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How To Easily Search For Tweets By Date On Twitter

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One of Twitter’s best features is also the most difficult to find.

Twitter’s advanced search is, ironically enough, not easy to locate. Many people are surprised to learn it even exists

That’s probably because the average user is content with Twitter’s basic search bar.

But you’re here because you’re not satisfied with the basics, are you?

You want to dig deeper. Maybe you want to go back in time and see what was being tweeted about on a specific date.

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Or, perhaps you’re curious to uncover everything someone has tweeted about a specific topic.

Twitter has a built-in search function for that.

Its entire archive of public tweets is searchable, making it possible to find anything you’re looking for if you use the right filters.

Want to see what the reactions on Twitter were like when Google launched a major algorithm update? This article will teach you how.

Want to reminisce on your business’s first tweet to see how far you’ve come since then? We’ll go over how to do that as well.

In order to search for tweets within a specific date range, you’ll have to utilize Twitter’s advanced search functionality.

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Keep reading to learn how advanced search differs from regular search, followed by some examples of advanced search in action.

How To Use Twitter’s Advanced Search Feature

Twitter’s advanced search feature goes beyond the general search bar, letting you conduct highly specific queries with customizable parameters.

To access this feature, visit Twitter’s advanced search page.

Clicking that link will open advanced search in a pop-over window on the web-based version of Twitter.

Search for tweets by a specific date by scrolling all the way down to the bottom of the pop-over window.

You’ll see fields, like in the image below, with options to add dates to your search.

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You can add a range of dates, or just one specific date.

Screenshot by author, May 2022How To Use Twitter’s Advanced Search Feature

In addition to searching by date, you also have the option to narrow down your search by any of Twitter’s other advanced search options.

Those options include things like:

  • Words used in tweets.
  • Exact phrases used in tweets.
  • Hashtags used in tweets.
  • Tweets from a specific account.
  • Accounts mentioned in tweets.
  • Tweets with links only.
  • Amount of engagement (i.e., tweets with a minimum number of replies/likes/retweets).

Here are some examples of searches using these filters.

Example: Find Your First Tweets

Let’s look at an example using several of the advanced search filters in one query.

Longtime Twitter users occasionally find themselves wondering what their first tweets were like and how much engagement they received.

With that said, we’re about to take a trip back in time to look at the first tweets ever published by Search Engine Journal.

First, we must add our Twitter handle in the accounts filter, as shown below.

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Example: Find Your First TweetsScreenshot by author, May 2022Example: Find Your First Tweets

Next, we’ll add a date filter.

We’ll use the date on our Twitter profile that tells us when we first joined.

Just for fun, we’ll create a date range until the end of the year so we can see all tweets from our first several months on Twitter.

Note: You have to enter a value for the date, month, and year, or else Twitter will disregard the date filter.

Example: Find Your First TweetsScreenshot by author, May 2022Example: Find Your First Tweets

Now, the only thing left to do is hit the big “Search” button and see the results.

Example: Find Your First TweetsScreenshot by author, May 2022Example: Find Your First Tweets

There it is, folks.

Our first-ever tweet was a news story about an ad partnership between Yahoo and Twitter.

And we received no engagement at all on any of our first tweets.

How times have changed since then.

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Example: Find Tweets With Specific Keywords From Specific Accounts

Here’s another example that may be useful.

Let’s say you want to find all tweets from a specific account that contain specific keywords.

You may find yourself wanting to look up what Google has officially stated regarding specific SEO topics.

In this particular example, let’s try to find everything Google’s official Twitter accounts have published regarding core updates.

First, we’ll use the keyword filters.

Consider the ways in which the keywords you’re looking up might be used in tweets.

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In this example, our subject might be referred to either as “core update” or “core algorithm update.”

So, we’ll put in “core” and “update” to make sure we catch everything.

Example: Find Tweets With Specific Keywords From Specific AccountsScreenshot by author, May 2022Example: Find Tweets With Specific Keywords From Specific Accounts

Next, we’ll add Google’s official Twitter accounts.

Google has many official accounts, so we’ll only add the ones that are most likely to tweet important information regarding core updates.

Example: Find Tweets With Specific Keywords From Specific AccountsScreenshot by author, May 2022Example: Find Tweets With Specific Keywords From Specific Accounts

From here you can narrow it down even further with engagement and date filters.

We’re going to leave those filters alone for this particular example though.

Here’s what we get after hitting the big “Search” button.

Example: Find Tweets With Specific Keywords From Specific AccountsScreenshot by author, May 2022Example: Find Tweets With Specific Keywords From Specific Accounts

There’s a snapshot of everything tweeted about core updates from Google’s accounts in one place.

Example: Find Your Most Liked Tweets

Another way to utilize Twitter’s advanced search feature is to surface an account’s most-liked tweets.

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You can also find tweets by the number of comments and retweets they received, but for the purpose of this example, we’ll just filter by likes.

This can be for your account, or any other public account on Twitter.

Go back to Twitter’s advanced search form, enter the account you want to look up, and then customize the parameters under Engagements. 

Example: Find Your Most Liked TweetsScreenshot by author, May 2022Example: Find Your Most Liked Tweets

Perform your search and Twitter will show you all tweets from an account that meet a threshold for the number of likes.

As seen in the example below, every time you conduct a query with advanced search Twitter displays the formula it used in the search bar.

If you want to refine a query without going back to the advanced search form, you can simply change the values in the search bar.

Example: Find Your Most Liked TweetsScreenshot by author, May 2022Example: Find Your Most Liked Tweets

Summary

Those are just a few of the many ways to explore Twitter’s archives with advanced search.

All filters can be used in conjunction. That means you can search by date, or search for most-liked tweets within a date range, or search for tweets with comments that also contain a specific word, and so on.

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There are nearly endless combinations of filters you can use to find the exact tweets you need.

Twitter’s advanced search filters are relatively easy to use, but that wasn’t always the case.

You used to have to type in the search operators manually, which required a deep understanding of the way Twitter search works.

Previously, searches by date could be performed by manually adding the “since:” and “until:” operators to your search.

Now, you can simply fill out a form instead of memorizing all the various search commands.

Unfortunately, Twitter’s advanced search isn’t available on the mobile app.

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If you’d like to search by date on the mobile app you can still do so the old-fashioned way using the “since:” and “until:” operators.

Or, you can use the mobile browser version of Twitter, which supports advanced search.

Want to learn more about the ins and outs of this powerful search feature?

Read: Everything You Need to Know About Twitter Advanced Search.

More Resources:


Featured Image: Lenka Horavova/Shutterstock

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How Compression Can Be Used To Detect Low Quality Pages

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Compression can be used by search engines to detect low-quality pages. Although not widely known, it's useful foundational knowledge for SEO.

The concept of Compressibility as a quality signal is not widely known, but SEOs should be aware of it. Search engines can use web page compressibility to identify duplicate pages, doorway pages with similar content, and pages with repetitive keywords, making it useful knowledge for SEO.

Although the following research paper demonstrates a successful use of on-page features for detecting spam, the deliberate lack of transparency by search engines makes it difficult to say with certainty if search engines are applying this or similar techniques.

What Is Compressibility?

In computing, compressibility refers to how much a file (data) can be reduced in size while retaining essential information, typically to maximize storage space or to allow more data to be transmitted over the Internet.

TL/DR Of Compression

Compression replaces repeated words and phrases with shorter references, reducing the file size by significant margins. Search engines typically compress indexed web pages to maximize storage space, reduce bandwidth, and improve retrieval speed, among other reasons.

This is a simplified explanation of how compression works:

  • Identify Patterns:
    A compression algorithm scans the text to find repeated words, patterns and phrases
  • Shorter Codes Take Up Less Space:
    The codes and symbols use less storage space then the original words and phrases, which results in a smaller file size.
  • Shorter References Use Less Bits:
    The “code” that essentially symbolizes the replaced words and phrases uses less data than the originals.

A bonus effect of using compression is that it can also be used to identify duplicate pages, doorway pages with similar content, and pages with repetitive keywords.

Research Paper About Detecting Spam

This research paper is significant because it was authored by distinguished computer scientists known for breakthroughs in AI, distributed computing, information retrieval, and other fields.

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Marc Najork

One of the co-authors of the research paper is Marc Najork, a prominent research scientist who currently holds the title of Distinguished Research Scientist at Google DeepMind. He’s a co-author of the papers for TW-BERT, has contributed research for increasing the accuracy of using implicit user feedback like clicks, and worked on creating improved AI-based information retrieval (DSI++: Updating Transformer Memory with New Documents), among many other major breakthroughs in information retrieval.

Dennis Fetterly

Another of the co-authors is Dennis Fetterly, currently a software engineer at Google. He is listed as a co-inventor in a patent for a ranking algorithm that uses links, and is known for his research in distributed computing and information retrieval.

Those are just two of the distinguished researchers listed as co-authors of the 2006 Microsoft research paper about identifying spam through on-page content features. Among the several on-page content features the research paper analyzes is compressibility, which they discovered can be used as a classifier for indicating that a web page is spammy.

Detecting Spam Web Pages Through Content Analysis

Although the research paper was authored in 2006, its findings remain relevant to today.

Then, as now, people attempted to rank hundreds or thousands of location-based web pages that were essentially duplicate content aside from city, region, or state names. Then, as now, SEOs often created web pages for search engines by excessively repeating keywords within titles, meta descriptions, headings, internal anchor text, and within the content to improve rankings.

Section 4.6 of the research paper explains:

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“Some search engines give higher weight to pages containing the query keywords several times. For example, for a given query term, a page that contains it ten times may be higher ranked than a page that contains it only once. To take advantage of such engines, some spam pages replicate their content several times in an attempt to rank higher.”

The research paper explains that search engines compress web pages and use the compressed version to reference the original web page. They note that excessive amounts of redundant words results in a higher level of compressibility. So they set about testing if there’s a correlation between a high level of compressibility and spam.

They write:

“Our approach in this section to locating redundant content within a page is to compress the page; to save space and disk time, search engines often compress web pages after indexing them, but before adding them to a page cache.

…We measure the redundancy of web pages by the compression ratio, the size of the uncompressed page divided by the size of the compressed page. We used GZIP …to compress pages, a fast and effective compression algorithm.”

High Compressibility Correlates To Spam

The results of the research showed that web pages with at least a compression ratio of 4.0 tended to be low quality web pages, spam. However, the highest rates of compressibility became less consistent because there were fewer data points, making it harder to interpret.

Figure 9: Prevalence of spam relative to compressibility of page.

The researchers concluded:

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“70% of all sampled pages with a compression ratio of at least 4.0 were judged to be spam.”

But they also discovered that using the compression ratio by itself still resulted in false positives, where non-spam pages were incorrectly identified as spam:

“The compression ratio heuristic described in Section 4.6 fared best, correctly identifying 660 (27.9%) of the spam pages in our collection, while misidentifying 2, 068 (12.0%) of all judged pages.

Using all of the aforementioned features, the classification accuracy after the ten-fold cross validation process is encouraging:

95.4% of our judged pages were classified correctly, while 4.6% were classified incorrectly.

More specifically, for the spam class 1, 940 out of the 2, 364 pages, were classified correctly. For the non-spam class, 14, 440 out of the 14,804 pages were classified correctly. Consequently, 788 pages were classified incorrectly.”

The next section describes an interesting discovery about how to increase the accuracy of using on-page signals for identifying spam.

Insight Into Quality Rankings

The research paper examined multiple on-page signals, including compressibility. They discovered that each individual signal (classifier) was able to find some spam but that relying on any one signal on its own resulted in flagging non-spam pages for spam, which are commonly referred to as false positive.

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The researchers made an important discovery that everyone interested in SEO should know, which is that using multiple classifiers increased the accuracy of detecting spam and decreased the likelihood of false positives. Just as important, the compressibility signal only identifies one kind of spam but not the full range of spam.

The takeaway is that compressibility is a good way to identify one kind of spam but there are other kinds of spam that aren’t caught with this one signal. Other kinds of spam were not caught with the compressibility signal.

This is the part that every SEO and publisher should be aware of:

“In the previous section, we presented a number of heuristics for assaying spam web pages. That is, we measured several characteristics of web pages, and found ranges of those characteristics which correlated with a page being spam. Nevertheless, when used individually, no technique uncovers most of the spam in our data set without flagging many non-spam pages as spam.

For example, considering the compression ratio heuristic described in Section 4.6, one of our most promising methods, the average probability of spam for ratios of 4.2 and higher is 72%. But only about 1.5% of all pages fall in this range. This number is far below the 13.8% of spam pages that we identified in our data set.”

So, even though compressibility was one of the better signals for identifying spam, it still was unable to uncover the full range of spam within the dataset the researchers used to test the signals.

Combining Multiple Signals

The above results indicated that individual signals of low quality are less accurate. So they tested using multiple signals. What they discovered was that combining multiple on-page signals for detecting spam resulted in a better accuracy rate with less pages misclassified as spam.

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The researchers explained that they tested the use of multiple signals:

“One way of combining our heuristic methods is to view the spam detection problem as a classification problem. In this case, we want to create a classification model (or classifier) which, given a web page, will use the page’s features jointly in order to (correctly, we hope) classify it in one of two classes: spam and non-spam.”

These are their conclusions about using multiple signals:

“We have studied various aspects of content-based spam on the web using a real-world data set from the MSNSearch crawler. We have presented a number of heuristic methods for detecting content based spam. Some of our spam detection methods are more effective than others, however when used in isolation our methods may not identify all of the spam pages. For this reason, we combined our spam-detection methods to create a highly accurate C4.5 classifier. Our classifier can correctly identify 86.2% of all spam pages, while flagging very few legitimate pages as spam.”

Key Insight:

Misidentifying “very few legitimate pages as spam” was a significant breakthrough. The important insight that everyone involved with SEO should take away from this is that one signal by itself can result in false positives. Using multiple signals increases the accuracy.

What this means is that SEO tests of isolated ranking or quality signals will not yield reliable results that can be trusted for making strategy or business decisions.

Takeaways

We don’t know for certain if compressibility is used at the search engines but it’s an easy to use signal that combined with others could be used to catch simple kinds of spam like thousands of city name doorway pages with similar content. Yet even if the search engines don’t use this signal, it does show how easy it is to catch that kind of search engine manipulation and that it’s something search engines are well able to handle today.

Here are the key points of this article to keep in mind:

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  • Doorway pages with duplicate content is easy to catch because they compress at a higher ratio than normal web pages.
  • Groups of web pages with a compression ratio above 4.0 were predominantly spam.
  • Negative quality signals used by themselves to catch spam can lead to false positives.
  • In this particular test, they discovered that on-page negative quality signals only catch specific types of spam.
  • When used alone, the compressibility signal only catches redundancy-type spam, fails to detect other forms of spam, and leads to false positives.
  • Combing quality signals improves spam detection accuracy and reduces false positives.
  • Search engines today have a higher accuracy of spam detection with the use of AI like Spam Brain.

Read the research paper, which is linked from the Google Scholar page of Marc Najork:

Detecting spam web pages through content analysis

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New Google Trends SEO Documentation

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Google publishes new documentation for how to use Google Trends for search marketing

Google Search Central published new documentation on Google Trends, explaining how to use it for search marketing. This guide serves as an easy to understand introduction for newcomers and a helpful refresher for experienced search marketers and publishers.

The new guide has six sections:

  1. About Google Trends
  2. Tutorial on monitoring trends
  3. How to do keyword research with the tool
  4. How to prioritize content with Trends data
  5. How to use Google Trends for competitor research
  6. How to use Google Trends for analyzing brand awareness and sentiment

The section about monitoring trends advises there are two kinds of rising trends, general and specific trends, which can be useful for developing content to publish on a site.

Using the Explore tool, you can leave the search box empty and view the current rising trends worldwide or use a drop down menu to focus on trends in a specific country. Users can further filter rising trends by time periods, categories and the type of search. The results show rising trends by topic and by keywords.

To search for specific trends users just need to enter the specific queries and then filter them by country, time, categories and type of search.

The section called Content Calendar describes how to use Google Trends to understand which content topics to prioritize.

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Google explains:

“Google Trends can be helpful not only to get ideas on what to write, but also to prioritize when to publish it. To help you better prioritize which topics to focus on, try to find seasonal trends in the data. With that information, you can plan ahead to have high quality content available on your site a little before people are searching for it, so that when they do, your content is ready for them.”

Read the new Google Trends documentation:

Get started with Google Trends

Featured Image by Shutterstock/Luis Molinero

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All the best things about Ahrefs Evolve 2024

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All the best things about Ahrefs Evolve 2024

Hey all, I’m Rebekah and I am your Chosen One to “do a blog post for Ahrefs Evolve 2024”.

What does that entail exactly? I don’t know. In fact, Sam Oh asked me yesterday what the title of this post would be. “Is it like…Ahrefs Evolve 2024: Recap of day 1 and day 2…?” 

Even as I nodded, I couldn’t get over how absolutely boring that sounded. So I’m going to do THIS instead: a curation of all the best things YOU loved about Ahrefs’ first conference, lifted directly from X.

Let’s go!

OUR HUGE SCREEN

CONFERENCE VENUE ITSELF

It was recently named the best new skyscraper in the world, by the way.

 

OUR AMAZING SPEAKER LINEUP – SUPER INFORMATIVE, USEFUL TALKS!

 

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GREAT MUSIC

 

AMAZING GOODIES

 

SELFIE BATTLE

Some background: Tim and Sam have a challenge going on to see who can take the most number of selfies with all of you. Last I heard, Sam was winning – but there is room for a comeback yet!

 

THAT BELL

Everybody’s just waiting for this one.

 

STICKER WALL

AND, OF COURSE…ALL OF YOU!

 

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There’s a TON more content on LinkedIn – click here – but I have limited time to get this post up and can’t quite figure out how to embed LinkedIn posts so…let’s stop here for now. I’ll keep updating as we go along!



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