When it comes to social networks we all have access to a living lab. Everything that happens there can be deconstructed and provide valuable insights on how we connect and what drives us. The truth is that while many of us spend an incredible amount of time sharing information and interacting in environments such as Twitter, Facebook and Google Plus, we know next to nothing about that motivates us there. Why do we share some content and not others? Why do some posts leave us indifferent while others get us all fired up?
The question as to whether a strong social signal helps your website rank in search has been at the very top of the questions asked around semantic search. While the intuitive answer is that “yes, social signals should play a role” there are some practical considerations that prevent that from happening and which Google’s Matt Cutts addressed in a short video in January 2014:
Matt’s explanations feed directly into what Google representatives have repeatedly said, at conferences I have been present at where they have explained that social signals are “dirty signals” and “weak” at best because Google hasn’t got sufficient access or cannot always fathom the intent of the posts it crawls.
Incidentally, in the video above Matt talks about semantic search’s ability to extract a person’s identity from their social network profiles even when no real name or apparently other identifying information is mentioned.
While social signals ‘weak’ or otherwise are not a ranking factor that does not mean they do not impact on search and do not help raise visibility both of personal profiles and the websites associated with them.
One way Google might do it is through the use of machine learning applying algorithms to Twitter that operate under the premise of not-so-distant supervision.
For the record, unsupervised indexing of Tweets leads to the accumulation of errors that eventually deprecate the indexing record. Supervised indexing, where human testers actively sample and correct the tagging of the indexed corpus is also not without problems as incorrect assumptions, human error and even false positive taggings lead to drops in accuracy.
Google’s proposed approach of “adapting taggers to Twitter with not-so-distant supervision” takes a hybrid approach that appears to resolve the majority of problems associated with correctly understanding what a Tweet means and how important it is in relation to other things happening on the web. A “tag” is a piece of metadata that allows an indexing bot to correctly label a Tweet so that it can be incorporated into Google’s semantic index.
As Google’s own researchers say this is far from easy: “The challenges include dealing with variations in spelling, specific conventions for commenting and retweeting, frequent use of abbreviations and emoticons, non-standard syntax, fragmented or mixed language, etc.” add to this the fact that the 140-character limit, of necessity, creates a homogenous nature to Twitter that makes it hard to separate one Tweet from another in terms of structure and you can begin to see the scope of the challenge.
How Semantic Search Indexes Tweets
Google can get round all these issues by using its superior computer processing power and search to perform a number of tasks:
- Analysis of a Tweet in relation to similarly structured search queries
- Analysis of a Tweet in relation to accelerating content across the web
- Analysis of a Tweet in relation to lexical content found on the website(s) a Tweet links to
- Analysis of the website(s) a Tweet points to
- Correlation of search queries and associated snippets provided by the search engine with the Tweet lexical structure
- Assessment of Click-Through-Rate (CTR) data on specific search queries as a qualitative guide to creating a tag library that can be applied to Tweet indexing
- Extraction of entities in Tweets and matching with known entities indexed
- Analysis of the Tweet in relation to other Tweets of a similar or directly tangential nature trending across Twitter
In other words it is a little like rocket science.
What Do You Need to Do?
If you are serious about leveraging your Twitter presence to gain in semantic search here is what you had to do:
- When you Tweet link to a URL on your site (or a site relevant to your Tweet)
- Ensure that at least 20% of your Tweets contain a URL leading to a specific page on your website
- Make sure the page you Tweet contains sufficient information to allow Google to accurately determine the intent of the Tweet
- Use language in your Tweet that is reflected in the content of the URL you point to
- Because of the way Tweets can be semantically indexed, Twitter is now more closely linked to search than ever. Tweet snippets that could potentially come up in search.
- Frame your Tweets with at least some correlation to potential search queries
- Avoid using syntax in your Tweets that is unusual or hard to fathom
- Create domain authority in your Twitter account by weighing your Tweets with content specific to your expertise
- Make sure that your Tweet and website page have at least one matching word that is not a stopword
- Do not link Tweets to your website’s Home Page, it is always discounted by Google’s indexing
While social signals do not directly act as a ranking signal in search, a strong social signal helps Google establish confidence in its indexing of websites which then does lead to higher visibility in search. Its indexing also allows it to pinpoint what the website does, increasing Google’s understanding of its differentiation from other websites which leads to more accurate matching of website content and search queries input in Google search.
A robust, carefully crafted Twitter presence can significantly aid your visibility in search by helping define your website’s relevance.
Adapting taggers to Twitter with not-so-distant supervision (Google Research)
Simple and knowledge-intensive generative model for entity recognition (Microsoft Research)
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In Understanding The Future of Marketing I explained the dichotomy of technology into Augmented Reality (AR) and Virtual Reality (VR): “Augmented reality uses digital technology to make the real world feel digital, while virtual reality uses digital technology to make the digital world feel real.”