressed in pre-existing studies


Author(s):  
M. G. Khachatrian ◽  
P. G. Klyucharev

Online social networks are of essence, as a tool for communication, for millions of people in their real world. However, online social networks also serve an arena of information war. One tool for infowar is bots, which are thought of as software designed to simulate the real user’s behaviour in online social networks.The paper objective is to develop a model for recognition of bots in online social networks. To develop this model, a machine-learning algorithm “Random Forest” was used. Since implementation of machine-learning algorithms requires the maximum data amount, the Twitter online social network was used to solve the problem of bot recognition. This online social network is regularly used in many studies on the recognition of bots.For learning and testing the Random Forest algorithm, a Twitter account dataset was used, which involved above 3,000 users and over 6,000 bots. While learning and testing the Random Forest algorithm, the optimal hyper-parameters of the algorithm were determined at which the highest value of the F1 metric was reached. As a programming language that allowed the above actions to be implemented, was chosen Python, which is frequently used in solving problems related to machine learning.To compare the developed model with the other authors’ models, testing was based on the two Twitter account datasets, which involved as many as half of bots and half of real users. As a result of testing on these datasets, F1-metrics of 0.973 and 0.923 were obtained. The obtained F1-metric values  are quite high as compared with the papers of other authors.As a result, in this paper a model of high accuracy rates was obtained that can recognize bots in the Twitter online social network.


2014 ◽  
Vol 8 (3) ◽  
pp. 1411-1413
Author(s):  
Nader Yahya Alkeinay ◽  
Norita Md Norwawi ◽  
Fauziah Abdul Wahid ◽  
Roesnita Ismail ◽  
Najwa Hayaati Mohd Alwi

Social network is term used to refer to the social structure that is made up of a set of social actors. The social actors in this case include organizations or individuals. Social networks allow people to interact and socialize as they get to learn and know each other. Through social networking sites, people from different parts of a country or the world also get to meet and interact. However, there have been issues with regards to social network privacy for those who use the internet to use social network sites. This paper will look at some of the factors that affect trust of the users as well as the privacy issues related to social networks (Fernandez, 2009).


2021 ◽  
Vol 309 ◽  
pp. 01046
Author(s):  
Sarangam Kodati ◽  
Kumbala Pradeep Reddy ◽  
Sreenivas Mekala ◽  
PL Srinivasa Murthy ◽  
P Chandra Sekhar Reddy

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.


Author(s):  
Ana Maria Magdalena Saldana-Perez ◽  
Marco Antonio Moreno-Ibarra ◽  
Miguel Jesus Torres-Ruiz

It is interesting to exploit the user generated content (UGC), and to use it with a view to infer new data; volunteered geographic information (VGI) is a concept derived from UGC, which main importance lies in its continuously updated data. The present approach tries to explode the use of VGI, by collecting data from a social network and a RSS service; the short texts collected from the social network are written in Spanish language; a text mining and a recovery information processes are applied over the data, in order to remove special characters on text, and to extract relevant information about the traffic events on the study area, then data are geocoded. The texts are classified by using a machine learning algorithm into five classes, each of them represents a specific traffic event or situation.


2021 ◽  
Vol 9 ◽  
Author(s):  
Sensen Guo ◽  
Xiaoyu Li ◽  
Zhiying Mu

In recent years, machine learning technology has made great improvements in social networks applications such as social network recommendation systems, sentiment analysis, and text generation. However, it cannot be ignored that machine learning algorithms are vulnerable to adversarial examples, that is, adding perturbations that are imperceptible to the human eye to the original data can cause machine learning algorithms to make wrong outputs with high probability. This also restricts the widespread use of machine learning algorithms in real life. In this paper, we focus on adversarial machine learning algorithms on social networks in recent years from three aspects: sentiment analysis, recommendation system, and spam detection, We review some typical applications of machine learning algorithms and adversarial example generation and defense algorithms for machine learning algorithms in the above three aspects in recent years. besides, we also analyze the current research progress and prospects for the directions of future research.


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