The Google Dilemma
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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. -
Duckduckgo Search Engines Android
Duckduckgo search engines android Continue 1 5.65.0 10.8MB DuckduckGo Privacy Browser 1 5.64.0 10.8MB DuckduckGo Privacy Browser 1 5.63.1 10.78MB DuckduckGo Privacy Browser 1 5.62.0 10.36MB DuckduckGo Privacy Browser 1 5.61.2 10.36MB DuckduckGo Privacy Browser 1 5.60.0 10.35MB DuckduckGo Privacy Browser 1 5.59.1 10.35MB DuckduckGo Privacy Browser 1 5.58.1 10.33MB DuckduckGo Privacy Browser 1 5.57.1 10.31MB DuckduckGo Privacy browser © DuckduckGo. Privacy, simplified. This article is about the search engine. For children's play, see duck, duck, goose. Internet search engine DuckDuckGoScreenshot home page DuckDuckGo on 2018Type search engine siteWeb Unavailable inMultilingualHeadquarters20 Paoli PikePaoli, Pennsylvania, USA Area servedWorldwideOwnerDuck Duck Go, Inc., createdGabriel WeinbergURLduckduckgo.comAlexa rank 158 (October 2020 update) CommercialRegregedSeptember 25, 2008; 12 years ago (2008-09-25) was an Internet search engine that emphasized the privacy of search engines and avoided the filter bubble of personalized search results. DuckDuckGo differs from other search engines by not profiling its users and showing all users the same search results for this search term. The company is based in Paoli, Pennsylvania, in Greater Philadelphia and has 111 employees since October 2020. The name of the company is a reference to the children's game duck, duck, goose. The results of the DuckDuckGo Survey are a compilation of more than 400 sources, including Yahoo! Search BOSS, Wolfram Alpha, Bing, Yandex, own web scanner (DuckDuckBot) and others. It also uses data from crowdsourcing sites, including Wikipedia, to fill in the knowledge panel boxes to the right of the results. -
Prospects, Leads, and Subscribers
PAGE 2 YOU SHOULD READ THIS eBOOK IF: You are looking for ideas on finding leads. Spider Trainers can help You are looking for ideas on converting leads to Marketing automation has been shown to increase subscribers. qualified leads for businesses by as much as 451%. As You want to improve your deliverability. experts in drip and nurture marketing, Spider Trainers You want to better maintain your lists. is chosen by companies to amplify lead and demand generation while setting standards for design, You want to minimize your list attrition. development, and deployment. Our publications are designed to help you get started, and while we may be guilty of giving too much information, we know that the empowered and informed client is the successful client. We hope this white paper does that for you. We look forward to learning more about your needs. Please contact us at 651 702 3793 or [email protected] . ©2013 SPIDER TRAINERS PAGE 3 TAble Of cOnTenTS HOW TO cAPTure SubScriberS ...............................2 HOW TO uSe PAiD PrOGrAMS TO GAin Tipping point ..................................................................2 SubScriberS ...........................................................29 create e mail lists ...........................................................3 buy lists .........................................................................29 Pop-up forms .........................................................4 rent lists ........................................................................31 negative consent -
Fully Automatic Link Spam Detection∗ Work in Progress
SpamRank – Fully Automatic Link Spam Detection∗ Work in progress András A. Benczúr1,2 Károly Csalogány1,2 Tamás Sarlós1,2 Máté Uher1 1 Computer and Automation Research Institute, Hungarian Academy of Sciences (MTA SZTAKI) 11 Lagymanyosi u., H–1111 Budapest, Hungary 2 Eötvös University, Budapest {benczur, cskaresz, stamas, umate}@ilab.sztaki.hu www.ilab.sztaki.hu/websearch Abstract Spammers intend to increase the PageRank of certain spam pages by creating a large number of links pointing to them. We propose a novel method based on the concept of personalized PageRank that detects pages with an undeserved high PageRank value without the need of any kind of white or blacklists or other means of human intervention. We assume that spammed pages have a biased distribution of pages that contribute to the undeserved high PageRank value. We define SpamRank by penalizing pages that originate a suspicious PageRank share and personalizing PageRank on the penalties. Our method is tested on a 31 M page crawl of the .de domain with a manually classified 1000-page stratified random sample with bias towards large PageRank values. 1 Introduction Identifying and preventing spam was cited as one of the top challenges in web search engines in a 2002 paper [24]. Amit Singhal, principal scientist of Google Inc. estimated that the search engine spam industry had a revenue potential of $4.5 billion in year 2004 if they had been able to completely fool all search engines on all commercially viable queries [36]. Due to the large and ever increasing financial gains resulting from high search engine ratings, it is no wonder that a significant amount of human and machine resources are devoted to artificially inflating the rankings of certain web pages. -
Clique-Attacks Detection in Web Search Engine for Spamdexing Using K-Clique Percolation Technique
International Journal of Machine Learning and Computing, Vol. 2, No. 5, October 2012 Clique-Attacks Detection in Web Search Engine for Spamdexing using K-Clique Percolation Technique S. K. Jayanthi and S. Sasikala, Member, IACSIT Clique cluster groups the set of nodes that are completely Abstract—Search engines make the information retrieval connected to each other. Specifically if connections are added task easier for the users. Highly ranking position in the search between objects in the order of their distance from one engine query results brings great benefits for websites. Some another a cluster if formed when the objects forms a clique. If website owners interpret the link architecture to improve ranks. a web site is considered as a clique, then incoming and To handle the search engine spam problems, especially link farm spam, clique identification in the network structure would outgoing links analysis reveals the cliques existence in web. help a lot. This paper proposes a novel strategy to detect the It means strong interconnection between few websites with spam based on K-Clique Percolation method. Data collected mutual link interchange. It improves all websites rank, which from website and classified with NaiveBayes Classification participates in the clique cluster. In Fig. 2 one particular case algorithm. The suspicious spam sites are analyzed for of link spam, link farm spam is portrayed. That figure points clique-attacks. Observations and findings were given regarding one particular node (website) is pointed by so many nodes the spam. Performance of the system seems to be good in terms of accuracy. (websites), this structure gives higher rank for that website as per the PageRank algorithm. -
La Sécurité Informatique Edition Livres Pour Tous (
La sécurité informatique Edition Livres pour tous (www.livrespourtous.com) PDF générés en utilisant l’atelier en source ouvert « mwlib ». Voir http://code.pediapress.com/ pour plus d’informations. PDF generated at: Sat, 13 Jul 2013 18:26:11 UTC Contenus Articles 1-Principes généraux 1 Sécurité de l'information 1 Sécurité des systèmes d'information 2 Insécurité du système d'information 12 Politique de sécurité du système d'information 17 Vulnérabilité (informatique) 21 Identité numérique (Internet) 24 2-Attaque, fraude, analyse et cryptanalyse 31 2.1-Application 32 Exploit (informatique) 32 Dépassement de tampon 34 Rétroingénierie 40 Shellcode 44 2.2-Réseau 47 Attaque de l'homme du milieu 47 Attaque de Mitnick 50 Attaque par rebond 54 Balayage de port 55 Attaque par déni de service 57 Empoisonnement du cache DNS 66 Pharming 69 Prise d'empreinte de la pile TCP/IP 70 Usurpation d'adresse IP 71 Wardriving 73 2.3-Système 74 Écran bleu de la mort 74 Fork bomb 82 2.4-Mot de passe 85 Attaque par dictionnaire 85 Attaque par force brute 87 2.5-Site web 90 Cross-site scripting 90 Défacement 93 2.6-Spam/Fishing 95 Bombardement Google 95 Fraude 4-1-9 99 Hameçonnage 102 2.7-Cloud Computing 106 Sécurité du cloud 106 3-Logiciel malveillant 114 Logiciel malveillant 114 Virus informatique 120 Ver informatique 125 Cheval de Troie (informatique) 129 Hacktool 131 Logiciel espion 132 Rootkit 134 Porte dérobée 145 Composeur (logiciel) 149 Charge utile 150 Fichier de test Eicar 151 Virus de boot 152 4-Concepts et mécanismes de sécurité 153 Authentification forte -
Holocaust-Denial Literature: a Fourth Bibliography
City University of New York (CUNY) CUNY Academic Works Publications and Research York College 2000 Holocaust-Denial Literature: A Fourth Bibliography John A. Drobnicki CUNY York College How does access to this work benefit ou?y Let us know! More information about this work at: https://academicworks.cuny.edu/yc_pubs/25 Discover additional works at: https://academicworks.cuny.edu This work is made publicly available by the City University of New York (CUNY). Contact: [email protected] Holocaust-Denial Literature: A Fourth Bibliography John A. Drobnicki This bibliography is a supplement to three earlier ones published in the March 1994, Decem- ber 1996, and September 1998 issues of the Bulletin of Bibliography. During the intervening time. Holocaust revisionism has continued to be discussed both in the scholarly literature and in the mainstream press, especially owing to the libel lawsuit filed by David Irving against Deb- orah Lipstadt and Penguin Books. The Holocaust deniers, who prefer to call themselves “revi- sionists” in an attempt to gain scholarly legitimacy, have refused to go away and remain as vocal as ever— Bradley R. Smith has continued to send revisionist advertisements to college newspapers (including free issues of his new publication. The Revisionist), generating public- ity for his cause. Holocaust-denial, which will be used interchangeably with Holocaust revisionism in this bib- liography, is a body of literature that seeks to “prove” that the Jewish Holocaust did not hap- pen. Although individual revisionists may have different motives and beliefs, they all share at least one point: that there was no systematic attempt by Nazi Germany to exterminate Euro- pean Jewry. -
Towards Active SEO 2
JISTEM - Journal of Information Systems and Technology Management Revista de Gestão da Tecnologia e Sistemas de Informação Vol. 9, No. 3, Sept/Dec., 2012, pp. 443-458 ISSN online: 1807-1775 DOI: 10.4301/S1807-17752012000300001 TOWARDS ACTIVE SEO (SEARCH ENGINE OPTIMIZATION) 2.0 Charles-Victor Boutet (im memoriam) Luc Quoniam William Samuel Ravatua Smith South University Toulon-Var - Ingémédia, France ____________________________________________________________________________ ABSTRACT In the age of writable web, new skills and new practices are appearing. In an environment that allows everyone to communicate information globally, internet referencing (or SEO) is a strategic discipline that aims to generate visibility, internet traffic and a maximum exploitation of sites publications. Often misperceived as a fraud, SEO has evolved to be a facilitating tool for anyone who wishes to reference their website with search engines. In this article we show that it is possible to achieve the first rank in search results of keywords that are very competitive. We show methods that are quick, sustainable and legal; while applying the principles of active SEO 2.0. This article also clarifies some working functions of search engines, some advanced referencing techniques (that are completely ethical and legal) and we lay the foundations for an in depth reflection on the qualities and advantages of these techniques. Keywords: Active SEO, Search Engine Optimization, SEO 2.0, search engines 1. INTRODUCTION With the Web 2.0 era and writable internet, opportunities related to internet referencing have increased; everyone can easily create their virtual territory consisting of one to thousands of sites, and practically all 2.0 territories are designed to be participatory so anyone can write and promote their sites. -
Antisemitism 2.0”—The Spreading of Jew-Hatredonthe World Wide Web
MonikaSchwarz-Friesel “Antisemitism 2.0”—The Spreading of Jew-hatredonthe World Wide Web This article focuses on the rising problem of internet antisemitism and online ha- tred against Israel. Antisemitism 2.0isfound on all webplatforms, not justin right-wing social media but alsoonthe online commentary sections of quality media and on everydayweb pages. The internet shows Jew‐hatred in all its var- ious contemporary forms, from overt death threats to more subtle manifestations articulated as indirect speech acts. The spreading of antisemitic texts and pic- tures on all accessibleaswell as seemingly non-radical platforms, their rapid and multiple distribution on the World Wide Web, adiscourse domain less con- trolled than other media, is by now acommon phenomenon within the spaceof public online communication. As aresult,the increasingimportance of Web2.0 communication makes antisemitism generallymore acceptable in mainstream discourse and leadstoanormalization of anti-Jewishutterances. Empirical results from alongitudinalcorpus studyare presented and dis- cussed in this article. They show how centuries old anti-Jewish stereotypes are persistentlyreproducedacross different social strata. The data confirm that hate speech against Jews on online platforms follows the pattern of classical an- tisemitism. Although manyofthem are camouflaged as “criticism of Israel,” they are rooted in the ancient and medieval stereotypes and mental models of Jew hostility.Thus, the “Israelization of antisemitism,”¹ the most dominant manifes- tation of Judeophobia today, proves to be merelyanew garb for the age-old Jew hatred. However,the easy accessibility and the omnipresenceofantisemitism on the web 2.0enhancesand intensifies the spreadingofJew-hatred, and its prop- agation on social media leads to anormalization of antisemitic communication, thinking,and feeling. -
Understanding and Combating Link Farming in the Twitter Social Network
Understanding and Combating Link Farming in the Twitter Social Network Saptarshi Ghosh Bimal Viswanath Farshad Kooti IIT Kharagpur, India MPI-SWS, Germany MPI-SWS, Germany Naveen K. Sharma Gautam Korlam Fabricio Benevenuto IIT Kharagpur, India IIT Kharagpur, India UFOP, Brazil Niloy Ganguly Krishna P. Gummadi IIT Kharagpur, India MPI-SWS, Germany ABSTRACT Web, such as current events, news stories, and people’s opin- Recently, Twitter has emerged as a popular platform for ion about them. Traditional media, celebrities, and mar- discovering real-time information on the Web, such as news keters are increasingly using Twitter to directly reach au- stories and people’s reaction to them. Like the Web, Twitter diences in the millions. Furthermore, millions of individual has become a target for link farming, where users, especially users are sharing the information they discover over Twit- spammers, try to acquire large numbers of follower links in ter, making it an important source of breaking news during the social network. Acquiring followers not only increases emergencies like revolutions and disasters [17, 23]. Recent the size of a user’s direct audience, but also contributes to estimates suggest that 200 million active Twitter users post the perceived influence of the user, which in turn impacts 150 million tweets (messages) containing more than 23 mil- the ranking of the user’s tweets by search engines. lion URLs (links to web pages) daily [3,28]. In this paper, we first investigate link farming in the Twit- As the information shared over Twitter grows rapidly, ter network and then explore mechanisms to discourage the search is increasingly being used to find interesting trending activity. -
Lecture 18: CS 5306 / INFO 5306: Crowdsourcing and Human Computation Web Link Analysis (Wisdom of the Crowds) (Not Discussing)
Lecture 18: CS 5306 / INFO 5306: Crowdsourcing and Human Computation Web Link Analysis (Wisdom of the Crowds) (Not Discussing) • Information retrieval (term weighting, vector space representation, inverted indexing, etc.) • Efficient web crawling • Efficient real-time retrieval Web Search: Prehistory • Crawl the Web, generate an index of all pages – Which pages? – What content of each page? – (Not discussing this) • Rank documents: – Based on the text content of a page • How many times does query appear? • How high up in page? – Based on display characteristics of the query • For example, is it in a heading, italicized, etc. Link Analysis: Prehistory • L. Katz. "A new status index derived from sociometric analysis“, Psychometrika 18(1), 39-43, March 1953. • Charles H. Hubbell. "An Input-Output Approach to Clique Identification“, Sociolmetry, 28, 377-399, 1965. • Eugene Garfield. Citation analysis as a tool in journal evaluation. Science 178, 1972. • G. Pinski and Francis Narin. "Citation influence for journal aggregates of scientific publications: Theory, with application to the literature of physics“, Information Processing and Management. 12, 1976. • Mark, D. M., "Network models in geomorphology," Modeling in Geomorphologic Systems, 1988 • T. Bray, “Measuring the Web”. Proceedings of the 5th Intl. WWW Conference, 1996. • Massimo Marchiori, “The quest for correct information on the Web: hyper search engines”, Computer Networks and ISDN Systems, 29: 8-13, September 1997, Pages 1225-1235. Hubs and Authorities • J. Kleinberg. “Authoritative -
Lelov: Cultural Memory and a Jewish Town in Poland. Investigating the Identity and History of an Ultra - Orthodox Society
Lelov: cultural memory and a Jewish town in Poland. Investigating the identity and history of an ultra - orthodox society. Item Type Thesis Authors Morawska, Lucja Rights <a rel="license" href="http://creativecommons.org/licenses/ by-nc-nd/3.0/"><img alt="Creative Commons License" style="border-width:0" src="http://i.creativecommons.org/l/by- nc-nd/3.0/88x31.png" /></a><br />The University of Bradford theses are licenced under a <a rel="license" href="http:// creativecommons.org/licenses/by-nc-nd/3.0/">Creative Commons Licence</a>. Download date 03/10/2021 19:09:39 Link to Item http://hdl.handle.net/10454/7827 University of Bradford eThesis This thesis is hosted in Bradford Scholars – The University of Bradford Open Access repository. Visit the repository for full metadata or to contact the repository team © University of Bradford. This work is licenced for reuse under a Creative Commons Licence. Lelov: cultural memory and a Jewish town in Poland. Investigating the identity and history of an ultra - orthodox society. Lucja MORAWSKA Submitted in accordance with the requirements for the degree of Doctor of Philosophy School of Social and International Studies University of Bradford 2012 i Lucja Morawska Lelov: cultural memory and a Jewish town in Poland. Investigating the identity and history of an ultra - orthodox society. Key words: Chasidism, Jewish History in Eastern Europe, Biederman family, Chasidic pilgrimage, Poland, Lelov Abstract. Lelov, an otherwise quiet village about fifty miles south of Cracow (Poland), is where Rebbe Dovid (David) Biederman founder of the Lelov ultra-orthodox (Chasidic) Jewish group, - is buried.