Google is known to be extra careful when returning results for "Your Money or Your Life" terms. It is therefore encouraging that Google's understanding of the topics involved in the black lives matter movement is the strongest we have looked at to dat. Even so, whilst there is a debate to be had around whether this topic should be categorized in society rather than law, our systems categorixed this as a law issue, because the concepts of "justice" and "racism" were prevalent within the content returned. "Justice" and "racism", however, are concepts that Google did not seem to pick up. It was also interesting to note that Google returned severla social media and news sites, demonstrating that it feels the query deserves freshness of results. These were interspersed with four different results from Blacklivesmatter.com, suggesting that Google has made a strong connection between the site and the topic.
Google uses Natural Language Processing (NLP) routines to convert its understanding of text into topics and concepts (Entities). We looked out how much understanding Google misses using this approach in the Law sector.
InLinks analyzed the page content of the top 10 Google results (in the US Market) ranking for the phrase black lives matter and compared the Named Entities recognized by Google’s NLP API with the proprietory routines designed by Inlinks to uncover gaps in Google’s machine learning in the Law sector
The results showed that 36.4% of entities seen on the results in the Law sector SERPS (search engine Results pages) were positively identified by Google.
This compares to 20.7% average across all analyzed industry sectors.
Different sectors tend to be analyzed with a different degree of accuracy by the search engines. This stems from two main challenges.
|Law Industry||Google Analysis||InLinks Analysis|
|Avg. Number of words per page||1565|
|Avg. Number of Topics per page||13.1||36|
|Benchmark - Avg. nb of entities/page (all sectors)||9.4||46.9|
|Of which, Topic types are:|
|- Cities & geo. areas||21||16|
Google’s Search API returned URLs for the following sites competing for this phrase:
#blacklivesmatter, #en.wikipedia, #m4bl, #twitter, #facebook, #instagram, #theguardian
The texts of each page are then sent to Google’s NLP API, in order to determine which entities are identified by the search engine. These are important for search since Google is then able to link these to its Knowledge Graph to feed its services including Google Discover, Google search, Voice Search and Google News. (Although, correct identification does not guarantee inclusion in these results)
Here is first of all the synthesis of the results returned by Google:
Here are some errors in the categorization of entities:
By understanding where Google is failing to recognize important concepts within the industry, there is an opportunity for companies in the sector to write clearer content that Google can better understand.
Another option is to explicitly state these concepts in Webpage schema for machine learning algorithms to take into account. This would require using Schema.org/WebPage and the "about" and mentions" properties for important concepts such as .
Internal linking of topics topages dedicated to each important topic will also help to reduce cannibalisation of content in Google’s understanding of contect within your content.
Google's understanding of the Law market, based on its NLP algorithms remains limited at 36.4% for this industry. Businesses either need to improve their schema or make their content more understandable by Google to improve its level of understanding.
Bureau of Land Management, Black Lives Matter, Killing of George Floyd, Pete Buttigieg, United States, Emergency medical technician, Minneapolis, ZIP Code, South Bend, Indiana, Social media
Black Lives Matter, United States, George Zimmerman, Patrisse Cullors, Trayvon Martin, African Americans, Ferguson, Missouri, Alicia Garza, Movement for Black Lives, Anti-Apartheid Movement, Opal Tometi, Blue Lives Matter, All Lives Matter, Facebook, Shooting of Michael Brown, New York City, George Floyd, Death of Eric Garner, St. Louis County, Missouri, Democratic Party (United States), Minnesota, Minneapolis, Occupy Wall Street, United Kingdom, Australia, Germany, New Zealand, Republican Party (United States), Rudy Giuliani, Black Power, Al Sharpton, Pan-Africanism, Missouri, Palestinians, Israel, National Action Network, Jesse Jackson, Rainbow/PUSH, Manhattan, Union Square, Manhattan
Black Lives Matter, Trayvon Martin, Canada, United Kingdom, United States, ZIP Code
Black Lives Matter, Killing of George Floyd, Nonprofit organization, Alicia Garza, Opal Tometi, Patrisse Cullors, United States, English language, French language, Canada, Spanish language, Brazil, Portuguese language, German language, Minneapolis, YouTube
Black Lives Matter, Trayvon Martin, Dream Defenders, Florida State Capitol, George Zimmerman, Black people, Shooting of Michael Brown
Movement for Black Lives, United States, Black people, White nationalism
African Americans, Black Lives Matter, George Zimmerman, Ferguson unrest, Opal Tometi, Alicia Garza, Patrisse Cullors, Trayvon Martin, St. Louis, Labor Day, Shooting of Michael Brown, Darnell L. Moore, Death of Sandra Bland, Shooting of Walter Scott, Shooting of Tamir Rice, ZIP Code
Black Lives Matter, Twitter, Minneapolis, United States
Login, Instagram, Amplifier, Spanish language, Spain, Portugal, Norwegian language, Malay language, Swedish language, Russian language, Canada
Edward Colston, United Kingdom, Black Lives Matter, United States, Australia, Minneapolis, George Floyd, Keir Starmer, Dan Tehan, Long Bay Correctional Centre, Thomas Chatterton Williams, IndigenousX, The Guardian, Raheem Sterling, London, Manchester City F.C., Newsnight