Here’s a great example of how a seemingly noble social media donation campaign can go awry: Microsoft tweeted two hours ago on the Bing search engine’s Twitter account that it would donate up to $100,000 to help victims of Japan’s earthquake — but only if Twitter users retweeted its original post to broadcast it to their followers, at $1 per retweet.
While many Twitter users are retweeting without complaint, others are pointing out that this campaign seems like a crass marketing opportunity for Bing.
Comedian Michael Ian Black, who has nearly 1.6 million Twitter followers, graphically responded to the campaign with this tweet: “Hey @bing, stop using a tragedy as a fucking marketing opportunity.” Searching for “@Bing” on Twitter at the time of this post reveals plenty of other users who aren’t taking too kindly to Microsoft’s campaign.
This backlash shows us that as useful as social media is for inspiring activism, it must be carefully deployed so as not to seem like a craven publicity stunt. Most recently, we saw a similar backlash to Kenneth Cole’s tweet about Cairo amid Egypt’s recent protests, in which the fashion label plugged its spring collection.
I can’t imagine how Microsoft didn’t see this coming. It would have received plenty of good will by simply donating $100,000 to help out quake victims. This campaign, on the other hand, seems like a forced attempt to get people talking about Bing, and ultimately to get it trending on Twitter. Now people are indeed talking about Bing, but Microsoft likely won’t appreciate what they’re saying.
The Future of Search Series is supported by SES New York Conference & Expo, the search and social marketing conference helping brands, agencies, and professionals connect, share and learn what’s next for the interactive industry.
A “social search” is one that ties a searcher’s social graph to his search queries. With social search, each searcher sees unique results that are shaped by the interests of his social network friends.
Google, not the company to often fudge with the appearance or function of its search results, turned on its version of social search more than a year ago. It has since gone on to more prominently feature social search results and blend them in with regular results.
One should not make light of these changes; they point to the company’s recognition that the average web user, who now spends more time on Facebook, may be not-so-quietly demanding a new form of search.
In fact, Facebook is more than a social network for many these days. It’s the center of our social graph, it’s where we go to find and read the day’s news, it’s how we comment on articles, and its ubiquitous “like” buttons help us refine our interest graphs and are becoming the de facto way for us to voice our approval for nearly anything on the web.
“Likes” have become so significant that they factor into Bing’s algorithm for social search results, and even have a place in Blekko’s human curated search engine. “Likes” also determine popularity: the more “likes” a piece of content or status update gets, the more that item is resurfaced inside and outside of Facebook.
The Changing Definition of (Social) Search
The rise of Facebook and its hold over our attention begs the question, should we still think of search as an explicit query-driven practice? Or, is search in the traditional sense outdated?
Are social networks (or information networks) the new search engine? Or, as Steve Jobs would argue, is the mobile app the new search engine? Or, is the question-and-answer formula of Quora the real search 2.0?
The answer is most likely all of the above, because search is being redefined by all of these factors.
Because search is changing, so too is the still maturing notion of social search, and we should certainly think about it as something much grander than socially-enhanced search results.
The average Facebook user does not say to himself, “I want to search for the most popular stories among my Facebook friends.” No. Facebook does the work for them by crafting a search experience, without search, that highlights content of social relevance.
It’s for this reason that one-off social search engines like Sharetivity are not the future — look at Sentimnt, which has closed down its consumer-facing social search product. A social search engine that requires the user to think about surfacing content from social networks is one that misses the point.
Semantic Analysis, Machine Learning and the Next Generation of Social Search
Let’s embrace the notion that social search should be effortless on the part of the user and exist within a familiar experience — mobile, social or search.
This social search future is already unfolding before our very eyes. Foursquare now taps its massive checkin database to churn out recommendations personalized by relationships and activities. My6sense prioritizes tweets, RSS feeds and Facebook updates, and it’s working to personalize the web through semantic analysis. Even Flipboard offers a fresh form of social search and helps the user find content through their social relationships.
Of course, there’s the obvious implementations of Facebook Instant Personalization: Rotten Tomatoes, Clicker and Yelp offer Facebook-personalized experiences, essentially using your social graph to return better “search” results.
Then, there’s a crop of new startups that dig through the clickstreams of friends, all of which have plans to move into content recommendations.
We’re just now scratching the surface of what’s possible when one’s expanding social graph becomes intertwined with search. But as time goes on, the social search experience will be so fluid — it will seem more like discovering than searching — we won’t even know it’s happening.
Series Supported by SES New York Conference & Expo
The Future of Search Series is supported by SES New York Conference & Expo, the search and social marketing conference helping brands, agencies, and professionals connect, share and learn what’s next for the interactive industry. Learn why more than 5,000 brands and agencies from the enterprise level to brick and motor businesses choose SES for their online marketing education.
Editor’s note: This is a guest post submitted by Mahendra Palsule, who has worked as an Editor at Techmeme since 2009. Apart from curating tech news, he likes analyzing trends in startups and the social web. He is based in Pune, India, and you can follow him on Twitter.
What’s the Next Big Thing after social networking?
This has been a favorite topic of much speculation among tech enthusiasts for many years. I think we are already witnessing a paradigm shift – a move away from simple social sharing towards personalized, relevant content.
The key element of the next big thing is the increasing significance of the Interest Graph to complement the Social Graph. While Facebook, Twitter, and Google are already working on delivering relevant content, a slew of startups are focusing exclusively on it.
Relevance is the only solution to the problem of information overload.
The above matrix is a representation of how the process of online information discovery has evolved over time.
Phase I: The Search Dominated Web
This is how Google began its dominance over the web two decades ago, using PageRank to surface the most popular web pages as identified by other web pages that linked to them.
Phase II: Web 2.0 With Social Bookmarking
In the Web 2.0 era, social bookmarking services gained significant traction, surfacing popular content. Sites like Reddit and StumbleUpon are hugely popular even today, driving millions of page views.
Phase III: Personalized Recommendations
Services like Hunch, GetGlue, etc. have focused on building an Interest Graph for users, to deliver personalized recommendations using a ‘taste engine’.
Phase IV: Personalized Serendipity
The latest crop of startups is focusing on personalization using a combination of Interest and Social Graphs. Personalized Serendipity is what Jeff Jarvis calls ‘Unexpected Relevance’. Examples include Gravity, my6sense, Genieo, and TrapIt.
What Exactly Is Relevance?
The battle against information overload is sometimes presented as a choice between Relevance and Popularity, where ‘relevant’ is equated to ‘personalized’ as against popular.
However, Relevance does not always mean Personalized. Relevance is very dynamic – it depends on the needs of a person at a specific point in time. There are times when users want to know about the most popular stories, and other times when they seek personalized content.
There are multiple approaches to filtering information for Relevant Content. Google, Paper.li, and PostRank are examples of algorithmic filtering, while Reddit, Hacker News use a crowdsourcing approach. Klout can be used to filter Twitter streams by influence, while Facebook uses social affinity as a filter for its newsfeed and social signals for its new Comments Plugin. Location is another high-impact signal for delivering relevant content, gaining importance in a mobile world.
In other words, Relevance spans across all the quadrants of the Discovery Matrix above, and none of the above approaches to filtering for relevance is the ‘best approach’. There is no killer approach to Relevance. Henry Nothhaft, Jr., CMO of TrapIt, described it as “the myth of the sweet spot”. The competitive edge will be with services that support multiple discovery methods, multiple filtering approaches, have flexibility, and support multiple mobile platforms.
Quora: A Showcase Of The Interest Graph
Quora has pioneered the use of the Interest Graph as a dominant signal for its newsfeed. Quora asks new users to select Topics to follow, as part of its onboarding process, which is the first revelation that Topics are as important as Users to follow.
Quora’s newsfeed is an interesting showcase of what happens when you mix an Interest Graph with a Social Graph – and the result is the mysterious addictiveness so many have experienced, but found difficult to explain. An item pops up in your newsfeed not because you were following a user, but because you were following a related topic.
This often leads to Personalized Serendipity – or Unexpected Relevance – which is why Quora gets many people hooked.
The war over the Interest Graph began between Twitter and Facebook last year, as Erick described so eloquently. So how did Quora beat them to this game?
For starters, Quora is built from the ground-up with the Interest Graph being a backbone of the framework. Twitter’s ‘Browse Interests’ is too broad and primitive to be of use, even at present. And while Facebook has a mechanism for allowing publishers to push new items to your feed, most publishers have been unaware of this functionality.
The implications of a Relevance-driven web are wide-ranging and broad in scope. Better utilization of the Interest Graph by services will lead to better ad targeting, and a potential decrease in reliance on CPM/CPC-based advertising. Monetization focus will be on higher yields through transactions and subscriptions as Dave McClure once described. Online media publishers will focus on Relevance Metrics revealing engagement and time-spent on site, than primitive metrics like page views and traffic.
Social media may lose its obsession with follower numbers and traffic, evolving to context-driven reputation systems and algorithms.
Interest Graphs will be used to build Better Social Graphs. Today’s monolithic Interest Graph will get further specialized into Taste Graphs, Financial Graphs, Local Network Graphs, etc., yielding higher relevance for different needs.
Here’s a great example of how a seemingly noble social media donation campaign can go awry: Microsoft tweeted two hours ago on the Bing search engine’s Twitter account that it would donate up to $100,000 to help victims of Japan’s earthquake — but only if Twitter users retweeted its original post to broadcast it to their followers, at $1 per retweet.
A “social search” is one that ties a searcher’s social graph to his search queries. With social search, each searcher sees unique results that are shaped by the interests of his social network friends.
