Introduction to Search Engine Optimization (SEO)
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Search Engine Optimization: a Survey of Current Best Practices
Grand Valley State University ScholarWorks@GVSU Technical Library School of Computing and Information Systems 2013 Search Engine Optimization: A Survey of Current Best Practices Niko Solihin Grand Valley Follow this and additional works at: https://scholarworks.gvsu.edu/cistechlib ScholarWorks Citation Solihin, Niko, "Search Engine Optimization: A Survey of Current Best Practices" (2013). Technical Library. 151. https://scholarworks.gvsu.edu/cistechlib/151 This Project is brought to you for free and open access by the School of Computing and Information Systems at ScholarWorks@GVSU. It has been accepted for inclusion in Technical Library by an authorized administrator of ScholarWorks@GVSU. For more information, please contact [email protected]. Search Engine Optimization: A Survey of Current Best Practices By Niko Solihin A project submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Information Systems at Grand Valley State University April, 2013 _______________________________________________________________________________ Your Professor Date Search Engine Optimization: A Survey of Current Best Practices Niko Solihin Grand Valley State University Grand Rapids, MI, USA [email protected] ABSTRACT 2. Build and maintain an index of sites’ keywords and With the rapid growth of information on the web, search links (indexing) engines have become the starting point of most web-related 3. Present search results based on reputation and rele- tasks. In order to reach more viewers, a website must im- vance to users’ keyword combinations (searching) prove its organic ranking in search engines. This paper intro- duces the concept of search engine optimization (SEO) and The primary goal is to e↵ectively present high-quality, pre- provides an architectural overview of the predominant search cise search results while efficiently handling a potentially engine, Google. -
Brand Values and the Bottom Line 1 1
Brand Values and the Bottom Line 1 1. Why You Should Read This Guide 3 2. Common Obstacles to Avoid 4 3. Website Structure 7 4. Keyword Research 8 5. Meta Information 9 Contents 6. Body Content 11 7. Internal Site Linking 12 8. URL Equity 13 9. The Elements of URL Equity 14 10. Assessing URL Equity 15 11. The Consequences of Redesigning Without a URL Strategy 16 12. Migrating URL Equity 17 13. Summary 18 14. Appendix 1 19 15. Appendix 2 20 16. About Investis Digital 21 Brand Values and the Bottom Line 2 1. Why You Should Read This Guide Best Practices: SEO for Website Redesign & Migration outlines organic search optimization best practices for a website redesign, as well as factors to consider in order to maintain the URL equity during a domain or platform migration. This guide illustrates the common pitfalls that you can avoid in the redesign phase of a website, making it possible for a site to gain better visibility within search engines results. Additionally, Best Practices: SEO for Website Redesign & Migration explains the importance of setting up all aspects of a website, To do a deep dive including: directory structure, file names, page content and internal into SEO for website linking. Finally, we illustrate case study examples of successful site redesign and redesigns. migration, contact Reading this guide will set you up Investis Digital. for SEO success when you undergo a website redesign. The guide will We’re here to help. help you avoid costly errors and gain more traffic, leading to valuable conversions. -
Received Citations As a Main SEO Factor of Google Scholar Results Ranking
RECEIVED CITATIONS AS A MAIN SEO FACTOR OF GOOGLE SCHOLAR RESULTS RANKING Las citas recibidas como principal factor de posicionamiento SEO en la ordenación de resultados de Google Scholar Cristòfol Rovira, Frederic Guerrero-Solé and Lluís Codina Nota: Este artículo se puede leer en español en: http://www.elprofesionaldelainformacion.com/contenidos/2018/may/09_esp.pdf Cristòfol Rovira, associate professor at Pompeu Fabra University (UPF), teaches in the Depart- ments of Journalism and Advertising. He is director of the master’s degree in Digital Documenta- tion (UPF) and the master’s degree in Search Engines (UPF). He has a degree in Educational Scien- ces, as well as in Library and Information Science. He is an engineer in Computer Science and has a master’s degree in Free Software. He is conducting research in web positioning (SEO), usability, search engine marketing and conceptual maps with eyetracking techniques. https://orcid.org/0000-0002-6463-3216 [email protected] Frederic Guerrero-Solé has a bachelor’s in Physics from the University of Barcelona (UB) and a PhD in Public Communication obtained at Universitat Pompeu Fabra (UPF). He has been teaching at the Faculty of Communication at the UPF since 2008, where he is a lecturer in Sociology of Communi- cation. He is a member of the research group Audiovisual Communication Research Unit (Unica). https://orcid.org/0000-0001-8145-8707 [email protected] Lluís Codina is an associate professor in the Department of Communication at the School of Com- munication, Universitat Pompeu Fabra (UPF), Barcelona, Spain, where he has taught information science courses in the areas of Journalism and Media Studies for more than 25 years. -
How Does Google Rank Web Pages in the Different Types of Results That Matter to the Real Estate World?
Easy Steps to Improve SEO for Your Website How does Google rank web pages in the different types of results that matter to the real estate world? When searching Google, a great variety of different listings can be returned in the SERP (Search Engine Re- sults Page). Images, videos, news stories, instant answers and facts, interactive calculators, and even cheeky jokes from Douglas Adams novels (example) can appear. But for real estate professionals, two kinds of re- sults matter most-- traditional organic listings and local (or “maps”) results. These are illustrated in the que- ry below: Easy Steps to Improve SEO for Your Website How does Google rank web pages in the different types of results that matter to the real estate world? Many search results that Google deems to have local search intent display a map with local results (that Google calls “Places”) like the one above. On mobile devices, these maps results are even more common, more prominent, and can be more interactive, as Google results enables quick access to a phone call or di- rections. You can see that in action via the smartphone search below (for “Denver Real Estate Agents”): Easy Steps to Improve SEO for Your Website How does Google rank web pages in the different types of results that matter to the real estate world? Real estate professionals seeking to rank in Google’s results should pay careful attention to the types of results pro- duced for the queries in which they seek to rank. Why? Because Google uses two very different sets of criteria to de- termine what can rank in each of these. -
Evaluation of Web-Based Search Engines Using User-Effort Measures
Evaluation of Web-Based Search Engines Using User-Effort Measures Muh-Chyun Tang and Ying Sun 4 Huntington St. School of Information, Communication and Library Studies Rutgers University, New Brunswick, NJ 08901, U.S.A. [email protected] [email protected] Abstract This paper presents a study of the applicability of three user-effort-sensitive evaluation measures —“first 20 full precision,” “search length,” and “rank correlation”—on four Web-based search engines (Google, AltaVista, Excite and Metacrawler). The authors argue that these measures are better alternatives than precision and recall in Web search situations because of their emphasis on the quality of ranking. Eight sets of search topics were collected from four Ph.D. students in four different disciplines (biochemistry, industrial engineering, economics, and urban planning). Each participant was asked to provide two topics along with the corresponding query terms. Their relevance and credibility judgment of the Web pages were then used to compare the performance of the search engines using these three measures. The results show consistency among these three ranking evaluation measures, more so between “first 20 full precision” and search length than between rank correlation and the other two measures. Possible reasons for rank correlation’s disagreement with the other two measures are discussed. Possible future research to improve these measures is also addressed. Introduction The explosive growth of information on the World Wide Web poses a challenge to traditional information retrieval (IR) research. Other than the sheer amount of information, some structural factors make searching for relevant and quality information on the Web a formidable task. -
How to Choose a Search Engine Or Directory
How to Choose a Search Engine or Directory Fields & File Types If you want to search for... Choose... Audio/Music AllTheWeb | AltaVista | Dogpile | Fazzle | FindSounds.com | Lycos Music Downloads | Lycos Multimedia Search | Singingfish Date last modified AllTheWeb Advanced Search | AltaVista Advanced Web Search | Exalead Advanced Search | Google Advanced Search | HotBot Advanced Search | Teoma Advanced Search | Yahoo Advanced Web Search Domain/Site/URL AllTheWeb Advanced Search | AltaVista Advanced Web Search | AOL Advanced Search | Google Advanced Search | Lycos Advanced Search | MSN Search Search Builder | SearchEdu.com | Teoma Advanced Search | Yahoo Advanced Web Search File Format AllTheWeb Advanced Web Search | AltaVista Advanced Web Search | AOL Advanced Search | Exalead Advanced Search | Yahoo Advanced Web Search Geographic location Exalead Advanced Search | HotBot Advanced Search | Lycos Advanced Search | MSN Search Search Builder | Teoma Advanced Search | Yahoo Advanced Web Search Images AllTheWeb | AltaVista | The Amazing Picture Machine | Ditto | Dogpile | Fazzle | Google Image Search | IceRocket | Ixquick | Mamma | Picsearch Language AllTheWeb Advanced Web Search | AOL Advanced Search | Exalead Advanced Search | Google Language Tools | HotBot Advanced Search | iBoogie Advanced Web Search | Lycos Advanced Search | MSN Search Search Builder | Teoma Advanced Search | Yahoo Advanced Web Search Multimedia & video All TheWeb | AltaVista | Dogpile | Fazzle | IceRocket | Singingfish | Yahoo Video Search Page Title/URL AOL Advanced -
Analysis of the Youtube Channel Recommendation Network
CS 224W Project Milestone Analysis of the YouTube Channel Recommendation Network Ian Torres [itorres] Jacob Conrad Trinidad [j3nidad] December 8th, 2015 I. Introduction With over a billion users, YouTube is one of the largest online communities on the world wide web. For a user to upload a video on YouTube, they can create a channel. These channels serve as the home page for that account, displaying the account's name, description, and public videos that have been up- loaded to YouTube. In addition to this content, channels can recommend other channels. This can be done in two ways: the user can choose to feature a channel or YouTube can recommend a channel whose content is similar to the current channel. YouTube features both of these types of recommendations in separate sidebars on the user's channel. We are interested analyzing in the structure of this potential network. We have crawled the YouTube site and obtained a dataset totaling 228575 distinct user channels, 400249 user recommendations, and 400249 YouTube recommendations. In this paper, we present a systematic and in-depth analysis on the structure of this network. With this data, we have created detailed visualizations, analyzed different centrality measures on the network, compared their community structures, and performed motif analysis. II. Literature Review As YouTube has been rising in popularity since its creation in 2005, there has been research on the topic of YouTube and discovering the structure behind its network. Thus, there exists much research analyzing YouTube as a social network. Cheng looks at videos as nodes and recommendations to other videos as links [1]. -
Package 'Rahrefs'
Package ‘RAhrefs’ July 28, 2019 Type Package Title 'Ahrefs' API R Interface Version 0.1.4 Description Enables downloading detailed reports from <https://ahrefs.com> about backlinks from pointing to website, provides authentication with an API key as well as ordering, grouping and filtering functionalities. License MIT + file LICENCE URL https://ahrefs.com/ BugReports https://github.com/Leszek-Sieminski/RAhrefs/issues Depends R (>= 3.4.0) Imports assertthat, httr, jsonlite, testthat NeedsCompilation no Encoding UTF-8 LazyData true RoxygenNote 6.1.1 Author Leszek Sieminski´ [aut, cre], Performance Media Polska sp. z o.o. [cph, fnd] Maintainer Leszek Sieminski´ <[email protected]> Repository CRAN Date/Publication 2019-07-28 08:40:02 UTC R topics documented: ahrefs_metrics . 2 ahrefs_reports . 2 rah_ahrefs_rank . 3 rah_anchors . 5 rah_anchors_refdomains . 8 rah_auth . 11 rah_backlinks . 11 1 2 ahrefs_metrics rah_backlinks_new_lost . 14 rah_backlinks_new_lost_counters . 17 rah_backlinks_one_per_domain . 20 rah_broken_backlinks . 23 rah_broken_links . 26 rah_condition . 29 rah_condition_set . 31 rah_domain_rating . 32 rah_downloader . 34 rah_linked_anchors . 36 rah_linked_domains . 39 rah_linked_domains_by_type . 42 rah_metrics . 45 rah_metrics_extended . 47 rah_pages . 50 rah_pages_extended . 52 rah_pages_info . 55 rah_refdomains . 58 rah_refdomains_by_type . 61 rah_refdomains_new_lost . 64 rah_refdomains_new_lost_counters . 66 rah_refips . 68 rah_subscription_info . 71 ahrefs_metrics Ahrefs metrics Description Description of Ahrefs -
Webpage Ranking Algorithms Second Exam Report
Webpage Ranking Algorithms Second Exam Report Grace Zhao Department of Computer Science Graduate Center, CUNY Exam Committee Professor Xiaowen Zhang, Mentor, College of Staten Island Professor Ted Brown, Queens College Professor Xiangdong Li, New York City College of Technology Initial version: March 8, 2015 Revision: May 1, 2015 1 Abstract The traditional link analysis algorithms exploit the context in- formation inherent in the hyperlink structure of the Web, with the premise being that a link from page A to page B denotes an endorse- ment of the quality of B. The exemplary PageRank algorithm weighs backlinks with a random surfer model; Kleinberg's HITS algorithm promotes the use of hubs and authorities over a base set; Lempel and Moran traverse this structure through their bipartite stochastic algo- rithm; Li examines the structure from head to tail, counting ballots over hypertext. Semantic Web and its inspired technologies bring new core factors into the ranking equation. While making continuous effort to improve the importance and relevancy of search results, Semantic ranking algorithms strive to improve the quality of search results: (1) The meaning of the search query; and (2) The relevancy of the result in relation to user's intention. The survey focuses on an overview of eight selected search ranking algorithms. 2 Contents 1 Introduction 4 2 Background 5 2.1 Core Concepts . 5 2.1.1 Search Engine . 5 2.1.2 Hyperlink Structure . 5 2.1.3 Search Query . 7 2.1.4 Web Graph . 7 2.1.5 Base Set of Webpages . 9 2.1.6 Semantic Web . -
Human Computation Luis Von Ahn
Human Computation Luis von Ahn CMU-CS-05-193 December 7, 2005 School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 Thesis Committee: Manuel Blum, Chair Takeo Kanade Michael Reiter Josh Benaloh, Microsoft Research Jitendra Malik, University of California, Berkeley Copyright © 2005 by Luis von Ahn This work was partially supported by the National Science Foundation (NSF) grants CCR-0122581 and CCR-0085982 (The Aladdin Center), by a Microsoft Research Fellowship, and by generous gifts from Google, Inc. The views and conclusions contained in this document are those of the author and should not be interpreted as representing official policies, either expressed or implied, of any sponsoring institution, the U.S. government or any other entity. Keywords: CAPTCHA, the ESP Game, Peekaboom, Verbosity, Phetch, human computation, automated Turing tests, games with a purpose. 2 Abstract Tasks like image recognition are trivial for humans, but continue to challenge even the most sophisticated computer programs. This thesis introduces a paradigm for utilizing human processing power to solve problems that computers cannot yet solve. Traditional approaches to solving such problems focus on improving software. I advocate a novel approach: constructively channel human brainpower using computer games. For example, the ESP Game, introduced in this thesis, is an enjoyable online game — many people play over 40 hours a week — and when people play, they help label images on the Web with descriptive keywords. These keywords can be used to significantly improve the accuracy of image search. People play the game not because they want to help, but because they enjoy it. I introduce three other examples of “games with a purpose”: Peekaboom, which helps determine the location of objects in images, Phetch, which collects paragraph descriptions of arbitrary images to help accessibility of the Web, and Verbosity, which collects “common-sense” knowledge. -
Instrumentalizing the Sources of Attraction. How Russia Undermines Its Own Soft Power
INSTRUMENTALIZING THE SOURCES OF ATTRACTION. HOW RUSSIA UNDERMINES ITS OWN SOFT POWER By Vasile Rotaru Abstract The 2011-2013 domestic protests and the 2013-2015 Ukraine crisis have brought to the Russian politics forefront an increasing preoccupation for the soft power. The concept started to be used in official discourses and documents and a series of measures have been taken both to avoid the ‘dangers’ of and to streamline Russia’s soft power. This dichotomous approach towards the ‘power of attraction’ have revealed the differences of perception of the soft power by Russian officials and the Western counterparts. The present paper will analyse Russia’s efforts to control and to instrumentalize the sources of soft power, trying to assess the effectiveness of such an approach. Keywords: Russian soft power, Russian foreign policy, public diplomacy, Russian mass media, Russian internet Introduction The use of term soft power is relatively new in the Russian political circles, however, it has become recently increasingly popular among the Russian analysts, policy makers and politicians. The term per se was used for the first time in Russian political discourse in February 2012 by Vladimir Putin. In the presidential election campaign, the then candidate Putin drew attention to the fact that soft power – “a set of tools and methods to achieve foreign policy goals without the use of arms but by exerting information and other levers of influence” is used frequently by “big countries, international blocks or corporations” “to develop and provoke extremist, separatist and nationalistic attitudes, to manipulate the public and to directly interfere in the domestic policy of sovereign countries” (Putin 2012). -
Pagerank Best Practices Neus Ferré Aug08
Search Engine Optimisation. PageRank best Practices Graduand: Supervisor: Neus Ferré Viñes Florian Heinemann [email protected] [email protected] UPC Barcelona RWTH Aachen RWTH Aachen Business Sciences for Engineers Business Sciences for Engineers and Natural Scientists and Natural Scientists July 2008 Abstract Since the explosion of the Internet age the need of search online information has grown as well at the light velocity. As a consequent, new marketing disciplines arise in the digital world. This thesis describes, in the search engine marketing framework, how the ranking in the search engine results page (SERP) can be influenced. Wikipedia describes search engine marketing or SEM as a form of Internet marketing that seeks to promote websites by increasing their visibility in search engine result pages (SERPs). Therefore, the importance of being searchable and visible to the users reveal needs of improvement for the website designers. Different factors are used to produce search rankings. One of them is PageRank. The present thesis focuses on how PageRank of Google makes use of the linking structure of the Web in order to maximise relevance of the results in a web search. PageRank used to be the jigsaw of webmasters because of the secrecy it used to have. The formula that lies behind PageRank enabled the founders of Google to convert a PhD into one of the most successful companies ever. The uniqueness of PageRank in contrast to other Web Search Engines consist in providing the user with the greatest relevance of the results for a specific query, thus providing the most satisfactory user experience.