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Reverse image search is one of the most well-known and easiest digital investigative techniques, with two-click functionality of choosing “Search Google for image” in many web browsers. This method has also seen widespread use in popular culture, perhaps most notably in the MTV show Catfish, which exposes people in online relationships who use stolen photographs on their social media.

However, if you only use Google for reverse image searching, you will be disappointed more often than not. Limiting your search process to uploading a photograph in its original form to just images.google.com may give you useful results for the most obviously stolen or popular images, but for most any sophisticated research project, you need additional sites at your disposal — along with a lot of creativity.

This guide will walk through detailed strategies to use reverse image search in digital investigations, with an eye towards identifying people and locations, along with determining an image’s progeny. After detailing the core differences between the search engines, Yandex, Bing, and Google are tested on five test images showing different objects and from various regions of the world.

Beyond Google

The first and most important piece of advice on this topic cannot be stressed enough: Google reverse image search isn’t very good.

As of this guide’s publication date, the undisputed leader of reverse image search is the Russian site Yandex. After Yandex, the runners-up are Microsoft’s Bing and Google. A fourth service that could also be used in investigations is TinEye, but this site specializes in intellectual property violations and looks for exact duplicates of images.

Yandex

Yandex is by far the best reverse image search engine, with a scary-powerful ability to recognize faces, landscapes, and objects. This Russian site draws heavily upon user-generated content, such as tourist review sites (e.g. FourSquare and TripAdvisor) and social networks (e.g. dating sites), for remarkably accurate results with facial and landscape recognition queries.

Its strengths lie in photographs taken in a European or former-Soviet context. While photographs from North America, Africa, and other places may still return useful results on Yandex, you may find yourself frustrated by scrolling through results mostly from Russia, Ukraine, and eastern Europe rather than the country of your target images.

To use Yandex, go to images.yandex.com, then choose the camera icon on the right.

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From there, you can either upload a saved image or type in the URL of one hosted online.

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If you get stuck with the Russian user interface, look out for Выберите файл (Choose file), Введите адрес картинки (Enter image address), and Найти (Search). After searching, look out for Похожие картинки (Similar images), and Ещё похожие (More similar).

The facial recognition algorithms used by Yandex are shockingly good. Not only will Yandex look for photographs that look similar to the one that has a face in it, but it will also look for other photographs of the same person (determined through matching facial similarities) with completely different lighting, background colors, and positions. While Google and Bing may just look for other photographs showing a person with similar clothes and general facial features, Yandex will search for those matches, and also other photographs of a facial match. Below, you can see how the three services searched the face of Sergey Dubinsky, a Russian suspect in the downing of MH17. Yandex found numerous photographs of Dubinsky from various sources (only two of the top results had unrelated people), with the result differing from the original image but showing the same person. Google had no luck at all, while Bing had a single result (fifth image, second row) that also showed Dubinsky.

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Yandex is, obviously, a Russian service, and there are worries and suspicions of its ties (or potential future ties) to the Kremlin. While we at Bellingcat constantly use Yandex for its search capabilities, you may be a bit more paranoid than us. Use Yandex at your own risk, especially if you are also worried about using VK and other Russian services. If you aren’t particularly paranoid, try searching an un-indexed photograph of yourself or someone you know in Yandex, and see if it can find yourself or your doppelganger online.

Bing

Over the past few years, Bing has caught up to Google in its reverse image search capabilities, but is still limited. Bing’s “Visual Search”, found at images.bing.com, is very easy to use, and offers a few interesting features not found elsewhere.

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Within an image search, Bing allows you to crop a photograph (button below the source image) to focus on a specific element in said photograph, as seen below. The results with the cropped image will exclude the extraneous elements, focusing on the user-defined box. However, if the selected portion of the image is small, it is worth it to manually crop the photograph yourself and increase the resolution — low-resolution images (below 200×200) bring back poor results.

Below, a Google Street View image of a man walking a couple of pugs was cropped to focus on just the pooches, leading to Bing to suggest the breed of dog visible in the photograph (the “Looks like” feature), along with visually similar results. These results mostly included pairs of dogs being walked, matching the source image, but did not always only include pugs, as French bulldogs, English bulldogs, mastiffs, and others are mixed in.

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Google

By far the most popular reverse image search engine, at images.google.com, Google is fine for most rudimentary reverse image searches. Some of these relatively simple queries include identifying well-known people in photographs, finding the source of images that have been shared quite a bit online, determining the name and creator of a piece of art, and so on. However, if you want to locate images that are not close to an exact copy of the one you are researching, you may be disappointed.

For example, when searching for the face of a man who tried to attack a BBC journalist at a Trump rally, Google can find the source of the cropped image, but cannot find any additional images of him, or even someone who bears a passing resemblance to him.

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trump results google

While Google was not very strong in finding other instances of this man’s face or similar-looking people, it still found the original, un-cropped version of the photograph the screenshot was taken from, showing some utility.

Five Test Cases

For testing out different reverse image search techniques and engines, a handful of images representing different types of investigations are used, including both original photographs (not previously uploaded online) and recycled ones. Due to the fact that these photographs are included in this guide, it is likely that these test cases will not work as intended in the future, as search engines will index these photographs and integrate them into their results. Thus, screenshots of the results as they appeared when this guide was being written are included.

These test photographs include a number of different geographic regions to test the strength of search engines for source material in western Europe, eastern Europe, South America, southeast Asia, and the United States. With each of these photographs, I have also highlighted discrete objects within the image to test out the strengths and weaknesses for each search engine.

Feel free to download these photographs (every image in this guide is hyperlinked directly to a JPEG file) and run them through search engines yourself to test out your skills.

Olisov Palace In Nizhny Novgord, Russia (Original, not previously uploaded online)

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Isolated: White SUV in Nizhny Novgorod

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Isolated: Trailer in Nizhny Novgorod

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Cityscape In Cebu, Philippines (Original, not previously uploaded online)

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Isolated: Condominium complex, “The Padgett Place

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Isolated: “Waterfront Hotel

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Students From Bloomberg 2020 Ad (Screenshot from video)

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Isolated: Student

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Av. do Café In São Paulo, Brazil (Screenshot Google Street View)

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Isolated: Toca do Açaí

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Isolated: Estacionamento (Parking)

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Amsterdam Canal (Original, not previously uploaded online)

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Isolated: Grey Heron

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Isolated: Dutch Flag (also rotated 90 degrees clockwise)

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Results

Each of these photographs were chosen in order to demonstrate the capabilities and limitations of the three search engines. While Yandex in particular may seem like it is working digital black magic at times, it is far from infallible and can struggle with some types of searches. For some ways to possibly overcome these limitations, I’ve detailed some creative search strategies at the end of this guide.

Novgorod’s Olisov Palace

Predictably, Yandex had no trouble identifying this Russian building. Along with photographs from a similar angle to our source photograph, Yandex also found images from other perspectives, including 90 degrees counter-clockwise (see the first two images in the third row) from the vantage point of the source image.

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Yandex also had no trouble identifying the white SUV in the foreground of the photograph as a Nissan Juke.

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Lastly, in the most challenging isolated search for this image, Yandex was unsuccessful in identifying the non-descript grey trailer in front of the building. A number of the results look like the one from the source image, but none are an actual match.

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Bing had no success in identifying this structure. Nearly all of its results were from the United States and western Europe, showing houses with white/grey masonry or siding and brown roofs.

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Likewise, Bing could not determine that the white SUV was a Nissan Juke, instead focusing on an array of other white SUVs and cars.

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Lastly, Bing failed in identifying the grey trailer, focusing more on RVs and larger, grey campers.

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Google‘s results for the full photograph are comically bad, looking to the House television show and images with very little visual similarity.

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Google successfully identified the white SUV as a Nissan Juke, even noting it in the text field search. As seen with Yandex, feeding the search engine an image from a similar perspective as popular reference materials — a side view of a car that resembles that of most advertisements — will best allow reverse image algorithms to work their magic.

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Lastly, Google recognized what the grey trailer was (travel trailer / camper), but its “visually similar images” were far from it.

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Scorecard: Yandex 2/3; Bing 0/3; Google 1/3

Cebu

Yandex was technically able to identify the cityscape as that of Cebu in the Philippines, but perhaps only by accident. The fourth result in the first row and the fourth result in the second row are of Cebu, but only the second photograph shows any of the same buildings as in the source image. Many of the results were also from southeast Asia (especially Thailand, which is a popular destination for Russian tourists), noting similar architectural styles, but none are from the same perspective as the source.

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Of the two buildings isolated from the search (the Padgett Palace and Waterfront Hotel), Yandex was able to identify the latter, but not the former. The Padgett Palace building is a relatively unremarkable high-rise building filled with condos, while the Waterfront Hotel also has a casino inside, leading to an array of tourist photographs showing its more distinct architecture.

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Bing did not have any results that were even in southeast Asia when searching for the Cebu cityscape, showing a severe geographic limitation to its indexed results.

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Like Yandex, Bing was unable to identify the building on the left part of the source image.

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Bing was unable to find the Waterfront Hotel, both when using Bing’s cropping function (bringing back only low-resolution photographs) and manually cropping and increasing the resolution of the building from the source image. It is worth noting that the results from these two versions of the image, which were identical outside of the resolution, brought back dramatically different results.

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As with Yandex, Google brought back a photograph of Cebu in its results, but without a strong resemblance to the source image. While Cebu was not in the thumbnails for the initial results, following through to “Visually similar images” will fetch an image of Cebu’s skyline as the eleventh result (third image in the second row below).

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As with Yandex and Bing, Google was unable to identify the high-rise condo building on the left part of the source image. Google also had no success with the Waterfront Hotel image.

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Scorecard: Yandex 4/6; Bing 0/6; Google 2/6

Bloomberg 2020 Student

Yandex found the source image from this Bloomberg campaign advertisement — a Getty Images stock photo. Along with this, Yandex also found versions of the photograph with filters applied (second result, first row) and additional photographs from the same stock photo series. Also, for some reason, porn, as seen in the blurred results below.

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When isolating just the face of the stock photo model, Yandex brought back a handful of other shots of the same guy (see last image in first row), plus images of the same stock photo set in the classroom (see the fourth image in the first row).

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Bing had an interesting search result: it found the exact match of the stock photograph, and then brought back “Similar images” of other men in blue shirts. The “Pages with this” tab of the result provides a handy list of duplicate versions of this same image across the web.

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Focusing on just the face of the stock photo model does not bring back any useful results, or provide the source image that it was taken from.

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Google recognizes that the image used by the Bloomberg campaign is a stock photo, bringing back an exact result. Google will also provide other stock photos of people in blue shirts in class.

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In isolating the student, Google will again return the source of the stock photo, but its visually similar images do not show the stock photo model, rather an array of other men with similar facial hair. We’ll count this as a half-win in finding the original image, but not showing any information on the specific model, as Yandex did.

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Scorecard: Yandex 6/8; Bing 1/8; Google 3.5/8

Brazilian Street View

Yandex could not figure out that this image was snapped in Brazil, instead focusing on urban landscapes in Russia.

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For the parking sign [Estacionamento], Yandex did not even come close.

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Bing did not know that this street view image was taken in Brazil.

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…nor did Bing recognize the parking sign

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…or the Toca do Açaí logo.

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Despite the fact that the image was directly taken from Google’s Street View, Google reverse image search did not recognize a photograph uploaded onto its own service.

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Just as Bing and Yandex, Google could not recognize the Portuguese parking sign.

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Lastly, Google did not come close to identifying the Toca do Açaí logo, instead focusing on various types of wooden panels, showing how it focused on the backdrop of the image rather than the logo and words.

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Scorecard: Yandex 7/11; Bing 1/11; Google 3.5/11

Amsterdam Canal

Yandex knew exactly where this photograph was taken in Amsterdam, finding other photographs taken in central Amsterdam, and even including ones with various types of birds in the frame.

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Yandex correctly identified bird in the foreground of the photograph as a grey heron (серая цапля), also bringing back an array of images of grey herons in a similar position and posture as the source image.

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However, Yandex flunked the test of identifying the Dutch flag hanging in the background of the photograph. When rotating the image 90 degrees clockwise to present the flag in its normal pattern, Yandex was able to figure out that it was a flag, but did not return any Dutch flags in its results.

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Bing only recognized that this image shows an urban landscape with water, with no results from Amsterdam.

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Though Bing struggled with identifying an urban landscape, it correctly identified the bird as a grey heron, including a specialized “Looks like” result going to a page describing the bird.

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However, like with Yandex, the Dutch flag was too confusing for Bing, both in its original and rotated forms.

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Google noted that there was a reflection in the canal of the image, but went no further than this, focusing on various paved paths in cities and nothing from Amsterdam.

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Google was close in the bird identification exercise, but just barely missed it — it is a grey, not great blue, heron.

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Google was also unable to identify the Dutch flag. Though Yandex seemed to recognize that the image is a flag, Google’s algorithm focused on the windowsill framing the image and misidentified the flag as curtains.

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Final Scorecard: Yandex 9/14; Bing 2/14; Google 3.5/14

Creative Searching

Even with the shortcomings described in this guide, there are a handful of methods to maximize your search process and game the search algorithms.

Specialized Sites

For one, you could use some other, more specialized search engines outside of the three detailed in this guide. The Cornell Lab’s Merlin Bird ID app, for example, is extremely accurate in identifying the type of birds in a photograph, or giving possible options. Additionally, though it isn’t an app and doesn’t let you reverse search a photograph, FlagID.org will let you manually enter information about a flag to figure out where it comes from. For example, with the Dutch flag that even Yandex struggled with, FlagID has no problem. After choosing a horizontal tricolor flag, we put in the colors visible in the image, then receive a series of options that include the Netherlands (along with other, similar-looking flags, such as the flag of Luxembourg).

flagsearch1.jpgflagsearch2.jpg

Language Recognition

If you are looking at a foreign language with an orthography you don’t recognize, try using some OCR or Google Translate to make your life easier. You can use Google Translate’s handwriting tool to detect the language* of a letter that you hand-write, or choose a language (if you know it already) and then write it out yourself for the word. Below, the name of a cafe (“Hedgehog in the Fog“) is written out with Google Translate’s handwriting tool, giving the typed-out version of the word (Ёжик) that can be searched.

*Be warned that Google Translate is not very good at recognizing letters if you do not already know the language, though if you scroll through enough results, you can find your handwritten letter eventually.

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Pixelation And Blurring

As detailed in a brief Twitter thread, you can pixelate or blur elements of a photograph in order to trick the search engine to focus squarely on the background. In this photograph of Rudy Giuliani’s spokeswoman, uploading the exact image will not bring back results showing where it was taken.

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However, if we blur out/pixelate the woman in the middle of the image, it will allow Yandex (and other search engines) to work their magic in matching up all of the other elements of the image: the chairs, paintings, chandelier, rug and wall patterns, and so on.

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After this pixelation is carried out, Yandex knows exactly where the image was taken: a popular hotel in Vienna.

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Conclusion

Reverse image search engines have progressed dramatically over the past decade, with no end in sight. Along with the ever-growing amount of indexed material, a number of search giants have enticed their users to sign up for image hosting services, such as Google Photos, giving these search algorithms an endless amount of material for machine learning. On top of this, facial recognition AI is entering the consumer space with products like FindClone and may already be used in some search algorithms, namely with Yandex. There are no publicly available facial recognition programs that use any Western social network, such as Facebook or Instagram, but perhaps it is only a matter of time until something like this emerges, dealing a major blow to online privacy while also (at that great cost) increasing digital research functionality.

If you skipped most of the article and are just looking for the bottom line, here are some easy-to-digest tips for reverse image searching:

  • Use Yandex first, second, and third, and then try Bing and Google if you still can’t find your desired result.
  • If you are working with source imagery that is not from a Western or former Soviet country, then you may not have much luck. These search engines are hyper-focused on these areas, and struggle for photographs taken in South America, Central America/Caribbean, Africa, and much of Asia.
  • Increase the resolution of your source image, even if it just means doubling or tripling the resolution until it’s a pixelated mess. None of these search engines can do much with an image that is under 200×200.
  • Try cropping out elements of the image, or pixelating them if it trips up your results. Most of these search engines will focus on people and their faces like a heat-seeking missile, so pixelate them to focus on the background elements.
  • If all else fails, get really creative: mirror your image horizontally, add some color filters, or use the clone tool on your image editor to fill in elements on your image that are disrupting searches.

[Source: This article was published in bellingcat.com By Aric Toler - Uploaded by the Association Member: Issac Avila] 

Categorized in Investigative Research

Google to offer users the option to auto-delete location history and web search data that it harvests

Google is to give users the choice of being able to automatically delete their search and location history after three months.

It announced the auto-delete tools for location history data, as well as web browsing and app activity, which will be rolled out in the coming weeks.

Last November Google was accused of misleading about location tracking after consumer groups from seven European nations asked their privacy regulators to take action against the search engine giant.

google logo mountainview 011

Location tracking

Consumer groups from the Netherlands, Poland, Czech Republic, Greece, Norway, Slovenia and Sweden, all filed GDPR complaints against Google’s location tracking.

They alleged that Google is tracking the movements of millions of users in breach of the European Union’s privacy laws.

Google, of course, is already facing a lawsuit in the United States for allegedly tracking phone users regardless of privacy settings.

That lawsuit was filed after an investigation by the Associated Press found that a number of Google services running on Android and Apple devices determine the user’s location and store it, even when Google’s “Location History” setting is switched off.

It should be remembered that Google had already allowed users to manually delete the data it harvests when they use its products such as YouTube, Maps and Search.

But now it trying to give users more control by offering auto-delete tools.

“And when you turn on settings like Location History or Web & App Activity, the data can make Google products more useful for you – like recommending a restaurant that you might enjoy, or helping you pick up where you left off on a previous search,” wrote David Monsees, product manager of search in a blog posting.

Auto-delete

“We work to keep your data private and secure, and we’ve heard your feedback that we need to provide simpler ways for you to manage or delete it,” Monsees added.

“You can already use your Google Account to access simply on/off controls for Location History and Web & App Activity, and if you choose – to delete all or part of that data manually,” he wrote. “In addition to these options, we’re announcing auto-delete controls that make it even easier to manage your data.”

Essentially, the user will give a time limit to choose for how long you want your data to be saved. This could be 3 months or 18 months.

Any data older than that will be automatically deleted from your account on an ongoing basis.

“These controls are coming first to Location History and Web & App Activity and will roll out in the coming weeks,” Monsees wrote. “You should always be able to manage your data in a way that works best for you–and we’re committed to giving you the best controls to make that happen.”

It should be noted that there will be no auto-delete of YouTube watch history or voice commands issued via Home and Assistant.

[Source: This article was published in silicon.co.uk By Tom Jowitt - Uploaded by the Association Member: James Gill]

Categorized in Search Engine

The new language model can think in both directions, fingers crossed

Google has updated its search algorithms to tap into an AI language model that is better at understanding netizens' queries than previous systems.

Pandu Nayak, a Google fellow and vice president of search, announced this month that the Chocolate Factory has rolled out BERT, short for Bidirectional Encoder Representations from Transformers, for its most fundamental product: Google Search.

To pull all of this off, researchers at Google AI built a neural network known as a transformer. The architecture is suited to deal with sequences in data, making them ideal for dealing with language. To understand a sentence, you must look at all the words in it in a specific order. Unlike previous transformer models that only consider words in one direction – left to right – BERT is able to look back to consider the overall context of a sentence.

“BERT models can, therefore, consider the full context of a word by looking at the words that come before and after it—particularly useful for understanding the intent behind search queries,” Nayak said.

For example, below's what the previous Google Search and new BERT-powered search looks like when you query: “2019 brazil traveler to usa need a visa.”

2019 brazil

Left: The result returned for the old Google Search that incorrectly understands the query as a US traveler heading to Brazil. Right: The result returned for the new Google Search using BERT, which correctly identifies the search is for a Brazilian traveler going to the US. Image credit: Google.

BERT has a better grasp of the significance behind the word "to" in the new search. The old model returns results that show information for US citizens traveling to Brazil, instead of the other way around. It looks like BERT is a bit patchy, however, as a Google Search today still appears to give results as if it's American travelers looking to go to Brazil:

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Current search result for the query: 2019 brazil traveler to USA need a visa. It still thinks the sentence means a US traveler going to Brazil

The Register asked Google about this, and a spokesperson told us... the screenshots were just a demo. Your mileage may vary.

"In terms of not seeing those exact examples, the side-by-sides we showed were from our evaluation process, and might not 100 percent mirror what you see live in Search," the PR team told us. "These were side-by-side examples from our evaluation process where we identified particular types of language understanding challenges where BERT was able to figure out the query better - they were largely illustrative.

"Search is dynamic, content on the web changes. So it's not necessarily going to have a predictable set of results for any query at any point in time. The web is constantly changing and we make a lot of updates to our algorithms throughout the year as well."

Nayak claimed BERT would improve 10 percent of all its searches. The biggest changes will be for longer queries, apparently, where sentences are peppered with prepositions like “for” or “to.”

“BERT will help Search better understand one in 10 searches in the US in English, and we’ll bring this to more languages and locales over time,” he said.

Google will run BERT on its custom Cloud TPU chips; it declined to disclose how many would be needed to power the model. The most powerful Cloud TPU option currently is the Cloud TPU v3 Pods, which contain 64 ASICs, each carrying performance of 420 teraflops and 128GB of high-bandwidth memory.

At the moment, BERT will work best for queries made in English. Google said it also works in two dozen countries for other languages, too, such as Korean, Hindi, and Portuguese for “featured snippets” of text. ®

[Source: This article was published in theregister.co.uk By Katyanna Quach - Uploaded by the Association Member: Anthony Frank]

Categorized in Search Engine

As always, when Google releases a new update to its search algorithm, it’s an exciting (and potentially scary) time for SEO. Google’s latest update, BERT, represents the biggest alteration to its search algorithm in the last five years.

So, what does BERT do?

Google says the BERT update means its search algorithm will have an easier time comprehending conversational nuances in a user’s query.

The best example of this is statements where prepositional words such as ‘to’ and ‘for’ inform the intent of the query.

BERT stands for Bidirectional Encoder Representations from Transformers, which is a language processing technique based on neural networking principles.

Google estimates the update will impact about 10% of United States-based queries and has revealed BERT can already be seen in action on featured snippets around the world.

How does Google BERT affect on-page SEO?

SEO practitioners can breathe a collective sigh of relief, because the Google BERT update is not designed to penalise websites, rather, only improve the way the search engine understands and interprets search queries.

However, because the search algorithm is better at understanding nuances in language, it means websites with higher-quality written content are going to be more discoverable.

Websites that have a lot of detailed ‘how-to’ guides and other in-depth content designed to benefit users are going to get the most from Google BERT. This means businesses who aren’t implementing a thorough content strategy are likely to fall behind the curve.

Basically, the BERT update follows Google’s long-running trend of trying to improve the ability of its search algorithm to accurately serve conversational search queries.

The ultimate result of this trend is users being able to perform detailed search queries with the Google voice assistant as if they were speaking to a real person.

Previous algorithm updates

While BERT may be the first major change to Google search in five years, it’s not the biggest shakeup in their history.

The prior Google PANDA and Google PENGUIN updates were both significant and caused a large number of websites to become penalised due to the use of SEO strategies that were considered ‘spammy’ or unfriendly to users.

PANDA

Google PANDA was developed in response to user complaints about ‘content farms’.

Basically, Google’s algorithm was rewarding quantity over quality, meaning there was a business incentive for websites to pump out lots of cheaply acquired content for the purposes of serving ads next to or even within them.

The PANDA update most noticeably affected link building or ‘article marketing’ strategies where low-quality content was published to content farms with a link to a business’ website attached to a keyword repeated throughout the article.

It meant that there was a significant push towards more ethical content marketing strategies, such as guest posting.

PENGUIN

Google PENGUIN is commonly seen as a follow up to the work started by PANDA, targeting spammy link-building practices and ‘black-hat’ SEO techniques.

This update was focused primarily on the way the algorithm evaluates the authority of links as well as the sincerity of their implementation in website content. Spammy or manipulative links now carried less weight. 

However, this meant that if another website posted a link to yours in a spammy or manipulative way, it would negatively affect your search rankings.

This meant that webmasters and SEO-focused businesses needed to make use of the disavow tool to inform Google what inbound links they approve of and which they don’t.

[Source: This article was published in smartcompany.com.au By LUCAS BIKOWSKI - Uploaded by the Association Member: Bridget Miller]

Categorized in Search Engine

Don't try to optimize for BERT, try to optimize your content for humans.

Google introduced the BERT update to its Search ranking system last week. The addition of this new algorithm, designed to better understand what’s important in natural language queries, is a significant change. Google said it impacts 1 in 10 queries. Yet, many SEOs and many of the tracking tools did not notice massive changes in the Google search results while this algorithm rolled out in Search over the last week.

The question is, Why?

The short answer. This BERT update really was around understanding “longer, more conversational queries,” Google wrote in its blog post. The tracking tools, such as Mozcast and others, primarily track shorter queries. That means BERT’s impact is less likely to be visible to these tools.

And for site owners, when you look at your rankings, you likely not tracking a lot of long-tail queries. You track queries that send higher volumes of traffic to your web site, and those tend to be short-tail queries.

Moz on BERT. Pete Meyers of Moz said the MozCast tool tracks shorter head terms and not the types of phrases that are likely to require the natural language processing (NLP) of BERT.

dr.pete

RankRanger on BERT. The folks at RankRanger, another toolset provider told me something similar. “Overall, we have not seen a real ‘impact’ — just a few days of slightly increased rank fluctuations,” the company said. Again, this is likely due to the dataset these companies track — short-tail keywords over long -tail keywords.

Overall tracking tools on BERT. If you look at the tracking tools, virtually all of them showed a smaller level of fluctuation on the days BERT was rolling out compared to what they have shown for past Google algorithm updates such as core search algorithm updates, or the Panda and Penguin updates.

Here are screenshots of the tools over the past week. Again, you would see significant spikes in changes, but these tools do not show that:

mozcast 800x348

serpmetrics 800x308

algoroo 800x269

advancedwebranking 800x186

accuranker 800x245

rankranger 800x265

semrush 800x358

SEO community on BERT. When it comes to individuals picking up on changes to their rankings in Google search, that also was not as large as a Google core update. We did notice chatter throughout the week, but that chatter within the SEO community was not as loud as is typical with other Google updates.

Why we care. We are seeing a lot of folks asking about how they can improve their sites now that BERT is out in the wild. That’s not the way to think about BERT. Google has already stated there is no real way to optimize for it. Its function is to help Google better understand searchers’ intent when they search in natural language. The upside for SEOs and content creators is they can be less concerned about “writing for the machines.” Focus on writing great content — for real people.

Danny Sullivan from Google said again, you cannot really optimize for BERT:

johan

Continue with your strategy to write the best content for your users. Don’t do anything special for BERT, but rather, be special for your users. If you are writing for people, you are already “optimizing” for Google’s BERT algorithm.

[Source: This article was published in searchengineland.com By Barry Schwartz - Uploaded by the Association Member: Joshua Simon]

Categorized in Search Engine

Google said it is making the biggest change to its search algorithm in the past five years that, if successful, users might not be able to detect.

The search giant on Friday announced a tweak to the software underlying its vaunted search engine that is meant to better interpret queries when written in sentence form. Whereas prior versions of the search engine may have overlooked words such as “can” and “to,” the new software is able to help evaluate whether those change the intent of a search, Google has said. Put a bit more simply, it is a way of understanding search terms in relation to each other and it looks at them as an entire phrase, rather than as just a bucket of words, the company said. Google is calling the new software BERT, after a research paper published last year by Google executives describing a form of language processing known as Bidirectional Encoder Representations from Transformers.

While Google is constantly tweaking its algorithm, BERT could affect as many as 10 percent of English language searches, said Pandu Nayak, vice president of search, at a media event. Understanding queries correctly so Google returns the best result on the first try is essential to Google’s transformation from a list of links to determining the right answer without having to even click through to another site. The challenge will increase as queries increasingly move from text to voice-controlled technology.

But even big changes aren’t likely to register with the masses, he conceded.

“Most ranking changes the average person does not notice, other than the sucking feeling that their searches were better,” said Nayak.

“You don’t have the comparison of what didn’t work yesterday and what does work today,” said Ben Gomes, senior vice president of search.

BERT, said Nayak, may be able to determine that a phrase such as “math practice books for adults” likely means the user wants to find math books that adults can use, because of the importance of the word “for.” A prior version of the search engine displayed a book result targeted for “young adults,” according to a demonstration he gave.

Google is rolling out the new algorithm to U.S. users in the coming weeks, the company said. It will later offer it to other countries, though it didn’t offer specifics on timing.

The changes suggest that even after 20 years of data collection and Google’s dominance of search — with about 90 percent market share — Web searches may best be thought of as equal parts art and science. Nayak pointed to examples like searches for how to park a car on a hill with no curb or whether a Brazilian needs a visa to travel to the United States as yielding less than satisfactory results without the aide of the BERT software.

To test BERT, Google turned to its thousands of contract workers known as “raters,” Nayak said, who compared results from search queries with and without the software. Over time, the software learns when it needs to read entire phrases versus just keywords. About 15 percent of the billions of searches conducted each day are new, Google said.

Google said it also considers other input, such as whether a user tries rephrasing a search term rather than initially clicking on one of the first couple of links.

Nayak and Gomes said they didn’t know whether BERT would be used to improve advertising sales that are related to search terms. Advertising accounts for the vast majority of Google’s revenue.

[Source: This article was published inunionleader.com By Greg Bensinger - Uploaded by the Association Member: Jeremy Frink]

Categorized in Search Engine

A Boolean search, in the context of a search engine, is a type of search where you can use special words or symbols to limit, widen, or define your search.

This is possible through Boolean operators such as ANDORNOT, and NEAR, as well as the symbols + (add) and - (subtract).

When you include an operator in a Boolean search, you're either introducing flexibility to get a wider range of results, or you're defining limitations to reduce the number of unrelated results.

Most popular search engines support Boolean operators, but the simple search tool you'll find on a website probably doesn't.

Boolean Meaning

George Boole, an English mathematician from the 19th century, developed an algebraic method that he first described in his 1847 book, The Mathematical Analysis of Logic and expounded upon in his An Investigation of the Laws of Thought (1854).

Boolean algebra is fundamental to modern computing, and all major programming languages include it. It also figures heavily in statistical methods and set theory.

Today's database searches are largely based on Boolean logic, which allows us to specify parameters in detail—for example, combining terms to include while excluding others. Given that the internet is akin to a vast collection of information databases, Boolean concepts apply here as well.

Boolean Search Operators

For the purposes of a Boolean web search, these are the terms and symbols you need to know:

Boolean Operator Symbol Explanation Example
AND + All words must be present in the results football AND nfl
OR Results can include any of the words paleo OR primal
NOT - Results include everything but the term that follows the operator  diet NOT vegan
NEAR The search terms must appear within a certain number of words of each other swedish NEAR minister

Note: Most search engines default to using the OR Boolean operator, meaning that you can type a bunch of words and it will search for any of them, but not necessarily all of them.

Tips: Not all search engines support these Boolean operators. For example, Google understands - but doesn't support NOT. Learn more about Boolean searches on Google for help.

Why Boolean Searches Are Helpful

When you perform a regular search, such as dog if you're looking for pictures of dogs, you'll get a massive number of results. A Boolean search would be beneficial here if you're looking for a specific dog breed or if you're not interested in seeing pictures for a specific type of dog.

Instead of just sifting through all the dog pictures, you could use the NOT operator to exclude pictures of poodles or boxers.

A Boolean search is particularly helpful after running an initial search. For instance, if you run a search that returns lots of results that pertain to the words you entered but don't actually reflect what you were looking for, you can start introducing Boolean operators to remove some of those results and explicitly add specific words.

To return to the dog example, consider this: you see lots of random dog pictures, so you add +park to see dogs in parks. But then you want to remove the results that have water, so you add -water. Immediately, you've cut down likely millions of results.

More Boolean Search Examples

Below are some more examples of Boolean operators. Remember that you can combine them and utilize other advanced search options such as quotes to define phrases.

AND

free AND games

Helps find free games by including both words.

"video chat app" iOS AND Windows

Searches for video chat apps that can run on both Windows and iOS devices.

OR

"open houses" saturday OR sunday

Locate open houses that are open either day.

"best web browser" macOS OR Mac

If you're not sure how the article might be worded, you can try a search like this to cover both words.

NOT

2019 movies -horror

Finds movies mentioning 2019, but excludes all pages that have the word horror.

"paleo recipes" -sugar

Locates web pages about paleo recipes but ensures that none of them include the word sugar.

Note: Boolean operators need to be in all uppercase letters for the search engine to understand them as an operator and not a regular word.

[Source: This article was published in lifewire.com By Tim Fisher - Uploaded by the Association Member: Jason bourne] 

Categorized in Research Methods

[Source: This article was published in nakedsecurity.sophos.com By Mark Stockley - Uploaded by the Association Member: Deborah Tannen]

The history of computing features a succession of organisations that looked, for a while at least, as if they were so deeply embedded in our lives that we’d never do without them.

IBM looked like that, and Microsoft did too. More recently it’s been Google and Facebook.

Sometimes they look unassailable because, in the narrow territory they occupy, they are.

When they do fall it isn’t because somebody storms that territory, they fall because the ground beneath them shifts.

For years and years Linux enthusiasts proclaimed “this will be the year that Linux finally competes with Windows on the desktop!”, and every year it wasn’t.

But Linux, under the brand name Android, eventually smoked Microsoft when ‘Desktop’ gave way to ‘Mobile’.

Google has been the 800-pound gorilla of web search since the late 1990s and all attempts to out-Google it has failed. Its market share is rock solid and it’s seen off all challengers from lumbering tech leviathans to nimble and disruptive startups.

Google will not cede its territory to a Google clone but it might one day find that its territory is not what it was.

The web is getting deeper and darker and Google, Bing and Yahoo don’t actually search most of it.

They don’t search the sites on anonymous, encrypted networks like Tor and I2P (the so-called Dark Web) and they don’t search the sites that have either asked to be ignored or that can’t be found by following links from other websites (the vast, virtual wasteland known as the Deep Web).

The big search engines don’t ignore the Deep Web because there’s some impenetrable technical barrier that prevents them from indexing it – they do it because they’re commercial entities and the costs and benefits of searching beyond their current horizons don’t stack up.

That’s fine for most of us, most of the time, but it means that there are a lot of sites that go un-indexed and lots of searches that the current crop of engines are very bad at.

That’s why the US’s Defence Advanced Research Projects Agency (DARPA) invented a search engine for the deep web called Memex.

Memex is designed to go beyond the one-size-fits-all approach of Google and deliver the domain-specific searches that are the very best solution for narrow interests.

In its first year it’s been tackling the problems of human trafficking and slavery – things that, according to DARPA, have a significant presence beyond the gaze of commercial search engines.

When we first reported on Memex in February, we knew that it would have potential far beyond that. What we didn’t know was that parts of it would become available more widely, to the likes of you and me.

A lot of the project is still somewhat murky and most of the 17 technology partners involved are still unnamed, but the plan seems to be to lift the veil, at least partially, over the next two years, starting this Friday.

That’s when an initial tranche of Memex components, including software from a team called Hyperion Gray, will be listed on DARPA’s Open Catalog.

The Hyperion Gray team described their work to Forbes as:

Advanced web crawling and scraping technologies, with a dose of Artificial Intelligence and machine learning, with the goal of being able to retrieve virtually any content on the internet in an automated way.

Eventually our system will be like an army of robot interns that can find stuff for you on the web, while you do important things like watch cat videos.

More components will follow in December and, by the time the project wraps, a “general purpose technology” will be available.

Memex and Google don’t overlap much, they solve different problems, they serve different needs and they’re funded in very different ways.

But so were Linux and Microsoft.

The tools that DARPA releases at the end of the project probably won’t be a direct competitor to Google but I expect they will be mature and better suited to certain government and business applications than Google is.

That might not matter to Google but there are three reasons why Memex might catch its eye.

The first is not news but it’s true none the less – the web is changing and so is internet use.

When Google started there was no Snapchat, Bitcoin or Facebook. Nobody cared about the Deep Web because it was hard enough to find the things you actually wanted and nobody cared about the Dark Web (remember FreeNet?) because nobody knew what it was for.

The second is this statement made by Christopher White, the man heading up the Memex team at DARPA, who’s clearly thinking big:

The problem we're trying to address is that currently access to web content is mediated by a few very large commercial search engines - Google, Microsoft Bing, Yahoo - and essentially it's a one-size fits all interface...

We've started with one domain, the human trafficking domain ... In the end we want it to be useful for any domain of interest.

That's our ambitious goal: to enable a new kind of search engine, a new way to access public web content

And the third is what we’ve just discovered – Memex isn’t just for spooks and G-Men, it’s for the rest of us to use and, more importantly, to play with.

It’s one thing to use software and quite another to be able to change it. The beauty of open-source software is that people are free to take it in new directions – just like Google did when it picked up Linux and turned it into Android.

Categorized in Search Engine

[Source: This article was published in seroundtable.com By Barry Schwartz - Uploaded by the Association Member: Bridget Miller]

Google's John Mueller said it again, do not worry about words or keywords in the URLs. John responded to a recent question on Twitter saying "I wouldn't worry about keywords or words in a URL. In many cases, URLs aren't seen by users anyway."

oliver

It references that video from Matt Cutts back in 2009 where it says keywords play a small role in rankings, but really small.

In 2017, John Mueller said keywords in URLs are overrated and that it is a small ranking factor back in 2016.

Forum discussion at Twitter.

Categorized in Search Engine

[Source: This article was published in searchengineland.com By Awario - Uploaded by the Association Member: Robert Hensonw]

Boom! Someone just posted a tweet praising your product. On the other side of the world, an article featuring your company among the most promising startups of 2019 was published. Elsewhere, a Reddit user started a thread complaining about your customer care. A thousand miles away, a competitor posted an announcement about a new product they are building. 

What if you (and everyone on your team, from Social Media to PR to Product to Marketing) could have access to that data in real time?

That’s exactly where social listening steps in.

What is social media listening?

Social listening is the process of tracking mentions of certain words, phrases, or even complex queries across social media and the web, followed by an analysis of the data.

A typical word to track would be a brand name, but the possibilities of social media monitoring go way beyond that: you can monitor mentions of your competitors, industry, campaign hashtags, and even search for people who’re looking for office space in Seattle if that’s what you’re after.

Despite its name, social listening isn’t just about social media: many listening tools also monitor news websites, blogs, forums, and the rest of the web.

But that’s not the only reason why the concept can be confusing. Social listening goes by many different names: buzz analysis, social media measurement, brand monitoring, social media intelligence… and, last but not least, social media monitoring. And while these terms don’t exactly mean the same thing, you’ll often see them used interchangeably today.

The benefits of social listening

The exciting thing about social media listening is that it gives you access to invaluable insights on your customers, market, and competition: think of it as getting answers to questions that matter to your business, but without having to ask the actual questions.

There’s an infinite number of ways you can use this social media data; here’re just a few obvious ones.

1. Reputation management.

A sentiment graph showcasing a reputation crisis. Screenshot from Awario.

This is one of the most common reasons companies use social listening. Businesses monitor mentions of their brand and products to track brand health and react to changes in the volume of mentions and sentiment early to prevent reputation crises.

2. Competitor analysis.

Social media share of voice for the airlines. Screenshot from the Aviation Industry 2019 report.

Social media monitoring tools empower you with an ability to track what’s being said about your competition on social networks, in the media, on forums and discussion boards, etc. 

This kind of intelligence is useful at every step of competitor analysis: from measuring Share of Voice and brand health metrics to benchmark them against your own, to learning what your rivals’ customers love and hate about their products (so you can improve yours), to discovering the influencers and publishers they partner with… The list goes on. For more ways to use social media monitoring for competitive intelligence, this thorough guide to competitor analysis comes heavily recommended.

3. Product feedback.

The topic cloud for Slack after its logo redesign. Screenshot from Awario.

By tracking what your clients are saying about your product online and monitoring key topics and sentiment, you can learn how they react to product changes, what they love about your product, and what they believe is missing from it. 

As a side perk, this kind of consumer intelligence will also let you learn more about your audience. By understanding their needs better and learning to speak their language, you’ll be able to improve your ad and website copy and enhance your messaging so that it resonates with your customers.

4. Customer service.

Recent tweets mentioning British Airways. Screenshot from Awario.

Let’s talk numbers.

Fewer than 30% of social media mentions of brands include their handle — that means that by not using a social listening tool you’re ignoring 70% of the conversations about your business. Given that 60% of consumers expect brands to respond within an hour and 68% of customers leave a company because of its unhelpful (or non-existent) customer service, not reacting to those conversations can cost your business actual money.

5. Lead generation.

Social media leads for smartwatch manufacturers. Screenshot from Awario.

While lead generation isn’t the primary use case for most social listening apps, some offer social selling add-ons that let you find potential customers on social media. For the nerdy, Boolean search is an extremely flexible way to search for prospects: it’s an advanced way to search for mentions that uses Boolean logic to let you create complex queries for any use case. Say, if you’re a NYC-based insurance company, you may want to set up Boolean alerts to look for people who’re about to move to New York so that you can reach out before they’re actually thinking about insurance. Neat, huh? 

6. PR.

Most influential news articles about KLM. Screenshot from Awario.

Social listening can help PR teams in more than one way. First, it lets you monitor when press releases and articles mentioning your company get published. Second, PR professionals can track mentions of competitors and industry keywords across the online media to find new platforms to get coverage on and journalists to partner with.

7. Influencer marketing.

Top influencers for Mixpanel. Screenshot from Awario.

Most social media monitoring tools will show you the impact, or reach, of your brand mentions. From there, you can find who your most influential brand advocates are. If you’re looking to find new influencers to partner with, all you need to do is create a social listening alert for your industry and see who the most influential people in your niche are. Lastly, make sure to take note of your competitors’ influencers — they will likely turn out to be a good fit for your brand as well.

8. Research.

Analytics for mentions of Brexit over the last month. Screenshot from Awario.

Social listening isn’t just for brands — it also lets you monitor what people are saying about any phenomenon online. Whether you’re a journalist writing an article on Brexit, a charity looking to evaluate the volume of conversations around a social cause, or an entrepreneur looking to start a business and doing market research, social listening software can help.

 

3 best social media listening tools

Now that we’re clear on the benefits of social media monitoring, let’s see what the best apps for social listening are. Here are our top 3 picks for every budget and company size.

1. Awario

Awario is a powerful social listening and analytics tool. With real-time search, a Boolean search mode, and extensive analytics, it’s one of the most popular choices for companies of any size.

Awario offers the best value for your buck. With it, you’ll get over 1,000 mentions for $1 — an amazing offer compared to similar tools. 

Key features: Boolean search, Sentiment Analysis, Topic clouds, real-time search.

Supported platforms: Facebook, Instagram, Twitter, YouTube, Reddit, news and blogs, the web.

Free trial: Try Awario free for 7 days by signing up here.

Pricing: Pricing starts at $29/mo for the Starter plan with 3 topics to monitor and 30,000 mentions/mo. The Pro plan ($89/mo) includes 15 topics and 150,000 mentions. Enterprise is $299/mo and comes with 50 topics and 500,000 mentions. If you choose to go with an annual option, you’ll get 2 months for free. 

2. Tweetdeck

TweetDeck is a handy (and free) tool to manage your brand’s presence on Twitter. It lets you schedule tweets, manage several Twitter accounts, reply to DMs, and monitor mentions of anything across the platform — all in a very user-friendly, customizable dashboard. 

For social media monitoring, TweetDeck offers several powerful ways to search for mentions on Twitter with a variety of filters for you to use. You can then engage with the tweets without leaving the app. 

TweetDeck is mostly used for immediate engagement — the tool doesn’t offer any kind of analytics.

Key features: User-friendly layout, ability to schedule tweets, powerful search filters.

Supported platforms: Twitter.

Free trial: N/A

Pricing: Free.

3. Brandwatch

Brandwatch is an extremely robust social media intelligence tool. It doesn’t just let you monitor brand mentions on social: the tool comes with image recognition, API access, and customizable dashboards that cover just about any social listening metric you can think of. 

Brandwatch’s other product, Vizia, offers a way to visualize your social listening data and even combine it with insights from a number of other sources, including Google Analytics.

Key features: Powerful analytics, exportable visualizations, image recognition.

Supported platforms: Facebook, Twitter, Instagram, YouTube, Pinterest, Sina Weibo, VK, QQ, news and blogs, the web.

Free trial: No.

Pricing: Brandwatch is an Enterprise-level tool. Their most affordable Pro plan is offered at $800/month with 10,000 monthly mentions. Custom plans are available upon request.

Before you go

Social media is an invaluable source of insights and trends in consumer behavior but remember: social listening doesn’t end with the insights. It’s a continuous learning process — the end goal of which should be serving the customer better.

Categorized in Social
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