#Metadata optimization

[daivignanmarketingservices]

Next-Level SEO: Mastering Generative Engine Optimization Techniques

Next-Level SEO: Mastering Generative Engine Optimization Techniques

Generative Engine Optimization (GEO) ExplainedDefinition:Step-by-Step Process for Generative Engine Optimization (GEO)1. Understand Your Audience and Goals2. Keyword Research and Integration3. Content Generation with AI Tools4. Content Quality Assurance5. Metadata Optimization6. Incorporate Multimedia Elements7. User Experience (UX) Optimization8. Content Relevance and Engagement9. Natural…

[bkthemes]

Is It Possible to Use Alt Text to Gain Backlinks?

Introduction Alt text, or alternative text, is primarily used to describe images for accessibility and SEO purposes. While it helps visually impaired users understand image content, it also plays a crucial role in search engine indexing. Many marketers wonder whether alt text can be leveraged to gain backlinks, and while alt text itself doesn’t create direct backlinks, it can indirectly…

[tandem-interactive]

Best Practice for Title Tags

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[ourjobagency]

Salesforce customization is not merely an option, it's a necessity in today's rapidly changing business landscape.

To Know More Visit Us:

[askmediainflame]

Meet the Futuristic YouTube Ai Tools That Will Make Your Content Viral.-askmediainflame

In a world where every second, 300 hours of video content is uploaded to YouTube, how can you make your videos stand out from the crowd? Enter the futuristic YouTube AI tools that are revolutionizing the way we create and promote content. From analyzing viewer preferences to generating attention-grabbing thumbnails, these cutting-edge technologies are like a secret weapon in the battle for viral success.For More Information Visit Link- https://www.askmediainflame.com/wp-admin/post.php?post=127&action=edit

[powerfulaitools]

The Connection Between Keyword Intent and On-page Optimization

Introduction Are you ready to unlock the true potential of your website’s visibility and organic traffic? In the vast realm of search engine optimization (SEO), keywords reign supreme. Understanding the intricate connection between keyword intent and on-page optimization is the key to mastering the art of SEO and propelling your website to the top of search engine result pages. As a digital…

[jstor]

I saw something about generative AI on JSTOR. Can you confirm whether you really are implementing it and explain why? I’m pretty sure most of your userbase hates AI.

A generative AI/machine learning research tool on JSTOR is currently in beta, meaning that it's not fully integrated into the platform. This is an opportunity to determine how this technology may be helpful in parsing through dense academic texts to make them more accessible and gauge their relevancy.

To JSTOR, this is primarily a learning experience. We're looking at how beta users are engaging with the tool and the results that the tool is producing to get a sense of its place in academia.

In order to understand what we're doing a bit more, it may help to take a look at what the tool actually does. From a recent blog post:

Content evaluation

Problem: Traditionally, researchers rely on metadata, abstracts, and the first few pages of an article to evaluate its relevance to their work. In humanities and social sciences scholarship, which makes up the majority of JSTOR’s content, many items lack abstracts, meaning scholars in these areas (who in turn are our core cohort of users) have one less option for efficient evaluation. 

When using a traditional keyword search in a scholarly database, a query might return thousands of articles that a user needs significant time and considerable skill to wade through, simply to ascertain which might in fact be relevant to what they’re looking for, before beginning their search in earnest.

Solution: We’ve introduced two capabilities to help make evaluation more efficient, with the aim of opening the researcher’s time for deeper reading and analysis:

Summarize, which appears in the tool interface as “What is this text about,” provides users with concise descriptions of key document points. On the back-end, we’ve optimized the Large Language Model (LLM) prompt for a concise but thorough response, taking on the task of prompt engineering for the user by providing advanced direction to:

Extract the background, purpose, and motivations of the text provided.

Capture the intent of the author without drawing conclusions.

Limit the response to a short paragraph to provide the most important ideas presented in the text.

Search term context is automatically generated as soon as a user opens a text from search results, and provides information on how that text relates to the search terms the user has used. Whereas the summary allows the user to quickly assess what the item is about, this feature takes evaluation to the next level by automatically telling the user how the item is related to their search query, streamlining the evaluation process.

Discovering new paths for exploration

Problem: Once a researcher has discovered content of value to their work, it’s not always easy to know where to go from there. While JSTOR provides some resources, including a “Cited by” list as well as related texts and images, these pathways are limited in scope and not available for all texts. Especially for novice researchers, or those just getting started on a new project or exploring a novel area of literature, it can be needlessly difficult and frustrating to gain traction. 

Solution: Two capabilities make further exploration less cumbersome, paving a smoother path for researchers to follow a line of inquiry:

Recommended topics are designed to assist users, particularly those who may be less familiar with certain concepts, by helping them identify additional search terms or refine and narrow their existing searches. This feature generates a list of up to 10 potential related search queries based on the document’s content. Researchers can simply click to run these searches.

Related content empowers users in two significant ways. First, it aids in quickly assessing the relevance of the current item by presenting a list of up to 10 conceptually similar items on JSTOR. This allows users to gauge the document’s helpfulness based on its relation to other relevant content. Second, this feature provides a pathway to more content, especially materials that may not have surfaced in the initial search. By generating a list of related items, complete with metadata and direct links, users can extend their research journey, uncovering additional sources that align with their interests and questions.

Supporting comprehension

Problem: You think you have found something that could be helpful for your work. It’s time to settle in and read the full document… working through the details, making sure they make sense, figuring out how they fit into your thesis, etc. This all takes time and can be tedious, especially when working through many items. 

Solution: To help ensure that users find high quality items, the tool incorporates a conversational element that allows users to query specific points of interest. This functionality, reminiscent of CTRL+F but for concepts, offers a quicker alternative to reading through lengthy documents. 

By asking questions that can be answered by the text, users receive responses only if the information is present. The conversational interface adds an accessibility layer as well, making the tool more user-friendly and tailored to the diverse needs of the JSTOR user community.

Credibility and source transparency

We knew that, for an AI-powered tool to truly address user problems, it would need to be held to extremely high standards of credibility and transparency. On the credibility side, JSTOR’s AI tool uses only the content of the item being viewed to generate answers to questions, effectively reducing hallucinations and misinformation. 

On the transparency front, responses include inline references that highlight the specific snippet of text used, along with a link to the source page. This makes it clear to the user where the response came from (and that it is a credible source) and also helps them find the most relevant parts of the text. 

[azspot]

But the minute Google became dominant in search, everyone started manipulating links and metadata and everything else to get Google to send them more traffic. Links were no longer organic, no longer endorsements, but attempts to manipulate the algo. The more that was true, the more it became necessary to engage in “search engine optimization”, and the more algorithmic search engines sucked. Of course, Google also self-sabotaged, by trying to optimize search results so that Google would make the most money possible.

AI Will Degenerate In Much The Same Way Google Did

[namesnamesandmorenames]

So I’ve been messing with (improving) the formulas in my fanfiction database as a procrastination method for schoolwork (don’t recommend) but I think that they’re all officially working in the way I want them to? Crazy anyway so I'm going to celebrate by showing a few examples of how my Summarized Metadata formula works

So, if you caught my last two posts about my database you’ll know I track a lot of information and it was mostly done in table format like a big spreadsheet- which is fine but not exactly optimized for using it on your phone. Since then I added some buttons so I could automatically update multiple pieces of information with a click and I wanted to change the way I view information so I converted things into a gallery view and have been trying to figure out how best to present the information I want at a glance including if/then statements for information I wouldn't want if something else wasn't true idk it seems like a lot but it is what I want to be able to refer to at a glance and things link to other databases if they have the little diagonal arrow (I technically could have just had all of the properties I wanted visible but that would have meant things irrelevant to specific records would be visible too and notion doesn't have the ability for properties to be displayed horizontally so there would also be more white space than I want with numbers displayed in a line without indicators—)

Btw those previous posts are what happen when I hyperfixate on my information organization (pre-cataloging) course and this is what happens when I jump off the deep end after my programming course is too basic but I started hyperfixating on it anyway!

Anyway here is what the Summarized Metadata looks like (these also double as fic-recs if you happen to be in the 9-1-1 fandom!)

@cal-daisies-and-briars and @schrijverr are both really amazing creators in the fandom BTW so if you haven't read any of either of their work I can genuinely say I recommend all of it!

And I’ll show you one more fic I just finished and highly highly recommend because I was following it and the creator just completed it! @memequeme

Time for sleep now!

[mariacallous]

Analyzing 35 years of academic biomedical research funded by the National Institutes of Health (NIH), Pierre Azoulay of MIT and Wesley Greenblatt of Harvard find that peer-review penalizes risk-taking in the awarding of grants. Using citations and metadata from publications acknowledging NIH support, the authors construct four measures of risk-taking—variance in the influence of publications, tendency to challenge existing literature, dissimilarity from an investigator’s prior work, and uniqueness among contemporary research—to determine the effect of risk-taking on the likelihood of grant renewal. Distinguishing between risk-taking and novelty of research, the analysis shows that while more novel research is more likely to receive grant renewals, riskier research by any measure is less likely. Among research that does win renewed funding, however, risk-taking projects tend to be more influential, suggesting that the risk penalty may be greater than is socially optimal.

[megumi-fm]

this week on megumi.fm ▸ less worky more ache-y :(

aka essentially part2 of me just sitting at home and rotting away because my ankle will literally not let me do anything rip

📋 Tasks

💻 Internship mainly file redownloads and scripts for file counts, grouping and verification ↳ code to check for the redundancy in repository ✅ ↳ code to extract repository metadata ✅ ↳ code optimization ✅ ↳ learning about Superfamily tool and how to use it✅ ↳ weekly lab presentation ✅ 📝 so after a lot of edits and changes, the manuscript for our final project got accepted in Springer! so that's pretty cool

📅 Daily-s

🛌 consistent sleep [7/7] 💧 good water intake [7/7] 👟 exercise [/7] yet more ankle recovery

Fun Stuff this week

🎨 Updated my tumblr desktop themes hehe 📖 Reread Red White and Royal Blue 💗 grandma stayed over this week! I got to spend some time with her :)) then my ankle got better by the end of the week so we went to my uncle's place for lunch 📺 ongoing: Marry my Husband, Cherry Magic Th, Doctor Slump, Flex X Cop 📺 rewatch: Cherry Magic JP

📻 This week's soundtrack

Space Station No.8 by Jeff Satur -> Fade [TH/CN/EN] -> Lucid [TH/EN]

---

[Feb 5 to Feb 11 ; week 6/52 || i think all this sitting at home is getting to me. i'm losing motivation to do tasks, and my productivity is really plummeting. most of the time is spent waiting for code to run rather than actually doing anything, and my ankle has been making it's presence known so I just feel like staying in bed all day. just one more week at home. just. one more week of holding on ]

[historysurvivalguide]

Search without AI-Overview

AI-overviews and suggested answers have made research very annoying lately. Very often the prompted answers are just wrong, even at a casual glance of the article they are stealing from

And since it shows up first, this kind of misinformation can be dangerous

But there is a lot way to use Google without their weird ideas about “improving” searches with ads/misinformation

Add: “udm=14” at the end of search results to remove prompted answers

Current results (left) and added “udm=14” (right)

“It’s essentially Google, minus the crap. No parsing of the information in the results. No surfacing metadata like address or link info. No knowledge panels, but also, no ads. It looks like the Google we learned to love in the early 2000s, buried under the "More" menu like lots of other old things Google once did more to emphasize, like Google Books.”

This was added under the search options as “web” but it is faster to appended the udm option

A simplified view does not replace the declining quality of Google’s results, largely caused by decades of SEO optimization by website creators. The same overly optimized results are going to be there, like it or not. It is not Google circa 2001—it is a Google-circa-2001 presentation of Google circa 2024, a very different site.

Google Announcement on Twitter

[crossatlanticsoftware-blog]

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[ourjobagency]

Salesforce customization is not merely an option, it's a necessity in today's rapidly changing business landscape.

To Know More Visit Us:

[askmediainflame]

Meet the Futuristic YouTube Ai Tools That Will Make Your Content Viral.-askmediainflame

In a world where every second, 300 hours of video content is uploaded to YouTube, how can you make your videos stand out from the crowd? Enter the futuristic YouTube AI tools that are revolutionizing the way we create and promote content. From analyzing viewer preferences to generating attention-grabbing thumbnails, these cutting-edge technologies are like a secret weapon in the battle for viral success.For More Information Visit Link- https://www.askmediainflame.com/wp-admin/post.php?post=127&action=edit

[electronicbluebirdhottub]

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