scholarly journals The Extraction of Social Networks from Web Using Search Engines

2017 ◽  
Vol 2 (3) ◽  
pp. 170
Author(s):  
Faranak Salman Mohajer
Keyword(s):  
Social Network ◽  
Search Engine ◽  
Text Processing ◽  
Web Pages ◽  
Large Collection ◽  
The Social ◽  
Processing Techniques ◽  
Available Information ◽  
Google Search ◽  
Very High

In this paper, our purpose is to create a large collection of related vocabularies and concepts to the user’s favorite field (articles, people, conferences, books, etc.) from the available information on the infinite and vast source of web which is expressed in the form of social network. In the other words, we introduced a way to help the researchers to be able to specify their favorite topic in a particular field and by this way, observe and extract the social network of the related concepts to that topic. In order to extract the nodes of this network, we used the sampling of web pages through the Google search engine, text processing techniques, and information retrieval. The topic of the extracted social network in this research is the scientific conferences in the field of computer sciences. In order to evaluate the effectiveness of this method, the extracted network from the results of the search engine is compared with the scientific conferences available in the DBLP[1] database. The obtained results from the social network analysis showed that the extracted network is of very high accuracy.[1] Digital Bibliography and Library Project

Social Network Mining from the Web

2011 ◽  
pp. 149-175 ◽  
Author(s):  
Yutaka Matsuo ◽  
Junichiro Mori ◽  
Mitsuru Ishizuka
Keyword(s):  
Social Network ◽  
Search Engine ◽  
Relevant Information ◽  
Web Content ◽  
Message Boards ◽  
Network Information ◽  
Network Mining ◽  
Social Network Mining ◽  
The Social ◽  
The Web

This chapter describes social network mining from the Web. Since the end of the 1990s, several attempts have been made to mine social network information from e-mail messages, message boards, Web linkage structure, and Web content. In this chapter, we specifically examine the social network extraction from the Web using a search engine. The Web is a huge source of information about relations among persons. Therefore, we can build a social network by merging the information distributed on the Web. The growth of information on the Web, in addition to the development of a search engine, opens new possibilities to process the vast amounts of relevant information and mine important structures and knowledge.

Facebook & Google

2016 ◽  
Vol 57 (4) ◽  
pp. 24-29
Author(s):  
Tilo Hildebrandt
Keyword(s):  
Social Network ◽  
Marketing Strategy ◽  
Search Engine ◽  
Market Share ◽  
Special Role ◽  
Web Marketing ◽  
The Social ◽  
Pull Marketing ◽  
The Web

Die Bedeutung der großen Marketingplattformen nimmt stetig zu; inzwischen haben die meisten Unternehmen erkannt, dass diese im Hinblick auf ihre Marketing-Strategie viele Potenziale bereithalten. Eine besondere Rolle nehmen hier das soziale Netzwerk „Facebook“ und die Suchmaschine „Google“ ein. Der Beitrag „Facebook und Google. Wie Community und Suchportal in das Web-Business integriert werden“ von Prof. Dr. Tilo Hildebrandt, Geschäftsführer der DTH Beratungs- und Beteiligungs GmbH, stellt die Möglichkeiten dar, wie Unternehmen Facebook und Google für ihren Erfolg im Web nutzen können. Zunächst stehen das Pull-Marketing, wie wir es mit Blick auf Google erkennen, sowie das Push-Marketing, das Facebook zusätzlich hierzu nutzt, im Fokus. Im Anschluss erläutert Dr. Hildebrandt die Potenziale einer Community für das Web-Marketing. Darüber hinaus veranschaulicht er, inwiefern Facebook einen besonderen Nutzen als Marketing-Instrument aufweist und geht auf die dort auffindbaren Synergiegruppen ein. Abschließend verdeutlicht Dr. Hildebrandt die Verwendung spezifischer Controlling-Größen, die im Kontext von Facebook und Google als Grundlage für Optimierungsmaßnahmen genutzt werden können. The significance of the great marketing platforms is constantly increasing; by now most of the corporations have recognized that these platforms have many potentials with regard to their marketing strategy. A special role is assumed by the social network „Facebook“ (market share of 85% on all accesses to communities) and the search engine „Google“ (market share of more than 95% referring to the searches in Germany). The article „Facebook and Google. How to integrate community and search portal in the web business“ by Dr. Tilo Hildebrandt, CEO of DTH Beratungs- und Beteiligungs GmbH, represents the ways that companies can use Facebook and Google for their success in the web. Keywords: web business, wachstum, pull und push marketing, optimierung, community

scholarly journals Why-type Question to Query Reformulation for efficient Document Retrieval

2022 ◽  
Vol 12 (1) ◽  
pp. 0-0
Keyword(s):  
User Interface ◽  
Search Engine ◽  
Question Answering ◽  
Document Retrieval ◽  
Web Pages ◽  
Query Reformulation ◽  
Different Types ◽  
Why Questions ◽  
Google Search ◽  
Type Question

Understanding the actual need of user from a question is very crucial in non-factoid why-question answering as Why-questions are complex and involve ambiguity and redundancy in their understanding. The precise requirement is to determine the focus of question and reformulate them accordingly to retrieve expected answers to a question. The paper analyzes different types of why-questions and proposes an algorithm for each class to determine the focus and reformulate it into a query by appending focal terms and cue phrase ‘because’ with it. Further, a user interface is implemented which asks input why-question, applies different components of question , reformulates it and finally retrieve web pages by posing query to Google search engine. To measure the accuracy of the process, user feedback is taken which asks them to assign scoring from 1 to 10, on how relevant are the retrieved web pages according to their understanding. The results depict that maximum precision of 89% is achieved in Informational type why-questions and minimum of 48% in opinionated type why-questions.

A New Concept of Electronic Text Based on Semantic Coding System for Machine Translation

2022 ◽  
Vol 21 (1) ◽  
pp. 1-16
Author(s):  
Meftah Mohammed Charaf Eddine
Keyword(s):  
Machine Translation ◽  
Text Processing ◽  
Electronic Text ◽  
Web Pages ◽  
Coding System ◽  
Text Editor ◽  
Accuracy Rate ◽  
Structural Ambiguity ◽  
Very High ◽  
Structural Aspects

In the field of machine translation of texts, the ambiguity in both lexical (dictionary) and structural aspects is still one of the difficult problems. Researchers in this field use different approaches, the most important of which is machine learning in its various types. The goal of the approach that we propose in this article is to define a new concept of electronic text, which makes the electronic text free from any lexical or structural ambiguity. We used a semantic coding system that relies on attaching the original electronic text (via the text editor interface) with the meanings intended by the author. The author defines the meaning desired for each word that can be a source of ambiguity. The proposed approach in this article can be used with any type of electronic text (text processing applications, web pages, email text, etc.). Thanks to the approach that we propose and through the experiments that we have conducted using it, we can obtain a very high accuracy rate. We can say that the problem of lexical and structural ambiguity can be completely solved. With this new concept of electronic text, the text file contains not only the text but also with it the true sense of the exact meaning intended by the writer in the form of symbols. These semantic symbols are used during machine translation to obtain a translated text completely free of any lexical and structural ambiguity.

The self-Googling phenomenon: Investigating the performance of personalized information resources

First Monday ◽  
2009 ◽  
Author(s):  
Thomas Nicolai ◽  
Lars Kirchhof ◽  
Axel Bruns ◽  
Jason Wilson ◽  
Barry Saunders
Keyword(s):  
Search Engine ◽  
Search Engines ◽  
Information Resources ◽  
The Self ◽  
Web Pages ◽  
Online Experiment ◽  
Search Queries ◽  
Quantitative Studies ◽  
Significant Difference ◽  
Google Search

This paper investigates self-Googling through the monitoring of search engine activities of users and adds to the few quantitative studies on this topic already in existence. We explore this phenomenon by answering the following questions: To what extent is the self-Googling visible in the usage of search engines; is any significant difference measurable between queries related to self-Googling and generic search queries; to what extent do self-Googling search requests match the selected personalised Web pages? To address these questions we explore the theory of narcissism in order to help define self-Googling and present the results from a 14-month online experiment using Google search engine usage data.

scholarly journals The Involvement of Students in Social Network Sites Affects Their Learning

2019 ◽  
Vol 14 (13) ◽  
pp. 33 ◽  
Author(s):  
Elda Tartari ◽  
Alban Tartari ◽  
Dilina Beshiri
Keyword(s):  
Social Network ◽  
Negative Impact ◽  
Social Network Sites ◽  
Secondary Data ◽  
Web Pages ◽  
The Social ◽  
High Level ◽  
The Relationship ◽  
Extensive Involvement ◽  
Modern Technologies

The issue taken in consideration for this study is related to the extensive involvement of children in social media web pages and especially Facebook’s social network. The purpose of this study is to explore the relationship between the level of engagement of pupils in social network sites and their performance of academic achievement. The methods used in the study are secondary data review and quantity methods. The population of this survey is school pupils between 10 to 15 years old. The sample was 1323 pupils surveyed in this study, selected at random from elementary and secondary schools. The data analysis focuses primarily in regressive models of the logistic binary. The study findings revealed a high level of pupils' involvement in social networks and mainly on Facebook's social network. It was also shown that the social network Facebook has a negative impact on the learning objectives of pupils who have opened an address compared to others who don’t have an address in this network. Modern technologies are developing rapidly and the relationship between teachers, parents and pupils must function effectively through continuous communication on the effects of social network sites on their learning process.

Ontology-Based Image Annotation by Leveraging Social Context

2012 ◽  
Vol 3 (3) ◽  
pp. 53-66
Author(s):  
Najeeb Elahi ◽  
Randi Karlsen ◽  
Waqas Younas
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Image Annotation ◽  
Automatic Annotation ◽  
Image Management ◽  
Active User ◽  
The Social ◽  
Central Actor ◽  
Processing Techniques

Manual image annotation is an extensive and a cumbersome task, yet extremely important for image management and retrieval. The purpose of the authors’ system is to semi-automatically generate ontology-based annotations for a social network by leveraging the annotations provided by the most active user (i.e., the central actor). Context of an image is of central importance in their approach towards semantic semi-automatic annotation. For context of an image, the authors consider several factors like geo-reference, time and relationship among actors in social networks and instead of using image-processing techniques to manipulate and interpret the image, their system leverages the context, which is automatically available along with the image and have also extended Social Network Analysis techniques by considering the granularity of relationships among actors under consideration. The authors use a semantic web framework to represent the social network and to deal with the diversity of relationships. OntoCAIM ontology is developed which not only encompasses Social Network Analysis functionality but also defines mechanism to annotate the images with an underlying ontology.

Social Network Models for Enhancing Reference-Based Search Engine Rankings

2011 ◽  
pp. 109-133 ◽  
Author(s):  
Nikolaos Korfiatis ◽  
Miguel-Ángel Sicilia ◽  
Claudia Hess ◽  
Klaus Stein ◽  
Christoph Schlieder
Keyword(s):  
Information Retrieval ◽  
Social Network ◽  
Social Context ◽  
Search Engine ◽  
Web Search ◽  
Network Models ◽  
Reference Network ◽  
Ranking Models ◽  
The Social ◽  
Network Metrics

This chapter discusses the integration of information retrieval information from two sources: a social network and a document reference network, for enhancing reference based search engine rankings. In particular, current models of information retrieval are blind to the social context that surrounds information resources thus do not consider the trustworthiness of their authors when they present the query results to the users. Following this point we elaborate on the basic intuitions that highlight the contribution of the social context – as can be mined from social network positions for instance – into the improvement of the rankings provided in reference based search engines. A review on ranking models in web search engine retrieval along with social network metrics of importance such as prestige and centrality is provided as a background. Then a presentation of recent research models that utilize both contexts is provided along with a case study in the internet based encyclopedia Wikipedia based on the social network metrics.

The social network

2013 ◽  
Vol 44 (2) ◽  
pp. 22
Author(s):  
ALAN ROCKOFF
Keyword(s):  
Social Network ◽  
The Social

The Multilevel p2 Model

Methodology ◽  
2006 ◽  
Vol 2 (1) ◽  
pp. 42-47 ◽  
Author(s):  
Bonne J. H. Zijlstra ◽  
Marijtje A. J. van Duijn ◽  
Tom A. B. Snijders
Keyword(s):  
Social Network ◽  
Network Data ◽  
Mcmc Algorithm ◽  
Independent Observation ◽  
Single Observation ◽  
Multiple Networks ◽  
Social Network Data ◽  
Multiple Observations ◽  
Practical Support ◽  
The Social

The p 2 model is a random effects model with covariates for the analysis of binary directed social network data coming from a single observation of a social network. Here, a multilevel variant of the p 2 model is proposed for the case of multiple observations of social networks, for example, in a sample of schools. The multilevel p 2 model defines an identical p 2 model for each independent observation of the social network, where parameters are allowed to vary across the multiple networks. The multilevel p 2 model is estimated with a Bayesian Markov Chain Monte Carlo (MCMC) algorithm that was implemented in free software for the statistical analysis of complete social network data, called StOCNET. The new model is illustrated with a study on the received practical support by Dutch high school pupils of different ethnic backgrounds.

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