scholarly journals ТРАНСФОРМАЦИЯ СОЦИАЛЬНЫХ ПРАКТИК ОТВЕТСТВЕННОГО ОТЦОВСТВА В ДЕВИАНТНЫЕ ФОРМЫ СОЦИАЛЬНОЙ АКТИВНОСТИ (в печати)

2021 ◽  
Author(s):  
Анна Юрьевна Карпова ◽  
Алексей Олегович Савельев ◽  
Сергей Анатольевич Кузнецов
Keyword(s):  
Web Mining

Изучение социальных практик ответственного отцовства актуализируется в настоящее время в контексте трансформации института отцовства. Особое внимание исследователей направлено на социальное конструирование ответственного отцовства в медиа пространстве – профильных социальных сетях сообществ. Проблема в том, что внутри отцовских сообществ в социальных сетях появляются маркеры радикальных мнений, идей, которые по сути являются предиктором перехода к девиантным формам социальной активности и деструктивным практикам. Цель исследования – оценка семантического сходства и перекрестных связей между сообществами «Союза отцов» и «Совета отцов» и сообществами с признаками радикализации. Методы исследования: контент анализ, web mining. По результатам исследования обозначено наличие устойчивой связи тематического и семантического сходства между отдельными сообществами «отцов» и «пролайферов». Отмечаются признаки трансформации социальных практик ответственного отцовства в девиантные формы социальной активности. Подчеркивается, что применение разнообразных методов web mining повышает возможность оценить влияние радикальных мнений и идей в онлайн среде, соответственно, возрастает возможность предупреждения и предотвращения такой формы социальной активности в сообществах ответственных отцов как распространение радикальной идеологии. В заключении, обозначены направления будущих исследований.

Low-complexity fuzzy relational clustering algorithms for Web mining

2001 ◽  
Vol 9 (4) ◽  
pp. 595-607 ◽  
Author(s):  
R. Krishnapuram ◽  
A. Joshi ◽  
O. Nasraoui ◽  
L. Yi
Keyword(s):  
Web Mining ◽  
Clustering Algorithms ◽  
Low Complexity ◽  
Relational Clustering

scholarly journals HPM: A Hybrid Model for User’s Behavior Prediction Based on N-Gram Parsing and Access Logs

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Sonia Setia ◽  
Verma Jyoti ◽  
Neelam Duhan
Keyword(s):  
Web Mining ◽  
Contextual Information ◽  
Hybrid Approach ◽  
Web Pages ◽  
Continuous Growth ◽  
Novel Approach ◽  
Content Mining ◽  
Long Access ◽  
N Gram ◽  
The Individual

The continuous growth of the World Wide Web has led to the problem of long access delays. To reduce this delay, prefetching techniques have been used to predict the users’ browsing behavior to fetch the web pages before the user explicitly demands that web page. To make near accurate predictions for users’ search behavior is a complex task faced by researchers for many years. For this, various web mining techniques have been used. However, it is observed that either of the methods has its own set of drawbacks. In this paper, a novel approach has been proposed to make a hybrid prediction model that integrates usage mining and content mining techniques to tackle the individual challenges of both these approaches. The proposed method uses N-gram parsing along with the click count of the queries to capture more contextual information as an effort to improve the prediction of web pages. Evaluation of the proposed hybrid approach has been done by using AOL search logs, which shows a 26% increase in precision of prediction and a 10% increase in hit ratio on average as compared to other mining techniques.

Semantic Web mining for Content-Based Online Shopping Recommender Systems

2019 ◽  
Vol 15 (4) ◽  
pp. 41-56 ◽  
Author(s):  
Ibukun Tolulope Afolabi ◽  
Opeyemi Samuel Makinde ◽  
Olufunke Oyejoke Oladipupo
Keyword(s):  
Semantic Web ◽  
Recommender Systems ◽  
Web Mining ◽  
Online Shopping ◽  
Semantic Analysis ◽  
Second Phase ◽  
Web Content ◽  
Textual Data ◽  
Content Mining ◽  
Bayes Algorithm

Currently, for content-based recommendations, semantic analysis of text from webpages seems to be a major problem. In this research, we present a semantic web content mining approach for recommender systems in online shopping. The methodology is based on two major phases. The first phase is the semantic preprocessing of textual data using the combination of a developed ontology and an existing ontology. The second phase uses the Naïve Bayes algorithm to make the recommendations. The output of the system is evaluated using precision, recall and f-measure. The results from the system showed that the semantic preprocessing improved the recommendation accuracy of the recommender system by 5.2% over the existing approach. Also, the developed system is able to provide a platform for content-based recommendation in online shopping. This system has an edge over the existing recommender approaches because it is able to analyze the textual contents of users feedback on a product in order to provide the necessary product recommendation.

Application of Data Pre-Processing Method in Web Mining

2014 ◽  
Vol 687-691 ◽  
pp. 1592-1595
Author(s):  
Yun Peng Duan ◽  
Chun Xi Zhao ◽  
Ying Shi
Keyword(s):  
Data Mining ◽  
Web Mining ◽  
Early Stage ◽  
Data Preprocessing ◽  
Web Technology ◽  
Processing Method ◽  
Web Log Mining ◽  
Web Log ◽  
User Access ◽  
Log Mining

With the widely application of the WWW and the emergence of Web technology, make the research of data mining has entered a new stage. Web log mining is based on the idea of data mining to analyze the server log processing. Paper aimed at the early stage of the data mining is put forward based on log data preprocessing methods, the purpose is to divide server logs into multiple unique user access sequence at a time, and to give a good algorithm.

scholarly journals Semantic Web Mining using RDF Data

2016 ◽  
Vol 133 (10) ◽  
pp. 14-19 ◽  
Author(s):  
V. A. ◽  
Amruta A.
Keyword(s):  
Semantic Web ◽  
Web Mining ◽  
Rdf Data
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