Patterns of frequent user interactions in blogosphere

2020 ◽  
Author(s):  
Krzysztof Rudek ◽  
Jarosław Koźlak
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Relative Difference ◽  
Pattern Identification ◽  
Frequent Patterns ◽  
Frequent User ◽  
User Interactions ◽  
Data Set ◽  
Political Issues

Abstract The aim of the paper is to identify and categorize frequent patterns describing interactions between users in social networks. We analyze a social network with relationships between users that evolve in time already identified. In our research, we discover patterns based on frequent interactions between groups of users. The patterns are described by the characteristics of these interactions, such as their reciprocity, or the relative difference between estimations of global influences of the users participating in the discussions. The modification of the apriori algorithms is applied as one of the methods for pattern identification. The analyzed social network is built using the data set containing data from the Polish blog website salon24, which concerns mostly socio-political issues.

Centrality measure based approach for detection of malicious nodes in twitter social network

2018 ◽  
Vol 7 (4.5) ◽  
pp. 518 ◽  
Author(s):  
Krishna Das ◽  
Smriti Kumar Sinha
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Information Diffusion ◽  
Opinion Mining ◽  
Theoretical Method ◽  
Short Paper ◽  
Malicious Nodes ◽  
Data Set ◽  
Centrality Measure ◽  
The Social

In this short paper, network structural measure called centrality measure based mathematical approach is used for detection of malicious nodes in twitter social network. One of the objectives in analysing social networks is to detect malicious nodes which show anomaly behaviours in social networks. There are different approaches for anomaly detection in social networks such as opinion mining methods, behavioural methods, network structural approach etc. Centrality measure, a graph theoretical method related to social network structure, can be used to categorize a node either as popular and influential or as non-influential and anomalous node. Using this approach, we have analyzed twitter social network to remove anomalous nodes from the nodes-edges twitter data set. Thus removal of these kinds of nodes which are not important for information diffusion in the social network, makes the social network clean & speedy in fast information propagation.   

The Alt-social Network of Natalie Bookchin’s Testament

2016 ◽  
Vol 18 (5) ◽  
pp. 459-477
Author(s):  
Sarah Whitcomb Laiola
Keyword(s):  
Data Mining ◽  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
Online Social Networks ◽  
Social Web ◽  
User Interactions ◽  
Media Criticism ◽  
Direct Translation ◽  
The Social

This article addresses issues of user precarity and vulnerability in online social networks. As social media criticism by Jose van Dijck, Felix Stalder, and Geert Lovink describes, the social web is a predatory system that exploits users’ desires for connection. Although accurate, this critical description casts the social web as a zone where users are always already disempowered, so fails to imagine possibilities for users beyond this paradigm. This article examines Natalie Bookchin’s composite video series, Testament, as it mobilizes an alt-(ernative) social network of vernacular video on YouTube. In the first place, the alt-social network works as an iteration of “tactical media” to critically reimagine empowered user-to-user interactions on the social web. In the second place, it obfuscates YouTube’s data-mining functionality, so allows users to socialize online in a way that evades their direct translation into data and the exploitation of their social labor.

scholarly journals The method of calculating the protection of information from the interaction of users in social networks

2021 ◽  
Vol 45 (1) ◽  
Author(s):  
V. A. Savchenko ◽  
◽  
V. M. Akhramovych ◽  
T. M. Dzyuba ◽  
S. O. Laptіev ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Harmonic Oscillator ◽  
Nonlinear Function ◽  
User Interaction ◽  
Protection System ◽  
Time Interval ◽  
Information Protection ◽  
User Interactions ◽  
The Social

The elements of user interaction in social networks are considered: it is shown that the method of analysis of user interactions is based on assumptions when the magnitude of the influence depends on the centrality of users in the social network; the greater the consonance, the higher the nature of the influence, the interaction is a nonlinear function; it is indicated that interaction is a process that has a time interval, the linear model of protection of the information protection system from user interaction is considered; the obtained equations of protection are the equation of a harmonic oscillator with damping amplitude, the iteration of oscillations of the protection system in the pre-resonant, resonant and post-resonant zones is shown.

Defining the Factors that Effect User Interest on Social Network News Feeds via Fuzzy Association Rule Mining

2013 ◽  
pp. 334-345
Author(s):  
Basar Öztaysi ◽  
Sezi Çevik Onar
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining ◽  
Data Mining Technique ◽  
Data Set ◽  
Frequent Items ◽  
Fuzzy Association Rule ◽  
Fuzzy Association Rule Mining

Social networking became one of the main marketing tools in the recent years since it’s a faster and cheaper way to reach the customers. Companies can use social networks for efficient communication with their current and potential customers but the value created through the usage of social networks depends on how well the organizations use these tools. Therefore a support system which will enhance the usage of these tools is necessary. Fuzzy Association rule mining (FARM) is a commonly used data mining technique which focuses on discovering the frequent items and association rules in a data set and can be a powerful tool for enhancing the usage of social networks. Therefore the aim of the chapter is to propose a fuzzy association rule mining based methodology which will present the potential of using the FARM techniques in the field of social network analysis. In order to reveal the applicability, an experimental evaluation of the proposed methodology in a sports portal will be presented.

Semantic network edges: a human-machine approach to represent typed relations in social networks

2015 ◽  
Vol 19 (1) ◽  
pp. 71-81 ◽  
Author(s):  
M. Cristina Pattuelli ◽  
Matthew Miller
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Semantic Network ◽  
Contextual Information ◽  
Open Data ◽  
Entity Recognition ◽  
Semantic Web Technologies ◽  
Data Set ◽  
Content Type ◽  
External Data

Purpose – The purpose of this paper is to describe a novel approach to the development and semantic enhancement of a social network to support the analysis and interpretation of digital oral history data from jazz archives and special collections. Design/methodology/approach – A multi-method approach was applied including automated named entity recognition and extraction to create a social network, and crowdsourcing techniques to semantically enhance the data through the classification of relations and the integration of contextual information. Linked open data standards provided the knowledge representation technique for the data set underlying the network. Findings – The study described here identifies the challenges and opportunities of a combination of a machine and a human-driven approach to the development of social networks from textual documents. The creation, visualization and enrichment of a social network are presented within a real-world scenario. The data set from which the network is based is accessible via an application programming interface and, thus, shareable with the knowledge management community for reuse and mash-ups. Originality/value – This paper presents original methods to address the issue of detecting and representing semantic relationships from text. Another element of novelty is in that it applies semantic web technologies to the construction and enhancement of the network and underlying data set, making the data readable across platforms and linkable with external data sets. This approach has the potential to make social networks dynamic and open to integration with external data sources.

scholarly journals Point-process models of social network interactions: Parameter estimation and missing data recovery

2015 ◽  
Vol 27 (3) ◽  
pp. 502-529 ◽  
Author(s):  
JOSEPH R. ZIPKIN ◽  
FREDERIC P. SCHOENBERG ◽  
KATHRYN CORONGES ◽  
ANDREA L. BERTOZZI
Keyword(s):  
Social Networks ◽  
Parameter Estimation ◽  
Social Network ◽  
Missing Data ◽  
Process Model ◽  
The United States ◽  
Process Models ◽  
Likelihood Method ◽  
Electronic Communications ◽  
Data Set

Electronic communications, as well as other categories of interactions within social networks, exhibit bursts of activity localised in time. We adopt a self-exciting Hawkes process model for this behaviour. First we investigate parameter estimation of such processes and find that, in the parameter regime we encounter, the choice of triggering function is not as important as getting the correct parameters once a choice is made. Then we present a relaxed maximum likelihood method for filling in missing data in records of communications in social networks. Our optimisation algorithm adapts a recent curvilinear search method to handle inequality constraints and a non-vanishing derivative. Finally we demonstrate the method using a data set composed of email records from a social network based at the United States Military Academy. The method performs differently on this data and data from simulations, but the performance degrades only slightly as more information is removed. The ability to fill in large blocks of missing social network data has implications for security, surveillance, and privacy.

The impact of organizational social networks on salespeople’s negative headquarters stereotypes

2020 ◽  
Vol 35 (12) ◽  
pp. 1901-1913
Author(s):  
Babak Hayati ◽  
Sandeep Puri
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Sales Performance ◽  
Sales Managers ◽  
Cross Sectional ◽  
Data Set ◽  
Content Type ◽  
Network Patterns ◽  
The Impact ◽  
Sales Forces

Purpose Extant sales management literature shows that holding negative headquarters stereotypes (NHS) by salespeople is harmful to their sales performance. However, there is a lack of research on how managers can leverage organizational structures to minimize NHS in sales forces. This study aims to know how social network patterns influence the flow of NHS among salespeople and sales managers in a large B2B sales organization. Design/methodology/approach The authors hypothesize and test whether patterns of social networks among salespeople and sales managers determine the stereotypical attitudes of salespeople toward corporate directors and, eventually, impact their sales performance. The authors analyzed a multi-level data set from the B2B sales forces of a large US-based media company. Findings The authors found that organizational social network properties including the sales manager’s team centrality, sales team’s network density and sales team’s external connectivity moderate the flow of NHS from sales managers and peer salespeople to a focal salesperson. Research limitations/implications First, the data was cross-sectional and did not allow the authors to examine the dynamics of social network patterns and their impact on NHS. Second, The authors only focused on advice-seeking social networks and did not examine other types of social networks such as friendship and trust networks. Third, the context was limited to one company in the media industry. Practical implications The authors provide recommendations to sales managers on how to leverage and influence social networks to minimize the development and flow of NHS in sales forces. Originality/value The findings advance existing knowledge on how NHS gets shared and transferred in sales organizations. Moreover, this study provides crucial managerial insights with regard to controlling and managing NHS in sales forces.

scholarly journals PREDICTING TRUST IN A SOCIAL NETWORK BASED ON STRUCTURAL SIMILARITIES USING A MULTI-LAYERED PERCEPTRON NEURAL NETWORK

2021 ◽  
Vol 22 (1) ◽  
pp. 103-117
Author(s):  
Amir Hossein Danesh ◽  
Hossein Shirgahi
Keyword(s):  
Neural Network ◽  
Social Networks ◽  
Social Network ◽  
Previous History ◽  
Structural Similarity ◽  
Training Data ◽  
Negative Effects ◽  
Data Set ◽  
Trained Neural Network ◽  
The Web

Although research on social networks is progressing rapidly, the positive and negative effects of this area should be evaluated. One of the problems is that social networks are very broad and anyone can have influence on them. This matter can cause the issue of people with different beliefs. Therefore, determining the amount of trust to various resources on social networks, and especially resources for which there is no previous history on the web, is one of the main challenges in this field. In this paper, we present a method for predicting trust in a social network by structural similarities through the neural network. In this method, the web of trust data set is converted to a structural similarity data set based on the similarity of the trustors and trustees first. Then, on the created data set, a part of the data set is considered as the training data and it is trained based on the multilayer perceptron neural network and then the trained neural network is tested based on the test data. In the proposed method, the MSE value is less than 0.01, which has improved more than 0.02 compared to previous methods. Based on the obtained results, the proposed method has provided acceptable accuracy. ABSTRAK: Walaupun kajian tentang rangkaian sosial adalah sangat pesat, kesan positif dan negatif dalam ruang lingkup ini perlu dinilai. Masalah rangkaian sosial adalah sangat luas dan sesiapa sahaja boleh terpengaruh. Perkara ini akan menyebabkan manusia dengan pelbagai isu kepercayaan. Oleh itu, menentukan nilai kepercayaan melalui pelbagai sumber dalam rangkaian sosial, terutama sumber-sumber yang tidak mempunyai sejarah lepas dalam web, adalah salah satu cabaran dalam bidang ini. Kajian ini membentangkan jangkaan kepercayaan dalam rangkaian sosial melalui persamaan struktur dengan menggunakan rangkaian neural. Kaedah ini ditentukan dengan menukar set data web kepercayaan kepada struktur set data hampir sama berdasarkan kesamaan pemegang dan pemberi amanah. Kemudian, sebilangan set data yang telah dibina ini dipertimbangkan sebagai data latihan dan ia dilatih berdasarkan rangkaian neural perseptron berbagai lapisan dan kemudian rangkaian neural yang terlatih ini diuji berdasarkan data ujian. Dalam kaedah yang dicadangkan ini, nilai MSE adalah kurang daripada 0.01, di mana telah diperbaiki kepada 0.02 lebih daripada kaedah-kaedah sebelum ini. Berdasarkan dapatan kajian, didapati kaedah yang dicadangkan ini menunjukkan ketepatan yang boleh diterima.

scholarly journals Passing on the good vibes: Entrepreneurs’ social support

2019 ◽  
Vol 21 (1) ◽  
pp. 60-71 ◽  
Author(s):  
Mette Søgaard Nielsen
Keyword(s):  
Social Support ◽  
Social Networks ◽  
Social Network ◽  
Emotional Support ◽  
Significant Contribution ◽  
Instrumental Support ◽  
Data Set ◽  
Nascent Entrepreneur ◽  
Role Relationships ◽  
And Gender

This study investigates whether the emotional support individuals have available from their social network influences the likelihood that they in turn provide instrumental support to entrepreneurs they know: if they pass on the good vibes. Hypotheses are tested on a Danish data set consisting of individuals who know a nascent entrepreneur ( N = 392). The article demonstrates how emotional support made available to individuals from their social network impacts the likelihood of them providing entrepreneurs with instrumental support. Furthermore, findings show how the relation between available emotional support and the provision of instrumental support depends on culturally defined norms associated with various role-relationships and gender. The study contributes to existing theory by changing the perspective from focusing only on differences in characteristics between entrepreneurs to how the individuals in entrepreneurs’ social networks differ in characteristics and how this affects their social support to entrepreneurs. Furthermore, a significant contribution is the demonstration of how the provision of social support to entrepreneurs is contingent on different role-relationships.

scholarly journals Evaluation and analysis of activity in social networks in the context of the digital economy development

2019 ◽  
Vol 3 (3) ◽  
pp. 73-81 ◽  
Author(s):  
Iryna Symovonyk ◽  
Yurii Matseliukh ◽  
Yevheniia Redina
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Digital Economy ◽  
Cloud Services ◽  
Activity Data ◽  
User Interactions ◽  
User Choice ◽  
User Activity ◽  
The Impact

Introduction. In recent years, the Ukrainian economy has been rapidly gaining the leverage of the digital economy, with virtual products created through cloud services, social networks and virtual communications becoming the drivers of development. In these circumstances, the task of evaluating and analysing user activity on social networks, conducting constant monitoring of the number of readers, their groups, reflection and effectiveness of the published publication becomes relevant. That is why today, in the digital economy, special attention should be paid to research on user activity on social networks. Aim and tasks. The purpose of the article is to study the features of user activity within the model of digital economy, which defined the following tasks: 1) study the principles of the digital economy and the role of social networks; 2) analysing user activity within a specific social network account; 3) development of an approach to activity evaluation based on detailed minute-by-minute data collection on the number of user interactions with each other. Results. The article examines the impact of activity within a specific account on user choice on the Instagram social network. The best way to gather information on the number of likes and comments you posted under a post has been installed and successfully implemented. The fastest and most effective algorithms for storing, processing, analysing, and cost-effectively evaluating the collected user activity data within a specific account have been researched, substantiated and implemented. Conclusions. An approach to assessing and analysing user activity within a specific social network account is proposed to be implement into the Digital Economy Model. The proposed approach allows us to collect, detailed manner, the number of Instagram user interactions with the last post in your account, as well as estimate of their growth trends, compare them with the information collected in the past or to the average over the selected period.

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