The SAGE Handbook of Web History by Niels Brügger, Ian
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Oracle Eloqua Landing Pages User Guide
Oracle Eloqua Landing Pages User Guide ©2018 Oracle Corporation. All rights reserved 07-Nov-2018 Contents 1 Landing pages overview 4 2 Landing page examples 7 3 Creating landing pages using the Design Editor 17 3.1 Working with landing page content blocks and layouts 20 3.1.1 Copying content blocks or layouts 24 3.2 Landing page styling 27 3.2.1 Background 29 3.2.2 Text Defaults 29 3.2.3 Hyperlink Defaults 29 3.2.4 Advanced Styles 29 3.3 Adding an image carousel 30 3.4 Adding a form 32 3.5 Changing the visibility of landing pages 33 3.6 Previewing landing pages 35 3.7 Creating folders for landing pages 38 3.8 Saving landing pages as templates 40 3.9 Editing landing pages 43 3.10 Making copies of landing pages 44 3.11 Deleting landing pages 47 4 Creating landing pages using the Source Editor 49 4.1 Code requirements for uploading HTML landing pages 53 4.2 Editing HTML landing pages using the Source Editor 55 4.3 Creating new landing pages and templates using the HTML upload wizard 57 5 Creating landing pages using the Classic Design Editor 63 5.1 Adding text boxes to landing pages 68 ©2018 Oracle Corporation. All rights reserved 2 of 102 5.2 Customizing landing page text boxes and images 70 5.3 Locking and unlocking objects in landing pages 83 5.4 Copying landing page objects 87 5.5 Grouping objects in landing pages 89 5.6 Using landing page recovery checkpoints 91 6 Landing page template manager 95 6.1 Granting template manager permission 95 6.2 Creating new landing page templates from the template manager 96 6.3 Editing landing page templates 98 6.4 Adding protections in landing page templates 99 6.4.1 Protected HTML landing page reference 102 ©2018 Oracle Corporation. -
What Are Facebook Messenger Ads?
1 TABLE OF CONTENTS INTRODUCTION CHAPTER 1: What is Facebook Messenger? CHAPTER 2: What are Facebook Messenger Ads? CHAPTER 3: What are the Features That Facebook Messenger Ads Offer CHAPTER 4: Three Ways to Get Facebook Messenger Ads to Bolster Revenue Growth CHAPTER 5: What is Chatbot Marketing? CONCLUSION 2 The Guide to Facebook Messenger Ads INTRODUCTION We all agree that instant messaging is simpler, more real-time and prompt. It is these attributes that lie at the bottom of the phenomenal success of apps like WhatsApp, Snap Chat, Instagram Messaging – all of which are now being increasingly used by businesses to promote their products and services. Facebook Messenger ads for eCommerce is one such channel which is being used by online businesses to interact with their prospective and current customers. With this eBook, you’ll learn how Facebook Messenger Ads are able to power your sales strategy, how you can get started with Facebook Messenger Ads and see great examples of popular brands using Facebook Messenger Ads. We will talk about Chatbot Marketing too. 3 CHAPTER 1: WHAT IS FACEBOOK MESSENGER? The Facebook Messenger is an instant messaging service by Facebook. Launched in 2011, this app can be used alongside your Facebook account on your computers, tabs or phones. While on the computer, you will see the Messenger integrated with your Facebook page. The same functionality is used through a separate app on your hand-held devices, which brings me to the next interesting fact; and that is, you don’t need a Facebook account to use its Messenger app. -
Partner in Business
PENTELEDATA’S CUSTOMER NEWSLETTER CONTENTS PARTNER IN BUSINESS OUR PARTNER IN BUSINESS - FIRST First Commonwealth Federal COMMONWEALTH FEDERAL CREDIT UNION Credit Union PenTeleData is proud of our partner- ship with First Commonwealth. First Commonwealth is the largest credit union in the Lehigh Valley, with over $550 million in LETTER FROM OUR GM assets, nearly 50,000 members and six branches. They offer the same financial services UPCOMING EVENTS found at a traditional bank, but with better FLASHBACK JUST 25 YEARS AGO...IT’S rates and lower fees. That's because they’re ALL BECAUSE OF OUR FIBER! structured differently. They are member- TECH TIP: owned and not-for-profit. Instead of earning What to do if your Cable Modem money for stockholders, they return profits to or DSL Stops Working? their member-owners (account holders) in the form of higher dividends on savings, lower DO YOU HEAR THE SONIC BOOM? rates on loans and lower fees. First Common- DOCSIS 3.0 packages for Business wealth was originally chartered in 1959 to begin this summer. An upgrade to their data processing sys- CUSTOMER CONTEST serve the employees of Western Electric in Al- tem will allow them to better serve their customers, with fully lentown. Today, they serve nearly 700 employer integrated accounts and streamlined processes. The more APRIL 2013 CUSTOMER CONTEST groups – ranging from large corporations to advanced technology will help to serve their members WINNER very small businesses. Their full-service menu quickly and efficiently with options such as mobile banking, includes everything from checking accounts OUR NEW RESIDENTIAL WEBSITE redesigned statements, account alerts via text messaging, and debit cards to mortgages, online banking FEATURES SOME VERY FRIENDLY FACES! and a customized landing page for account log-in. -
How to Promote Your How to Promote Your Business Blog Using Business
How to Promote Your Business Blog Using HubSpot Prashant Kaw Ellie Mirman Inbound Marketing Manager Inbound Marketing Manager Twitter: @prashantkaw Twitter: @ellieeille Promoting Your Business Blog Build thought leadership Optimize content for search Share your remarkable content Interact with your community Create business opportunities Q&A Who is HubSpot? • Founded: 2006 1,800+ Customers • Team: 120 (20 MIT) • A: $5m General Catalyst • B: $12m Matrix Partners • C: $16m Scale Ventures • Outside Director: GilGail GdGoodman, CEO Constant Contact (CTCT) Outbound Marketing Outbound Marketing is Broken 800-555-1234 Annoying Salesperson Marketing Has Changed 1950 - 2000 2000 - 2050 The Good News Build Thought LdLeaders hip Content > Coverage > Traffic/Leads What to Publish • Blog • Podcast • Videos • Phooostos • Presentations • eBooks • News Releases Create Content for SEO Organic Search is Better Pay Per Click – 25% of Clicks Organic Results 75% of Clicks Source: Marketing Sherpa and Enquiro Research Build a Long Lasting Asset Flickr photo: Thomas Hawk Pick Your Keyword Battles Optimize Every Blog Post Share Your CttContent Social Media = Cocktail Party Flickr: TECHcocktail Publish Content to Social Media Publish Content to Social Media Encourage Sharing of Content Interact with CitCommunity Host Conversations Answer Questions Comment On Blogs Move Beyond Your Blog Monitor & Engage in Conversations Create Business OtitiOpportunities Convert Conversations Targeted calls to action at every step Convert Conversations Capture leads with ldilanding -
Identity Governance 3.6 User and Administration Guide
Identity Governance 3.6 User and Administration Guide April 2020 Legal Notice The only warranties for products and services of Micro Focus and its affiliates and licensors (“Micro Focus”) are set forth in the express warranty statements accompanying such products and services. Nothing herein should be construed as constituting an additional warranty. Micro Focus shall not be liable for technical or editorial errors or omissions contained herein. The information contained herein is subject to change without notice. For additional information, such as certification-related notices and trademarks, see http://www.microfocus.com/about/ legal/. © Copyright 2020 Micro Focus or one of its affiliates. 2 Contents About this Book and the Library 11 1 Introduction 13 1.1 Understanding Installation and Configuration . .14 1.2 Understanding Key Administration and User Tasks . .14 1.3 Understanding Reporting . .14 1.4 Understanding Licenses . .15 1.5 Understanding REST Services for Identity Governance . .15 2 Adding Identity Governance Users and Assigning Authorizations 17 2.1 Understanding Authorizations in Identity Governance . .17 2.1.1 Global Authorizations. .17 2.1.2 Runtime Authorizations . .20 2.2 Adding Identity Governance Users. .22 2.3 Assigning Authorizations to Identity Governance Users . .23 2.4 Using Coverage Maps . .24 2.4.1 Creating Coverage Map . .25 2.4.2 Loading the Coverage Map . .29 3 Customizing Identity Governance for Your Enterprise 31 3.1 Customizing the Email Notification Templates . .31 3.1.1 Modifying Email Templates . .32 3.1.2 Adding an Image to the Email Template. .37 3.2 Customizing the Collector Templates for Data Sources . 38 3.3 Customizing Categories. -
How Content Volume on Landing Pages Influences Consumer Behavior
June 23 - 28 2018, La Verne, California, USA HOW CONTENT VOLUME ON LANDING PAGES INFLUENCES CONSUMER BEHAVIOR: EMPIRICAL EVIDENCE Ruti Gafni* The Academic College of Tel Aviv Yaffo, [email protected] Tel Aviv, Israel Nim Dvir University at Albany, State University of New York, [email protected] Albany, NY, USA * Corresponding author ABSTRACT Aim/Purpose This paper describes an empirical investigation on how consumer behavior is influenced by the volume of content on a commercial landing page -- a stand- alone web page designed to collect user data (in this case the user’s e-mail ad- dress), a behavior called “conversion.” Background Content is a term commonly used to describe the information made available by a website or other electronic medium. A pertinent debate among scholars and practitioners relate to information volume and consumer behavior: do more details elicit engagement and compliance, operationalized through conver- sions, or the other way around? Methodology A pilot study (n= 535) was conducted in real-world commercial setting, fol- lowed by a series of large-scale online experiments (n= 27,083). Both studies employed a between-group design: Two variations of landing pages, long and short, were created based on various behavioral theories. User traffic to the pages was generated using online advertising and randomized between the pag- es (A/B testing). Contribution This research contributes to the body of knowledge on the antecedents and outcomes of online commercial interaction, focusing on content as a determi- nant of consumer decision-making and behavior. Accepting Editor: Eli Cohen │ Received: December 2, 2017 │ Revised: February 28, 2018 │ Accepted: March 3, 2018. -
Internal Labor Markets and Manpower Analysis
DOCUMENT RESUME ED 048 457 VT 012 300 AUTHOR Doeringer, Peter B.; Piore, Michael J. TITLE Internal Labor Markets and Manpower Analysis. INSTITUTION Harvard Univ., Cambridge, Mass.; Massachusetts Inst. of Tech., Cambridge. SPONS AGENCY Manpower Administration (DOL), Washington, D.C. Office of Manpower Research. PUB DATE May 70 NOTE 344p. EDRS PRICE EDRS Price MF-$0.65 HC-$13.16 DESCRIPTORS *Business, Disadvantaged Groups, *Economic Research, Employment Opportunities, *Labor Economics, *Labor Market, Models, *Unemployment ABSTRACT Using data gathered in a series of interviews with management and union officials in over 75 companies between 1964 and 1969, this report analyzes the concept of the internal labor market and describes its relevance for federal manpower policy. The management interviews, which were mostly in personnel, industrial engineering, and operations areas of manufacturing companies, were supplemented by data on the disadvantaged provided by civil rights, poverty, and manpower agencies. The report utilizes the framework established in the study to show that the internal market does not imply inefficiency and may represent an improvement in dealing with structural unemployment. (BH) .4 /.. .118-1`;003 INTERNAL LABOR MARKETS AND MANPOWER ANALYSIS PETER B. DOERINGER Assistant Professor of Economics Harvard University MICHAEL J. PIORE Associate Profe s sor of Economics Massachusetts Institute of Technology ',Jay 1970 This research was preparedunder a contract with the Office of ManpowerResearch, U.S. Departmentof Labor, under the authorityof the Manpower Development and Training Act.Researchers undertakingsuch projects under the Government sponsorshipare encouraged to express their own judgementfreely.Therefore, points of view or opinions stated inthis document do not necessarily represent theofficial position of the Department of Labor. -
Idioms-And-Expressions.Pdf
Idioms and Expressions by David Holmes A method for learning and remembering idioms and expressions I wrote this model as a teaching device during the time I was working in Bangkok, Thai- land, as a legal editor and language consultant, with one of the Big Four Legal and Tax companies, KPMG (during my afternoon job) after teaching at the university. When I had no legal documents to edit and no individual advising to do (which was quite frequently) I would sit at my desk, (like some old character out of a Charles Dickens’ novel) and prepare language materials to be used for helping professionals who had learned English as a second language—for even up to fifteen years in school—but who were still unable to follow a movie in English, understand the World News on TV, or converse in a colloquial style, because they’d never had a chance to hear and learn com- mon, everyday expressions such as, “It’s a done deal!” or “Drop whatever you’re doing.” Because misunderstandings of such idioms and expressions frequently caused miscom- munication between our management teams and foreign clients, I was asked to try to as- sist. I am happy to be able to share the materials that follow, such as they are, in the hope that they may be of some use and benefit to others. The simple teaching device I used was three-fold: 1. Make a note of an idiom/expression 2. Define and explain it in understandable words (including synonyms.) 3. Give at least three sample sentences to illustrate how the expression is used in context. -
A Data Citation Roadmap for Scholarly Data Repositories
A Data Citation Roadmap for Scholarly Data Repositories Tim Clark (Harvard Medical School & Massachusetts General Hospital) Martin Fenner (DataCite) Mercè Crosas (Institute for Quantiative Social Science, Harvard University) DataCite Webinar, February 23, 2017 © 2017 Massachusetts General Hospital • NIH-funded BD2K program bioCADDIE for data discovery8 2014 Joint Declaration of Data Citation Principles JDDCP endorsed by over 100 scholarly organizations http://force11.org/datacitation 2015 Direct deposition and citation of primary research data http://doi.org/10.7717/peerj-cs.1 2016 Data Citation Implementation Pilot Participants And you! Role-based Participants • Publishers • Elsevier, Springer Nature, PLOS, eLife, Wiley, Frontiers, etc. … • Data Repositories • EMBL, Dataverse, Dryad, Figshare, Google, etc. • Informaticians (NIH BD2K, EBI, CDL, etc. ) • … & Authors (YOU) Publishers Roadmap Development! " Leads: Amye Kenall & Helena Cousijn! " Participants: Elsevier, SpringerNature, eLife, Elsevier PLoS, Frontiers, Wyley, et al.! " Workshop July 22 @ SpringerNature London campus, partially funded by NPG. ! SpringerNature " Continuing work via Telcons.! 11 Updang footer To update the footer: • Click into the text box on the slide master page and update the Data cita%on at Springer Nature journals – key events informaon Checking updates • The text updates will not flow through to all the slide layouts as some have been placed manually. • 1998 – : Accession codes required for various data types at Nature journals and Therefore you may need -
Uncovering Social Network Sybils in the Wild
Uncovering Social Network Sybils in the Wild ZHI YANG, Peking University 2 CHRISTO WILSON, University of California, Santa Barbara XIAO WANG, Peking University TINGTING GAO,RenrenInc. BEN Y. ZHAO, University of California, Santa Barbara YAFEI DAI, Peking University Sybil accounts are fake identities created to unfairly increase the power or resources of a single malicious user. Researchers have long known about the existence of Sybil accounts in online communities such as file-sharing systems, but they have not been able to perform large-scale measurements to detect them or measure their activities. In this article, we describe our efforts to detect, characterize, and understand Sybil account activity in the Renren Online Social Network (OSN). We use ground truth provided by Renren Inc. to build measurement-based Sybil detectors and deploy them on Renren to detect more than 100,000 Sybil accounts. Using our full dataset of 650,000 Sybils, we examine several aspects of Sybil behavior. First, we study their link creation behavior and find that contrary to prior conjecture, Sybils in OSNs do not form tight-knit communities. Next, we examine the fine-grained behaviors of Sybils on Renren using clickstream data. Third, we investigate behind-the-scenes collusion between large groups of Sybils. Our results reveal that Sybils with no explicit social ties still act in concert to launch attacks. Finally, we investigate enhanced techniques to identify stealthy Sybils. In summary, our study advances the understanding of Sybil behavior on OSNs and shows that Sybils can effectively avoid existing community-based Sybil detectors. We hope that our results will foster new research on Sybil detection that is based on novel types of Sybil features. -
Svms for the Blogosphere: Blog Identification and Splog Detection
SVMs for the Blogosphere: Blog Identification and Splog Detection Pranam Kolari∗, Tim Finin and Anupam Joshi University of Maryland, Baltimore County Baltimore MD {kolari1, finin, joshi}@umbc.edu Abstract Most blog search engines identify blogs and index con- tent based on update pings received from ping servers1 or Weblogs, or blogs have become an important new way directly from blogs, or through crawling blog directories to publish information, engage in discussions and form communities. The increasing popularity of blogs has and blog hosting services. To increase their coverage, blog given rise to search and analysis engines focusing on the search engines continue to crawl the Web to discover, iden- “blogosphere”. A key requirement of such systems is to tify and index blogs. This enables staying ahead of compe- identify blogs as they crawl the Web. While this ensures tition in a domain where “size does matter”. Even if a web that only blogs are indexed, blog search engines are also crawl is inessential for blog search engines, it is still pos- often overwhelmed by spam blogs (splogs). Splogs not sible that processed update pings are from non-blogs. This only incur computational overheads but also reduce user requires that the source of the pings need to be verified as a satisfaction. In this paper we first describe experimental blog prior to indexing content.2 results of blog identification using Support Vector Ma- In the first part of this paper we address blog identifica- chines (SVM). We compare results of using different feature sets and introduce new features for blog iden- tion by experimenting with different feature sets. -
By Nilesh Bansal a Thesis Submitted in Conformity with the Requirements
ONLINE ANALYSIS OF HIGH-VOLUME SOCIAL TEXT STEAMS by Nilesh Bansal A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy Graduate Department of Computer Science University of Toronto ⃝c Copyright 2013 by Nilesh Bansal Abstract Online Analysis of High-Volume Social Text Steams Nilesh Bansal Doctor of Philosophy Graduate Department of Computer Science University of Toronto 2013 Social media is one of the most disruptive developments of the past decade. The impact of this information revolution has been fundamental on our society. Information dissemination has never been cheaper and users are increasingly connected with each other. The line between content producers and consumers is blurred, leaving us with abundance of data produced in real-time by users around the world on multitude of topics. In this thesis we study techniques to aid an analyst in uncovering insights from this new media form which is modeled as a high volume social text stream. The aim is to develop practical algorithms with focus on the ability to scale, amenability to reliable operation, usability, and ease of implementation. Our work lies at the intersection of building large scale real world systems and developing theoretical foundation to support the same. We identify three key predicates to enable online methods for analysis of social data, namely : • Persistent Chatter Discovery to explore topics discussed over a period of time, • Cross-referencing Media Sources to initiate analysis using a document as the query, and • Contributor Understanding to create aggregate expertise and topic summaries of authors contributing online. The thesis defines each of the predicates in detail and covers proposed techniques, their practical applicability, and detailed experimental results to establish accuracy and scalability for each of the three predicates.