Social Media’s Direct Influence on Search Engine Ranking

Social media marketing is becoming more of an accepted part of the online marketing mix because of its ability to drive website traffic and inbound links to sites. Techniques like link baiting, Digg-baiting, and submitting sites to bookmarking or social voting services like Del.icio.us, Digg, StumbleUpon and Reddit have been known to achieve both short and long term value to the sites targeted by such campaigns.

Sometimes however, the short term effects can outweigh the long term effects, and if a social media campaign does not end up with a lot of external links which help with search engine rankings, the end result can be criticism. Complaints about social media marketing such as “none of the links point to the homepage”, “the traffic is worthless”, or “it’s just the flavor of the month” are tremendously overshadowed and cast aside by some new developments in the major search engines.

Social media marketing has a direct effect on search engine rankings and engines are using social voting systems and bookmarking trends to determine quality sites.

In an effort to open the eyes of the search and online marketing community to the importance of social media in the online marketing mix, I am going to discuss :

• Current trends with Google and Yahoo giving higher ranking to sites or content based upon social media voting.
• Ways search engines can use Bookmarking and Social News service to better their results.Patents the two search engines have filed which support the expansion of these trends.
• Steps you can take for basic social media marketing which will help with your current & future search rankings.

Social Media Reviews & Stats Driving Top Search Results

Google Ranking Videos in First Page Results

Social media voting and user views are currently taking a direct effect on Google’s page one search engine results. Google Universal Search melds the Google Web, Image, Video, News, Blog and other vertical search results into one page of relevant information.

At Search Engine Strategies San Jose, Sherwood Stranieri of Catalyst Online looked at the Google results for Criss Angel, the popular illusionist. When currently performing a search on Google for Criss Angel, 4 of the top 10 search results are video results which are housed at YouTube and MetaCafe.

If you compare the traditional SEO stats for the videos, you’ll see that the pages with the highest PageRank or incoming links are not ranked first:

1. YouTube Criss Angel Video : PageRank 3, 120 inbound links
2. YouTube Criss Angel Video 2 : PageRank 3, 246 inbound links
3. Metacafe Criss Angel Video : PageRank 5, 340 inbound links
4. Metacafe Criss Angel Video 2 : PageRank 4, 214 inbound links

However, if you look at the social video variables of views and comments, those rankings make much more sense:

1. YouTube Criss Angel Video : 5.4million views, 10k comments
2. YouTube Criss Angel Video 2 : 2.3 million views, 4k comments
3. Metacafe Criss Angel Video : 17 million views, 416 comments
4. Metacafe Criss Angel Video 2 : 3 million views, 478 comments

Comments are playing a very important factor in the ranking of these videos, as are views. User generated comments, essentially reviews of the media, have a direct impact on Google first page rankings. If your business uses successful viral web video for its marketing, then comments will naturally come. In addition, this can also work against a brand’s image; just do a search on Google for ‘mentos’.

Note : Even if your business does not use video uploads to YouTube or other media outlets to market your brand or services, keep in mind that the use of comments as a ranking algorithm may not always be limited to video. More and more sites are implementing user comments as a form of feedback, communication and community building. Google could easily determine that quality comments on news sites or blogs can make a difference in search engine results; which is even more reason to get your readers to contribute.

Yahoo Ranking Restaurants & Hotels By Reviews

For a more concrete example of how social media comments, reviews and rankings are currently influencing search engine results, let’s take a look at local business searches on Yahoo Search and its use of Restaurant & Hotel Shortcuts.

Yahoo Local prides itself on search relevancy based upon social media participation, and the Restaurants Shortcut on Yahoo Search is reflective of this.

As an example, when a search is performed on Yahoo for ‘Tampa Restaurants‘ a Shortcut is served with links to restaurant categories, neighborhoods and restaurants ranked by the number of user ratings & reviews.

As you can see, Bern’s Steak House is ranked #1 on this Yahoo Search because it has the most user ratings. Essentially, if their competition wanted to overtake the top position, they could easily do so via social media marketing : incentivizing or motivating their customer base to rate and write reviews about their restaurant and services in their Yahoo Local profile. For example, Charley’s Steak House needs only 22 ratings to top Bern’s in the Tampa results. If I were their marketing manager, I would take full initiative to do so and rank #1 on not only Yahoo, but also Google which aggregates business reviews from different local social sites including CitySearch, AOL and TripAdvisor.

Social Bookmarks and Voting’s Influence on Search Rankings

What Social Media Can Bring to Search

The examples of Yahoo user reviewed restaurant searches or the ranking of videos in Google search are current uses of social media’s measurrestaurantsable variables of reviews, ratings, comments and/or video plays being a critical part of these searches, but social media goes far beyond restaurants and videos.

Monitoring social bookmarking services like Del.icio.us, StumbleUpon and Ma.gnolia can help search engines in multiple ways by:

• Indexing Sites Faster : Humans bookmark sites launched by their friends or colleagues before a search engine bot can find them.
• Deeper Indexing : Many pages bookmarked are deep into sites and sometimes not as easily linked to by others, found via bad or nonexistent site navigation or linked to from external pages.
• Defining Quality : If someone takes the time to bookmark a site, it usually has some quality to it.
• Measuring Quality : Essentially if more users bookmark a page, the more quality and relevance that site has. A site with multiple bookmarks across multiple bookmarking services by multiple users is much more of an authority than a site with only several bookmarks by the same user.
• External Meta Data : Users who bookmark sites tag them with keywords and descriptions which add an honest and unbiased definition which is created by the public and not the owner of the site.
• Co Citation : Social bookmarking sites tend to categorize sites and pages based upon the tags used by humans to describe the site; therefore search algortihms can classify these sites with their peers.

In addition, by indexing the social measurement variables such as commenting and votes at Digg, Reddit, Netscape and various niche oriented (all of those Pligg powered hubs), search engine algorithms can also benefit from social news sharing sites by:

• Number of Votes : Similar to the number of bookmarks, the more votes a page receives on Digg or Reddit, the more useful that information usually is. If the same page receives multiple votes across multiple social news voting sites, the higher quality the site.
• Categorization : Like Co Citation, categorization can help define the subject of a site, therefore better helping the engine address searcher intent.
• Commenting : The number of comments can be compared to the number of votes, the higher the comment to voting ratio, the more relevant the news story or site was to the user; therefore, more relevant to the searcher.
• Relevant Sites : Techmeme and Netscape (and hopefully soon Digg) suggest relevant pages and sites to the stories which make their ‘popular’ categorical pages via intra-linking or blog index monitoring. Engines can learn from these projects to help users find alternative or relevant selections in their search results.

To the best of my knowledge, no search engines are currently implementing all of these theories into their current algorithms, but we cannot overlook that Google has been a partner of Digg for years, Microsoft will soon be, Yahoo owns Del.icio.us and AOL runs Netscape. These social services also offer API’s and are completely open to search spidering, which makes these variables mentioned above available to search engines for the taking.

Patents : Search Engines Using Social Media

In order to gather a more definitive scenario of how the major search engines will take advantage of social media metrics to serve more relevant results to the end user, I spoke with Bill Slawski of SEObytheSEA about patent applications filed by Google and Yahoo and Bill supplied the following :

Yahoo Social Media & Search Patents

1. Search using graph colorization and personalized bookmark processing

In a search processing system, identifying input authority weights for a plurality of pages, wherein an input authority weight represents a user’s weight of a page in terms of interest; distributing a page’s input authority weight over one or more pages that are linked in a graph to the page; and using a resulting authority weight for a page in effecting a search result list. The search result list might comprise one or more of reordering search hits and highlighting search hits.

This can be applied to a single user, or to a social network of users. See the section starting with: [0122] Application to Personalization

2. Systems and methods for collaborative tag suggestions

Discusses services like Flickr, del.icio.us, and Yahoo’s My Web 2.0, and a “goodness” measure to find the best tags to annotate different pages and objects (such as images and videos).

The suggested collaborative tags can be selected by a user to annotate content items found in a corpus of documents (e.g., the World Wide Web). As used herein, the term “annotation” refers generally to any descriptive and/or evaluative metadata related to a Web object (e.g., a Web page or site) that is collected from a user and thereafter stored in association with that user or object. Annotations may include various fields of metadata, such as a rating (which may be favorable or unfavorable) of the document, a list of keywords identifying a topic (or topics) of the document, a free-text description of the document, and/or other fields. An annotation may advantageously be collected from a user of the corpus and stored in association with an identifier of the user who created the annotation and an identifier of the document (or other content item) to which it relates.

3. Search engine with augmented relevance ranking by community participation

Shows aspects of a personalized Yahoo Search based upon user tagging and annotation of web pages, and trusted social networks. Trust ratings may be given to users of the social network, and may be used in a dual Trustrank system that provides a Trustrank value for pages and domains based upon the reputation of people bookmarking, visiting, saving, tagging, and annotating those pages.

4. * Search systems and methods with integration of user annotations
* Search systems and methods with integration of aggregate user annotations
* Search system and methods with integration of user annotations from a trust network

These appear to be related to Yahoo’s “My Web” pages, which allow people to bookmark and annotate pages. The abstract of the third patent application listed:

Computer systems and methods incorporate user annotations (metadata) regarding various pages or sites, including annotations by a querying user and by members of a trust network defined for the querying user into search and browsing of a corpus such as the World Wide Web. A trust network is defined for each user, and annotations by any member of a first user’s trust network are made visible to the first user during search and/or browsing of the corpus. Users can also limit searches to content annotated by members of their trust networks or by members of a community selected by the user.

5. Using community annotations as anchortext

Personalized information may be treated in a manner similar to other information comprising a content item for indexing, searching and ranking purposes. For example, personalized information such as annotations and tags may be treated similar to anchortext from a web page. Personalized information, like anchortext, includes descriptive text, but is created by individuals other than the author of a content item. Furthermore, personalized information provides descriptions, opinions and alternate forms of references (including spelling and word form variations) that might not be found in the original content item.

6. Interestingness ranking of media objects

An “interestingness” score might be created for images on Flickr, based upon user actions related to that image,quantity of user entered and edited metadata, access patterns for the pictures, time, system settings, and the relationship of the user to the poster of the image.

[0038] The quantity of user-entered metadata may include, for example, parameters such as the number of tags, comments and/or annotations assigned to the media object, and/or the number of users who have added the media object to their favorites/bookmarks. (Adding an audio media object to a user’s favorites may include adding the media object to a user’s playlist.) Alternatively or in addition to those parameters, the quantity of user-entered metadata may be user-related and include, for example, the number of users who have added tags, comments and/or annotations to the media object, and/or added the media object to their favorites/bookmarks.

Google Social Media & Search Patents

7. Methods and systems for personalized network searching

Describes how a person’s bookmarks and annotations for those bookmarks (and perhaps ratings based upon those bookmarks) may be used to rerank pages for personalized search. Bookmarks can also be shared:

In one embodiment, a user may share or overlay bookmarks. For example in one embodiment, a user is able to open up their bookmarks for others to view. In another embodiment, a user is able to aggregate other users’ bookmarks into their own set of bookmarks (either via copying or via an overlaid reference semantics). Such a feature may prove useful for community building (e.g., “Add this group’s bookmarks to your favorites” when joining a new mailing list). In one such embodiment, the bookmark indicators in results pages distinguish between those pages explicitly bookmarked by the user from those gathered by others. Given a canonical URL through which to reference another individual/organization’s bookmarks, the service provider can derive a sense of the popularity of a person’s links and weight those bookmarks correspondingly (a la PageRank applied to the subgraph of bookmark interlinks).

[0069] One embodiment of the present invention fosters community and relationship building. In one embodiment, the search engine is able to recognize clusters or pairs of users having similar interests. Such an embodiment is able to suggest other users with which to network.

8. Information retrieval based on historical data

[0114] According to an implementation consistent with the principles of the invention, user maintained or generated data may be used to generate (or alter) a score associated with a document. For example, search engine 125 may monitor data maintained or generated by a user, such as “bookmarks,” “favorites,” or other types of data that may provide some indication of documents favored by, or of interest to, the user. Search engine 125 may obtain this data either directly (e.g., via a browser assistant) or indirectly (e.g., via a browser). Search engine 125 may then analyze over time a number of bookmarks/favorites to which a document is associated to determine the importance of the document.

[0115] Search engine 125 may also analyze upward and downward trends to add or remove the document (or more specifically, a path to the document) from the bookmarks/favorites lists, the rate at which the document is added to or removed from the bookmarks/favorites lists, and/or whether the document is added to, deleted from, or accessed through the bookmarks/favorites lists. If a number of users are adding a particular document to their bookmarks/favorites lists or often accessing the document through such lists over time, this may be considered an indication that the document is relatively important. On the other hand, if a number of users are decreasingly accessing a document indicated in their bookmarks/favorites list or are increasingly deleting/replacing the path to such document from their lists, this may be taken as an indication that the document is outdated, unpopular, etc. Search engine 125 may then score the documents accordingly.

9. Methods and systems for improving a search ranking using article information

Systems and methods that improve client-side searching are described. In one aspect, a system and method for receiving a search query, determining a relevant article associated with the search query, and determining a ranking score for the relevant article based at least in part on client-side behavior data associated with the relevant article is described.

Many different aspects of user behavior are viewed in this document to determine a ranking score for articles, such as how often the article is accessed, or printed, or how far someone scrolls down a page. Bookmarking activity is described in this section:

[0044] Block 211 is followed by block 212, in which book-marking data associated with an article is determined. Book-marking information may comprise, for example, information about book marking of an internet URL, book marking within a text article to other portions of the same article or of a separate article, how many bookmarks are connected with a particular article, the textual content of the book mark associated with the article, or any other information relating to book marks associated with the article or article.

10. Entity Display Priority in a Distributed Geographic Information System

Annotations from users may be helpful in coming up with an “interestingness” ranking that determines whether placemarks are shown for certain locations on Google Maps or Google Earth.

Decentralized Web Annotation : Describes a way of allowing people to annotate Webpages from within their blogposts. Might be something we may see someday at Blogger.

Implementing Social Media Marketing into a Search Marketing or Link Building Campaign

The above information proves that social media attributes have a direct effect on search engine ranking. Here are some basic social media marketing techniques and resources on social media marketing from Search Engine Journal and other informative blogs.

Link Building : As we have discussed above, social media marketing plays an essential role in current and future search engine rankings, from bookmarks to link building. Oh yes, why link building? Because the site profile pages on these social sites usually pass link juice (especially Netscape) and also get highly indexed in search engines themselves; especially for longtail terms.

For more information on Link Building via blogs, social news sites and bookmarking see:

• Link Building on Social Networks
• Quality Link Building : Editorial Links vs. Acquired Links
• Yahoo Answers : Brand & Link Building via Social Media
• Dig It – There’s More To Social Media Link Building Than Digg
• Social Media Optimization : 3 Rules for What Not to Do

Local Reviews : If your business has a storefront like a restaurant, hotel or specialty boutique would, motivate customers to rate your services on Yahoo Local, CitySearch, TripAdvisor and other business rating services. As shown above, having more reviews that your competition will help achieve top search rankings.

For more information on enhancing your search visibility with reviews and ratings please see:

• Local Search : User Recommendations Make Your Business Money
• 3 Reasons Yahoo! Local Search Can Dominate Google
• RatingParadise.com : Lists of Guide Sites Offering User Reviews
• WikiIDWeb : User Review Sites

Submit to StumbleUpon : StumbleUpon encourages site owners to submit their own sites and pages for the StumbleUpon community to rate and review. If they don’t like your site, you won’t get much traffic. But if the community does like it, expect thousands upon thousands of users to visit and review your site themselves.

For more information on StumbleUpon please see:

• Comprehensive Guide to StumbleUpon
• The Unappreciated StumbleUpon Effect
• Why, Who and How About StumbleUpon
• Build Super Links and Traffic With StumbleUpon

Submit to Digg, Netscape and Reddit : If you have a good piece of content which is attractive to these communities, submit the story yourself, ask a friend, or a top user in these communites. But do this sparsely, too many submitals of your own stuff or useless information can lead to you or your site being banned by these services.

For more information on marketing via these sites see:

• Beginner’s Guide to Digg
• Using Digg and Netscape to Get Traffic
• How to Write the Right Title for Digg
• Sex Sells, Especially on Digg!
• Directory of Niche Pligg Powered Sites

Please feel free to share your thoughts on social media’s influence on search engine rankings in the comments below.