scholarly journals SOCIAL NETWORKS USER CLASSIFICATION FOR PROFESSIONAL ORIENTATION

2021 ◽  
Vol 2 (68) ◽  
pp. 48-50
Author(s):  
V. Obrubova ◽  
M. Ozerova
Keyword(s):  
Social Networks ◽  
Professional Orientation ◽  
Complex Formulation ◽  
User Classification

The article deals with a complex formulation of the topic social networks users classification to determine professional orientation.

scholarly journals IMBALANCE OF CLASSES IN SOLVING THE PROBLEM OF SOCIAL NETWORKS USER CLASSIFICATION FOR PROFESSIONAL ORIENTATION

2021 ◽  
Vol 2 (68) ◽  
pp. 41-43
Author(s):  
V. Obrubova ◽  
M. Ozerova
Keyword(s):  
Social Networks ◽  
Recognition Rate ◽  
Classification Model ◽  
Specific Task ◽  
Classification Problems ◽  
Professional Orientation ◽  
Data Imbalance ◽  
User Classification

The problem of data imbalance is often underestimated when solving classification problems. A classification model that looks well trained on your data and gives a good recognition rate may not be reliable. Consideration of this problem in the specific task of classifying users of social networks will make it possible to understand how, why and, most importantly, when it is necessary to get rid from data imbalances.

A Time-based Visualization for Web User Classification in Social Networks

2014 ◽  
Author(s):  
Andrew S. Brunker ◽  
Richard R. Rosenkranz ◽  
Anetta Van Itallie ◽  
W. Kerry Mummery ◽  
Quang Vinh Nguyen ◽  
...  
Keyword(s):  
Social Networks ◽  
User Classification

scholarly journals User Classification on Online Social Networks by Post Frequency

2017 ◽  
Author(s):  
Gabriel Tavares ◽  
Saulo Mastelini ◽  
Sylvio Jr.
Keyword(s):  
Social Networks ◽  
Language Processing ◽  
Online Social Networks ◽  
Computational Cost ◽  
Real Life ◽  
Gradient Boosting ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
User Classification ◽  
Extreme Gradient Boosting

This paper proposes a technique for classifying user accounts on social networks to detect fraud in Online Social Networks (OSN). The main purpose of our classification is to recognize the patterns of users from Human, Bots or Cyborgs. Classic and consolidated approaches of Text Mining employ textual features from Natural Language Processing (NLP) for classification, but some drawbacks as computational cost, the huge amount of data could rise in real-life scenarios. This work uses an approach based on statistical frequency parameters of the user posting to distinguish the types of users without textual content. We perform the experiment over a Twitter dataset and as learn-based algorithms in classification task we compared Random Forest (RF), Support Vector Machine (SVM), k-nearest Neighbors (k-NN), Gradient Boosting Machine (GBM) and Extreme Gradient Boosting (XGBoost). Using the standard parameters of each algorithm, we achieved accuracy results of 88% and 84% by RF and XGBoost, respectively

Online Technologies As an Innovative Form of Working with Applicants

2021 ◽  
pp. 40-43
Author(s):  
N. Bakurova
Keyword(s):  
Social Networks ◽  
Career Guidance ◽  
Online Services ◽  
Traditional Methods ◽  
Professional Orientation ◽  
Guidance Services ◽  
Online Technologies ◽  
Main Ideas ◽  
University Training

This article offers an analysis of the system of professional orientation of modern students. The main problems of pre-university training arising from the digitalization and informatization of education are disclosed. The main ideas of applying social networks and online services in career guidance activities are substantiated. Significant advantages of online career guidance services over traditional methods are shown.

Multilayer Social Networks

2016 ◽  
Author(s):  
Mark E. Dickison ◽  
Matteo Magnani ◽  
Luca Rossi
Keyword(s):  
Social Networks ◽  
Multilayer Social Networks

Hostility and Perceptions of Support in Ambiguous Social Interactions

2006 ◽  
Vol 27 (2) ◽  
pp. 108-115 ◽  
Author(s):  
Ana-Maria Vranceanu ◽  
Linda C. Gallo ◽  
Laura M. Bogart
Keyword(s):  
Social Support ◽  
Social Networks ◽  
Information Processing ◽  
Social Interactions ◽  
Social Information Processing ◽  
Perceived Social Support ◽  
Social Information ◽  
Inverse Association ◽  
Processing Bias ◽  
Information Processing Bias

The present study investigated whether a social information processing bias contributes to the inverse association between trait hostility and perceived social support. A sample of 104 undergraduates (50 men) completed a measure of hostility and rated videotaped interactions in which a speaker disclosed a problem while a listener reacted ambiguously. Results showed that hostile persons rated listeners as less friendly and socially supportive across six conversations, although the nature of the hostility effect varied by sex, target rated, and manner in which support was assessed. Hostility and target interactively impacted ratings of support and affiliation only for men. At least in part, a social information processing bias could contribute to hostile persons' perceptions of their social networks.

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