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Why Social Media is NOT a Replacement for Search Optimization University of Washington Digital Communications February 1, 2010
Ubiquitous Agenda Slide Search Engines & the Evolution of the Web Search Distraction Search Ennui Social Media Growth Usage Twitter Facebook Intersection of Search & Social Media New Forms of Relevance Dark Side of Search Consolidation of power Search Engine Bias Resources
Evolution of the Web Google claims to have found 1 trillion URLs There are approximately 320 million new pages added to the Web every week Shelf life of a page is roughly 1 year 80% of links are replaced within 1 year There’s a lot of Web junk out there ,[object Object]
Mostly small changesGooglearchy : dominant Web sites become more firmly entrenched in search results by nature of size Older sites rewarded for longevity Link rich get richer
Search Disorientation There is now too much to choose from in search results Berrypicking on Steroids Searchers do not know “how to search” 56% constructed poor queries Did not suite their information need Were too generic 95% did not use any operators to refine query 92% used information from a single resource 28% had difficulty maintaining orientation on a website Searchers hop-scotching from result to result 33% had difficulty navigating/orienting search results
Search Ennui Over confidence in search effectiveness for all searches 36% did not go beyond the first 3  search results  not pages
results on page 1 91% did not go beyond the first page of search results 55% selected irrelevant results 1 or more times Too lazy to switch? ,[object Object],[object Object]
Key Online Demand Generation Activities Customer Engagement media activates  Search for brand online = 67.2% Social Media Activities Read Blogs: 26.90% Play Company Online Games: 25.30% Post reviews: 35.20%
Twitter The numbers of new people joining Twitter has declined  The level of Twitter engagement is low – a majority of the users follow very few and have low tweet volume
Facebook 350 million active users 65 million mobile users (50% more active than non-mobile users) 70% of users are outside of the U.S. 35 million users update status daily Average user is a member of 12 groups Average user has 130 friends 500,000 active applications  250 applications with more than 1 million active users http://www.facebook.com/press/info.php?statistics
Intersection of Search & Social Media Reveal valuable information about conversations around brand or key phrases related to products & services Terms for optimization Locations where conversation is taking place Trends over time Shared search information Heystakshttp://www.heystaks.com/
How Search Engines Determine Influential Bloggers If a blog post has a large number of inlinks, then it is more likely to be influential.  influence of a blog post depends on the influence of the blog posts referencing it: The more influential the blog posts that refer to this blog post, the more influential this blog post becomes. The influence of a blog post could depend on the amount of discussion it initiated.  Little or no discussion = did not arouse others' interests or their contents were not appealing.  Large number of comments  indicates that the particular blog post affects many people so they care to write comments.   If a blog post refers to many articles or other blog posts, it is less likely to be an innovative piece  Keller and Berry have listed innovativeness as an essential property of being influential in a physical community.  The number of outlinks reflects how innovative a blog post is. An influential blog post is often well written., i.e. eloquent, descriptive, logical, insightful, authoritative, rhetoric, relatively self-contained.  The length of a blog post (ž) is a heuristic measure checking if post is influential or not.
More About Search
New Formulas for Relevance Behavioral: Microsoft: Compares snippets of Web search engine results with data collected from user behavior and client, Google: user bookmarks [online and client] used to construct “personalized search object” that is used to filter Web search results ,[object Object]
Click through
Currency
Time on pageSocial: The wisdom of crowds ,[object Object]
Bookmarks
Sharing
SubscriptionUniversal: text, image, audio, video
Search v.Next Microsoft:Launches “decision engine”  with focus on multiple meaning (contexts) as well as term indexing and topic association and tracking  ,[object Object]
Efficiently Representing Word Sense Probabilities (April 2009)
Tracking Storylines Around a Query (May 2008)

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Uw Digital Communications Social Media Is Not Search

  • 1. Why Social Media is NOT a Replacement for Search Optimization University of Washington Digital Communications February 1, 2010
  • 2. Ubiquitous Agenda Slide Search Engines & the Evolution of the Web Search Distraction Search Ennui Social Media Growth Usage Twitter Facebook Intersection of Search & Social Media New Forms of Relevance Dark Side of Search Consolidation of power Search Engine Bias Resources
  • 3.
  • 4. Mostly small changesGooglearchy : dominant Web sites become more firmly entrenched in search results by nature of size Older sites rewarded for longevity Link rich get richer
  • 5. Search Disorientation There is now too much to choose from in search results Berrypicking on Steroids Searchers do not know “how to search” 56% constructed poor queries Did not suite their information need Were too generic 95% did not use any operators to refine query 92% used information from a single resource 28% had difficulty maintaining orientation on a website Searchers hop-scotching from result to result 33% had difficulty navigating/orienting search results
  • 6.
  • 7. Key Online Demand Generation Activities Customer Engagement media activates Search for brand online = 67.2% Social Media Activities Read Blogs: 26.90% Play Company Online Games: 25.30% Post reviews: 35.20%
  • 8. Twitter The numbers of new people joining Twitter has declined The level of Twitter engagement is low – a majority of the users follow very few and have low tweet volume
  • 9. Facebook 350 million active users 65 million mobile users (50% more active than non-mobile users) 70% of users are outside of the U.S. 35 million users update status daily Average user is a member of 12 groups Average user has 130 friends 500,000 active applications 250 applications with more than 1 million active users http://www.facebook.com/press/info.php?statistics
  • 10. Intersection of Search & Social Media Reveal valuable information about conversations around brand or key phrases related to products & services Terms for optimization Locations where conversation is taking place Trends over time Shared search information Heystakshttp://www.heystaks.com/
  • 11. How Search Engines Determine Influential Bloggers If a blog post has a large number of inlinks, then it is more likely to be influential. influence of a blog post depends on the influence of the blog posts referencing it: The more influential the blog posts that refer to this blog post, the more influential this blog post becomes. The influence of a blog post could depend on the amount of discussion it initiated. Little or no discussion = did not arouse others' interests or their contents were not appealing. Large number of comments indicates that the particular blog post affects many people so they care to write comments. If a blog post refers to many articles or other blog posts, it is less likely to be an innovative piece Keller and Berry have listed innovativeness as an essential property of being influential in a physical community. The number of outlinks reflects how innovative a blog post is. An influential blog post is often well written., i.e. eloquent, descriptive, logical, insightful, authoritative, rhetoric, relatively self-contained. The length of a blog post (ž) is a heuristic measure checking if post is influential or not.
  • 13.
  • 16.
  • 20.
  • 21. Efficiently Representing Word Sense Probabilities (April 2009)
  • 22. Tracking Storylines Around a Query (May 2008)
  • 23.
  • 24. Rendering Context Sensitive Ads for Multi-topic searchers (April 2008)
  • 25. Presentation of Local Results (July 2008)
  • 26. Detecting Novel Content (November 2008)
  • 27. Document Scoring based on Document Content Update (May 2007)
  • 28.
  • 29. Delivering Items Based on Links to Resources Associated with Results (April 2008)
  • 30.
  • 31.
  • 32. Cookie with 5 year expiration
  • 33.
  • 34. Google US: “ Tiananmen Square” Showcasing Google’s preference for Wikipedia Majority of results are on massacre
  • 35. Google China Generic tourism results Tourism photos Filtered from outside as well as inside China Google US
  • 36. Google France Showcase Google’s preference for Wikipedia BBC result News photos document protest General Tourism sites Google US
  • 37. UW Digital Communications 2/10/2010 21 Resources Social Media Usage Statistics http://www.marketing-online.co.uk/wiki/Social_Media_Usage_Statistics Razorfish Digital Feed 2009 http://feed.razorfish.com/feed09/the-data/ Hubspot Twitter Usage Report http://www.hubspot.com/Portals/53/docs/01.10.sot.report.pdf Ignite Social Network Analysis Report http://hbr.org/2010/01/the-hbr-list-breakthrough-ideas-for-2010/ar/1 Identifying Influentials in the Blogsosphere: Agarwal, Liu, Tang Search & Social Media: http://palblog.fxpal.com/?p=2814
  • 38. UW Digital Communications 2/10/2010 22 Thank You Marianne Sweeny Search Services M 206.898.3786 D 425.519.7700 Emarianne.sweeny@ascentium.com E: sweeny48@u.washington.edu

Hinweis der Redaktion

  1. I very much enjoyed speaking to your class and presenting my views on social media, search, their interdependent natures as well as illuminating some of the important issues surrounding optimizing for search engines that marketeers should be prepared to address (or at least know about). You can follow me on Twitter (msweeny) or Linked-in should you wish for a more regular dose of my heresy.
  2. As marketing professionals, search engines will remain the best driver of customers and demand. This is because most of your customer are conditioned to turn to search engines to find information that they know but don’t (i.e. a specific online or physical address, factoids, etc) and information that they need but cannot articulate (opinions or background for decision-making, a more comprehensive view of a particular topic, etc). For the first type of information need, search engines tend to be very efficient as the searcher tends to be more precise in the search terms that they use. The second type of information need is more problematic because searchers don’t’ know what they don’t know and so cannot assist the search engine in retrieving what they need to find out.U.S. Searches (August 2009) – from Searchenginewatch.com10 billion searches total Google: 7 billion for 65% of market shareYahoo: 1.7 billion for 16% market shareBing: 1.2 billion for 10.7% market share
  3. Resources for this slide:Using the Internet: Skill Related Problems in User Online Behavior; van Deursen & van Dijk; 2009System and Method of Encoding and Decoding Variable-length data: June 27, 2006http://www.worldwidewebsize.com/
  4. The Web has grown faster and well beyond what its founders intended. If Tim Berners-Lee knew that we’d be posting billions of KB of video, images, social media posts, Web pages, ecommerce site, etc., he would not have selected HTTP as the hosting protocol. Now there is so much more than text to the online information that we consume and search engines are so lame at indexing and retrieving non-text materials. Added to this problem is the inability of searchers to work WITH the technology. Gone are the days of Academic power-users well-versed in Boolean syntax or Dialog query construction. However, the manner in which we store data has not fundamentally changed since these early days. Yes, there are There is no such thing as “advanced search” longer. We’re all lulled into the false sense that the search engine is smarter enough and we’re all “advanced” searchers as a result.Resources for this slide:Using the Internet: Skill Related Problems in User Online Behavior; van Deursen & van Dijk; 2009
  5. I did a Facebook pool of my contact and the #1 search engine choice overwhelmingly was Google. When pressed to try out another search engine (Yahoo or Bing anyone?) or to justify their initial choice, the response was “No, because Google works best and that is what I want.” Now, there is a slight difference in the ordering of the top 30 results for a query when pushed through the 3 big search engines. Note to Bing: Folks do not want a “decision engine.” They want to find something that looks like what they think that they are looking for but are not sure they will recognize.Resources used for this slide:Using the Internet: Skill Related Problems in User Online Behavior; van Deursen & van Dijk; 2009
  6. Social media has a unique purpose to connect people of like interests. It started with sharing personal experiences with a selected group (IM, FB/My Space, Skype, Twitter, etc) This purpose has extended over time to include business networking (Linked-in, Plaxo, Yammer, etc). Now, many of these have been co-opted for marketing purposes. My belief is that there is more value in treating these as a channel for “listening to” your customers than for outreach. Looking at the data from usage, the #1 activity for social media is not tweeting or playing games on FB, it is posting pictures and watching videos
and they are very discriminating on which videos that they watch. The more that you require from the consumer of a social application, the steeper the drop in participation.At a terrific MIT Forum panel discussion on the uses of social media and there was strong consensus that any marketing initiative should start with the question: “What is in it for the customer?” What do they get that is of value in following you or “letting you” follow them?
  7. Key activity is still passive digestion of online materialsSearch dominates the information finding activity. They start with a search engine and then go to social media from there. This is a symbiotic relationship with search more often than not as the initiator. That is not to say that some will turn to their network for answers some of the time. However, customers generally prefer some Web search experience in a search for information.
  8. The great run up in Twitter adoption has taken on a more realistic turn as the media hype moved to another flavor of connection and the initial adoptees became more discriminating on whom they follow and whom they let “follow” them.Of greater interest is the pattern that we saw in previous studies on social media use. There is a long tail with Twitter that sees a great majority of the users following few and posting few tweets. So, the “key influencers” seem to be tweeting to and for themselves.
  9. Started as a college classroom communication medium. Soon far exceeded its original intent to include college form of My SpaceNow taken over by the masses and used as a marketing communication medium, entertainment dissemination medium Attempts to segment have been unsuccessfulPrivacy concerns caused reputation damage in 2009Many applications but few players – signups do not equal useFundamental question for any application, marketing page or fan club is
.”What’s in it for the customer?”
  10. Search optimization and social media do intersect with concrete value found in the business intelligence that can be gathered and the information discovered from various social networksI follow: Jared Spool, other known information architects and Neil Gamin among others.
  11. With the advent of Authority-based relevance (HITS/Hilltop algorithms), social media became a valuable resource for high quality links. Then Bobo SEO started gaming the system and the links from blogs and forums became less value as they were categorized NOFOLLOW or as the rank of the originating site went down. In the last year+, the search engines have started deploying methods to deflect unnatural optimization and ensure contextual integrity. The value of indexing Twitter feeds remains to be seen.
  12. As marketers, there are nuances to search engine demand generation that you should be aware of. I promote Strategic or Proactive SEO because it is much easier to build an optimized site than to optimize an existing one. “An architect’s most useful tools are an eraser at the drafting board and a wrecking bar at the site.”Frank Lloyd Wright
  13. Search engines reward the legitimate“wisdom of crowds” Helping each other find information with online bookmarking and community sites Technorati, Reddit,TechCrunchDeliciousSearch engines look to agnostic resources for relevance ranking. Wikipedia results always appear at the top of a search results set. Some “X-file conspiracy theorist” (apparently I am one until I find a vindicating resource) believe that this is done by intention on the part of Google. Certainly, the current Google algorithm for determining relevance favors sites like Wikipedia with a contrived sense of agnosticism.
  14. Here is more evidence of the maturing Semantic WebSearch engines are now incorporating information on user behavior, document properties, domain properties and searcher behavior in the calculation of relevance.Watch out for those Facebook applications, quizzes,etc, Tweets, Linked-in dataImproving Search using Population Information (November 2008): Determine population information associated with the query that is derived from a population databaseLocations of usersPopulations that users are associated withGroups users are associated with (gender, shared interests, self- & auto-assigned identity data)Rendering Context Sensitive Ads for Multi-topic searchers (April 2008): Resolves ambiguities by monitoring user behavior to determine specific interest Presentation of Local Results (July 2008): Generating 2 sets of results, one with relevance based on location of device used for searchDetecting Novel Content (November 2008): indentify and assign novelty score to one or more textual sequences for an individual document in a setDocument Scoring based on Document Content Update (May 2007): scoring based on how document updated over time, rate of change, rate of change for anchor-link text pointing to documentDocument Scoring based on Link-based Criteria (April 2007): System to determine time-varying behavior of links pointing to a document ; growth in # of links pointing to the document (exceeds the acceptable threshold), freshness of links, age distribution of links deployed as Google ScoutMicrosoft: Launches “decision engine” with focus on multiple meaning (contexts) as well as term indexing and topic association and tracking-Lead researcher Susan Dumais at the forefront of user behavior for prediction on search relevance-Look to recent acquisition of Powerset (semantic indexing) and FAST ESP (semantic processing)Calculating Valence of Expressions within Docum0ents for Searching a Document Index (March 2009): System for natural language search and sentiment analysis through a breakdown of the valence manipulation in documentEfficiently Representing Word Sense Probabilities (April 2009): Word sense probabilities stored in a semantic index and mapped to “buckets.” Tracking Storylines Around a Query (May 2008): Employ probabilistic or spectral techniques to discover themes within documents delivered over a stream of timeConsolidate the plurality of info around certain subjects (track stories that continue over time)Collect results over time and sort (keeps track of the current themes and alerts to new)TrackRank (relevance)Present abstractsCompares the query with the contents of each document to discover whether query exists implicitly or explicitly in received documentBuilds topic modelsDocument Segmentation based on Visual Gaps (July 2006): Document white space/gaps used to identify hierarchical structure
  15. Some observers claim that Google is now running on as many as a million Linux servers. At the very least, it is running on hundreds of thousands. When you consider that the application Google delivers is instant access to documents and services available from, by last count, more than 81 million independent web servers, we're starting to understand how true it is, as Sun Microsystems co-founder John Gage famously said back in 1984, that "the network is the computer." It took over 20 years for the rest of the industry to realize that vision, but we're finally there. ...First, privacy. Collective intelligence requires the storage of enormous amounts of data. And while this data can be used to deliver innovative applications, it can also be used to invade our privacy. The recent news disclosures about phone records being turned over to the NSA is one example. Yahoo's recent disclosure of the identity of a Chinese dissident to Chinese authorities is another. The internet has enormous power to increase our freedom. It also has enormous power to limit our freedom, to track our every move and monitor our every conversation. We must make sure that we don't trade off freedom for convenience or security. Dave Farber, one of the fathers of the Internet, is fond of repeating the words of Ben Franklin: "Those who give up essential liberty to purchase a little temporary safety deserve neither, and will lose both." Second, concentration of power. While it's easy to see the user empowerment and democratization implicit in web 2.0, it's also easy to overlook the enormous power that is being accrued by those who've successfully become the repository for our collective intelligence. Who owns that data? Is it ours, or does it belong to the vendor? If history is any guide, the democratization promised by Web 2.0 will eventually be succeeded by new monopolies, just as the democratization promised by the personal computer led to an industry dominated by only a few companies. Those companies will have enormous power over our lives -- and may use it for good or ill. Already we're seeing companies claiming that Google has the ability to make or break their business by how it adjusts its search rankings. That's just a small taste of what is to come as new power brokers rule the information pathways that will shape our future world. http://radar.oreilly.com/2006/05/my-commencement-speech-at-sims.htmlMy Commencement Speech at SIMS (May 2006)
  16. Equal Representation By Search Engines: Vaughn & Zhang (2007)