“This remarkable group of editors and authors presents a range of opinions about the challenges and opportunities of business life in a digital era. Taken together, these essays help us to debunk many of the myths that we too commonly rely upon when we talk of digital natives, in the context of business, marketing, learning, career development, and otherwise. … No matter what, this book will make you think.”

—John Palfrey, co-author, Born Digital: Understanding the First Generation of Digital Natives

The book is a compilation of chapters by different authors who speak to the characteristics of “digital natives” — folks who have been immersed in digital technology from birth — and how digital natives are transforming the way business is done. My chapter is on the work style of young technophiles. It just so happens to be the first chapter and the sample chapter that you can download to get a preview of the rest of the book!

I am quoted in a book review by PR Newswire, talking about the impact of digital natives in the workplace:

“It’s not just their hip iPhones and contemporary slang (‘Facebook me!’) that marks this as a new era. Their work practice is fundamentally changing as they live and breathe this culture of distributed networking and social technologies.. … It may never be a practice that managers and previous generations wish to embrace personally—and that’s fine. They will, however, need to recognize and understand this emerging work practice if they wish to maximize the digital native workforce.”
—Brynn Evans, Ch 1 – When Facebook Comes to Work

———
Dancing with Digital Natives was edited by Michelle Manafy and Heidi Gautschi. The contributors include Mary Ann Bell, Shashi Bellamkonda, Sarah Bryans Bongey, Jami L. Carlacio, Albert M. Erisman, Brynn Evans, Susan Evans, Lance Heidig, David Hubbard, Richard Hull, Marshall Lager, Christa M. Miller, Emilie Moreau, Carolina M. Reid, Michael Russell, Peggy Anne Salz, Arana Shapiro, Dan Schawbel, Rebecca Rufo-Tepper, and Robert J. Torres.

It can be purchased on Amazon or directly from the publisher.

2011/408 pp/hardbound
ISBN 978-0-910965-87-3
Regular Price $27.95

It’s exciting that South by Southwest is just around the corner! When I submitted my panel proposal back in, what, June? July?…I had an inkling that games would be an interesting topic, and I was particularly interested in social change. Hence a panel proposal which is now called: 1UP! Games for Change. (hashtag will be #1UP)!

Yesterday, James Renovitch published an article in the Austin Chronicle based on an interview with me, Thor Muller, and Dave Gray about our respective game panels: “Gaming the System: Applying the mechanics of play to the everyday“. It got me even more excited about picking the brains of my two awesome panelists, Zao Yang (of MyMiniLife/Farmville) and Jude Ower (of theplaymob.com), about their perspective on “gamification” and how games can be used to motivate people to change their behavior – for personal or social change.

The short version of the panel is this:

This panel will explore ways that games can be used to motivate personal change and increase productivity. We’ll start by talking about the theory behind games and gamification for behavior change — in contrast to games for social networking (e.g., Foursquare) — and how game-like activities have been used for a long time to encourage people to change their own behavior. Then the discussion will revolve around applications of game theory in practical, everyday situations (e.g., in the workplace, for dieting, parenting, reducing our carbon footprint, etc). Our panelists will talk about their experience in these situations — what games they chose, how the games helped change people’s behavior, and any take-aways they have for using games in your own life or building them into your next enterprise.

For the long version, come attend the panel in Austin!

1UP! Games for Change: Tuesday March 15, at 5:00pm in Room 6AB of the Austin Convention Center.

What is social interaction design?

Social interaction design (SxD) is the practice of designing for person-to-person interactions mediated by a computer interface, going beyond pure usability and human-computer interaction. Even fairly solitary experiences like editing a Wikipedia page occur in a social context in which other users’ past interactions influence what new editors contribute.

“It’s the the interactions among users that informs design” (Adrian Chan).

What’s a good example of an SxD problem?

Vark.com is a question-answering service that routes users’ questions to people in their extended networks who may have relevant knowledge of the topic. The original service operates through IM, Twitter, and email; more recently an iPhone app has been developed.

Let’s consider the difference between the mobile and desktop experiences of Vark.com. Both asking and answering activities work rather well in desktop email and IM. In contrast, responding on-the-go is awkward—more often than not, we’re distracted, hurried, or unable to type a coherent answer without bumping into a fire hydrant.

There’s also an assumption that the answer resides solely in our heads, when in reality, providing an answer often requires sharing links or performing a quick search—that is, we may not have the answer immediately on hand, but we know where to look.

Furthermore, successful answers often manifest as conversations on the desktop (example above), in which messages are exchanged in a back and forth manner so that the questioner can clarify her question and the answerer can refine her response. This type of sustained interaction is much harder to establish with on-the-go users.

Finally, iPhone prompts (below) often lack enough information about the nature of the question or your relationship to the questioner. One reason for Vark’s success is that it seeks out answers from people within an extended, personal network, naturally building trust and accountability into the system. But without knowing how you know the questioner, the iPhone app experience feels instead intrusive and disruptive, and lacks any strong social motivator to respond.

Why is remote research useful for SxD?

Traditionally, user-centered designers conducted field studies or shadowed someone to learn more about their practices. The digital space complicates matters—not only is it difficult to shadow someone, but people’s actions are so fluid and varied that it’s hard to isolate specific behaviors in order to study them.

Remote research has emerged as a great way to do needs-finding for SxD, for three reasons:

First, it’s hard to recreate interactions between two or more people in a lab setting. Last year when I was studying user interactions during social search tasks, I realized that I needed to talk to multiple people: both the user who posed the question as well as the people who provided replies. I started by observing the questioning process: how the question was phrased, which communities or individuals were questioned, the historical relationships between the parties. Then I explored the answering process: answerers’ perception of the request, why they chose to reply, if they had a history of interacting like this.

What’s interesting is that answers provided over social networking sites (like Twitter and Facebook) were mostly jokes or “nudges” to attract the user’s attention (“Hey, remember me?”). But answerers in private channels (email, IM, phone) were more serious and thoughtful because people were contacted directly and had longstanding relationships with the user (“She asked me personally, and she’s helped me in the past”).

Second, social interactions unfold over time, and their repercussions aren’t always apparent in a hour-long lab study. I recall one user in my social search study who asked a question on ping.fm. He received a prompt reply which “seemed right”, so he reported it as his “final answer”. I followed up two days later to see if he had received any other replies. In fact, the conversation thread on ping.fm had progressed, and the community had collectively concluded that the earlier reply was incorrect. This observation was only made possible by the passage of time.

Third, social interactions are best understood within the context where they occured. Not just physical location, but also past history (between the people interacting) and reasons for having the interaction. For example, my sister tweets about her new startup, but I’m not familiar with her a field and don’t have a professional relationship with her, so I seldom reply to her tweets. However, when she emails, calls, or writes on my Facebook wall, I reply instantly—even on an unfamiliar topic. If you were only studying my Twitter use, you might wrongly conclude that I’m an ingrateful sister, but this interpretation would be taken out of the full context of my relationship with her.

Thus, whether you’re designing for healthcare, fitness, games, dating, or online privacy, it’s critical to gain insight into where, when, and why people to act the way they do. Community engagement through social media will differ substantially depending on people’s personalities, reputation, location, local culture and rules, nature of their relationships, and history of the community. Remote research methods—like experience sampling, remote observations, and critical incident surveys—are great tools for understanding the many facets of social behavior, and suggest productive avenues for pursuing SxD.

Additional resources:
Social interaction design salon (group blog)
Digital ethnography for social interaction design (slide show)

[Guest author: Brynn Evans is a digital anthropologist, design researcher, and author who studies social interaction design and social search. She extends a thousand thanks and a bear hug to Tony Tulathimutte for help in editing this post!]

With Google’s Social Search experiment, Bing’s integration with Twitter, and with Yahoo!’s partnership with One Riot—it’s clear that social search has both potential and momentum. But what will social search look like, and will it help us search better? And if it will, how?

I’ve written previously about how social search won’t replace traditional search, how social relevancy rank may be used to deliver good results, and why the concept of social search is a return to a familiar state rather than something to fear. Today I’ll get more specific with the three flavors of social search that will improve user search experiences.

Collective Social Search

“Collective Social Search” is similar in concept to the wisdom of crowds, in that search is augmented by trends shared on a network (a la Twitter Trends) or results ranked against the real-time buzz of a group. Why might this be useful? Well, in some instances, we can’t immediately find the information we’re looking for; and pooled, aggregated data from the collective may point us to new avenues that expand our discovery process.

As of yet, no major search systems are doing this very well — and we don’t know what type of interface would be optimal for sharing this information. The Cloudlet Plugin inserts tag clouds (based on keywords) into search results; but tag clouds are known to be more of a distraction than a utility. BingTweets has been touted as such a resource, but it really only offers Twitter and Bing results on two separate pages. OneRiot shows only collective data from the real-time stream, although it may be integrated with Yahoo! results soon. And we are still waiting to see how Google and Bing integrate the Twitter firehose into their traditional search results — as opposed to merely including them as additional document-like resources.

Equally important will be understanding when collective social data should be shared with users: while performing the search or after? And for which types of searches?

My research on search strategies begins to address this question. Collective guidance may be useful when users are exploring a search space, possibly because the search domain is not familiar to them (i.e. they lack knowledge of how to drill down to an answer), or because they are passively exploring a problem. I find myself doing this all the time when I prepare recipes to cook. I want to browse recipes from many different sources before I decide what my own recipe will consist of. I don’t have a specific recipe in mind (it’s not an urgent, active request), and therefore I don’t necessarily know when I’ve found what I’m looking for.

That said, it’s hard to determine from keyword strings how active or passive a user’s search is; i.e. it may be quite difficult to determine the type of search they’re performing or how far along they are in their search process (“exploring” or “narrowing”?). Furthermore, the utility of collective social data for mainstream consumers will be limited, mainly because it doesn’t come from trusted sources, unlike “friend-filtered social search” (see next section).

Friend-Filtered Social Search

“Friend-Filtered Social Search” is approximately what Google is doing with its Social Search experiment: providing social data that your peers, friends-of-friends, and wider “social circle” have shared. This data could appear alongside traditional search results (as with Google) or be exclusive results from within your peer network (as with TuneIn).

This is useful if your friends have shared relevant links, blog posts or tweets about a topic that you’re searching for. If you were gathering ideas about, say, “the future of the desktop,” you would see thought pieces, write-ups and links to projects from the main search algorithm, as well as stuff your friends are saying about applications they’ve encountered recently. If you trust your friends, they may serve as reliable filters, pointing you to relevant information.

The three major limitations of this approach are:

1. Your friends may have no archived social content that’s relevant (or available) to your query. Searching within your Facebook network quickly demonstrates this problem. For this reason, augmenting traditional algorithms with friend-filtered social data may be better, rather than relying exclusively on data from one person’s small exclusive network.
2. Current implementations are limited to keyword matching; whereas, searches that retrieve related posts based on topic, theme or timeframe might expose a wider set of results and combat the niche-social-network problem. This approach would be computationally harder than keywords alone, and exposing enough of the appropriate context remains a problem (see next item).
3. Understanding the context in which a post or link was shared is important. Without this, keyword- and even topic-matching might not convey to the user the relevance of a search result. Google provides limited context at the moment (showing only how you know a user, the source of the post and a short snippet). More testing is needed to learn how much and what kind of context is appropriate for social search content.

Similarly there is the issue of when friend-filtered social search would be relevant during a search. My instinct is that it will be useful throughout a search and for many types of searches (it is, after all, just another type of search result). This is critically different from collective social search and collaborative search.

Collaborative Search (aka Question-Answering)

“Collaborative Search” is when two or more users work together to find the answer to a problem. This could look like IM-based question-answering (a la Aardvark), Yahoo! Answers (which is relatively passive and asynchronous), or over-the-shoulder two-person search. In all of these cases, people speak to each other using natural language, which is incredibly useful for open-ended queries (e.g. “What is ‘design thinking’?”) or queries about unfamiliar domains (e.g. law, health, business, depending on your background). Such conversations, even not real-time ones, can assist people who don’t know the right keywords to use (what’s known as the ”vocabulary problem.”

My research has looked at the benefits of question-answering and at people’s processes and preferences during search. Many users report that they want to attempt to search on their own first, or don’t wish to interrupt their colleagues before they have given it a shot independently. This suggests that early social support should be passive (as with presenting collective or friend-filtered social data).

But later in the process, if the searcher gets stuck on a problem, they often turn to a colleague for help. If systems had a way of identifying difficult queries or search-process inefficiencies, they could offer more explicit social support to searchers. Perhaps the system could identify a domain-specific expert from the user’s extended social circle. Information that this person has shared could be presented to the user, or this person could be suggested as a resource to chat with or email (depending on availability and preferences).

It should be clear by now that these three flavors of social search are complementary. Each has its pros and cons and is appropriate for different kinds of searches and during different stages of the search process. A powerful “social search engine” would be “smart” by making use of all three, while also exploiting the value of traditional algorithms.

Photos by: Who Wants to Be?, Claudia Lim and brewbooks.

A word before I go on: I generally think it’s dangerous to assume a searcher’s intent based on their search query terms alone. A premise in Click is that at scale, we can pull out things like intentions and motivations from web-wide trends. While Tancer could make a stronger statement about being cautious around intent (he does this in a very subtle way), he also illustrates how huge sets of internet search data do reveal things about us that we may not offer up willingly (if asked about it).

He makes this point about porn and also about fears. Expanding on this notion, I want to further illustrate how what we say is not necessarily what we do.

Tancer notes that the results from a nation-wide survey asking people about their phobias was substantially different from the list of fears he uncovered in his web-wide search query data. The survey, callled the National Comorbidity Survey, found that people’s top nine fears were:

1. Bugs, mice, snakes, and bats
2. Heights
3. Water
4. Public Transportation
5. Storms
6. Closed spaces
7. Tunnels and bridges
8. Crowds
9. Speaking in public

In contrast, the top ten fears from queries containing “fear of” (after filtering for non-phobia fears) were:

1. Flying
2. Heights
3. Clowns
4. Intimacy
5. Death
6. Rejection
7. People
8. Snakes
9. Success
10. Driving

What’s interestingly different from these two sets is that the non-survey results (2nd list) includes more social fears: “fear of intimacy,” “fear of rejection,” “fear of people,” and “fear of success.” Why might these differences be present? Perhaps because people don’t want to admit their social phobias to the person administering the survey? Perhaps because, when asked about a “phobia,” people will think of things (heights, water, tunnels, snakes) and rather than social scenarios.

Either way, what people say and what people do (think or mean) can be seriously different; and this is the greatest danger of relying (exclusively) on survey data.

This is always at the forefront of my mind as I’ve designed, administered, and analyzed many types of surveys. However, surveys can be well-positioned in the research and design process provided a few tips and guidelines are observed:

1) Avoid asking for generalizations!
People are terrible at estimating how long, how much, or how often they do something. There are some exceptions to this: if you truly do something everyday (like shower first thing in the morning), then you’ll be better at discussing your behaviors (in the abstract). Infrequent behaviors are hard to estimate and should generally be avoided in surveys.

But Brynn, you might ask, you just ran a survey asking people about their coffee drinking behaviors. Is this reliable data? That’s an excellent question, but you have to also take into consideration our intended use of that data for the betacup project. We intentionally planned to collect data from a few hundred people to identify apparent use cases and patterns, which we will subsequently investigate more directly through interviews and observations in coffee shops. Therefore, my second tip is:

2) Surveys are well-suited for establishing basic trends and underlying patterns.
I would not design a product or service around survey data alone! They can be suitable as the first step in a research program, but don’t confuse the power of numbers with the power of direct observations. Having 250 or 1000 data points in a survey provides a completely different perspective on a problem than watching 5 people move through, order from, and drink coffee in a coffee shop, for example.

However, I have learned that if you get people to talk concretely about a specific time in their past, they do a reasonable job recounting it.

3) Getting people to talk in specifics will improve the reliability of survey data.
I’ve been using a modified critical incident reporting model for collecting information from people online. Typically critical incident reporting was a technique used in face-to-face interviews: A researcher would ask a user to describe a time when something specific happened (a salient episode from the past). These are called “critical incidents” — but I’ve found that this general method can be applied to everyday episodes if you ask people to describe the last time something happened. For example, can you tell me about the last time you read a book or cooked dinner at home?

One caveat on this approach: actions that people take very frequently or very infrequently are harder to recall. Do you remember the last time you hand-washed your car? (This is presumably an infrequent behavior). Or the exact steps you took to get prepared for work this morning? (This is may be done on auto-pilot if you have a regular routine.)

If you have other tips or guidelines for studying user behaviors from written, remote surveys, please share them here!

In the modern digital age, online surveys are cheap and quick to administer. Yet they are not trivial to design and caution should be taken to make sure every question serves a purpose relative to your overall project goals. As designers we must be cautious about the reliability and generalizability of survey data alone. What people self-report (I have a fear of bugs and mice) may be very different from what people actually mean (I have a fear of people, intimacy, and rejection). Yikes.

The betacup Bodystorming Session – Overlap 09 from The betacup on Vimeo.

“I grabbed a cup of instant from the hospitality table on the second morning of orientation. It was piping hot and I was in a hurry, so I stuck an ice cube in, but then a friend hailed me and began chatting; by the time we parted, I was holding a cup of watery lukewarm coffee.” –Anonymous (F, 18-24 year-old student from Virginia)

As we learn more about coffee drinking behaviors — the practices around buying and consuming coffee — the better we can tailor the design of a so-called “betacup.”

Krista Sanders, an original member of our team from Overlap’09, has the following reflections on user-centered design and our approach to coming up with a practical solution to the “paper cup problem”:

1. Behavior influences design. Identifying current behavioral patterns provides a solid launch point from which we can design improvements to the existing workflow or process.
2. Relevance increases adoption. By understanding a user’s basic tasks and goals, we are more likely to create potential solutions that not only adhere to, but also enhance the overall experience.
3. The Convenience Monster MUST be appeased. Centering the design process around even the simplest of user expectations is one of the most important requirements for achieving a successful design.

I apologize that it’s not the shortest survey. Thank you in advance!

I was part of a 3-person project team this quarter (Spring 2009) that tried to design a solution for this problem. Our “Social Shirt” prototype is intended to mitigate the awkwardness during social interactions with people who we don’t remember well or with distant acquaintances. It presents relevant information to the user through an LCD embedded in the fabric of a shirt near the wrist, which we found may referenced with minimal interruption during a normal conversation. We describe our data collection and prototyping (research and design) process in this report and in the video below.

The Social Shirt: Memory On Your Sleeve from Brynn Evans on Vimeo.

However, in reflecting on the strengths and weaknesses of social search, I don’t think it’s going to “topple” Google anytime soon.

There are four important points to consider here: (1) why Google is good at what it does; (2) the drawbacks to Google search; (3) social search as a potential solution; and (4) the limitations of social search. I do believe that there is a place for socially-augmented search, but it will be as a supplement to, not a replacement for, Google.

Why Google is good at what it does

There is a huge advantage to Google search as it currently exists. Google scans and indexes vast amounts of information, catalogs it in databases, and provides quick and efficient mechanisms for retrieval. Your average Google search takes milliseconds to complete, which leaves plenty of time for you, the searcher, to review the result listings, synthesize the information, and possibly refine and iterate on your search. Think about it: Without search engines, all of our time would be spent searching for and retrieving information from a much smaller corpora (based on our very real human processing limitations). We would be left with little time to contemplate our search results. Google, as a “tool”, is a scaffold for the cognitive process of search. For social search to topple Google, it would need to provide a greater scaffold than Google already does.

Additionally, people are hooked on Google. (This point should not be taken lightly!) People are relying more and more on the web for their daily information needs. In support of this, the PEW Internet & American Life Project [14] recently found that broadband and dial-up users prefer using the Internet over friends and family for help in solving problems. Of course, this isn’t the case for all information needs; community networks are still preferred in certain circumstances (e.g., choosing a new bank, dealing with a spiritual crisis). However, when asked why these individuals prefer web search to asking friends and family, they report two main reasons. First, the web can provide them with information more quickly. Second, the content they receive may be more accurate (presuming that it comes from a larger pool of potentially unbiased and/or more authoritative sources). These are strong motivations for users to continue using the web (and Google) to satisfy their information needs. The PEW study’s conclusion is that social networks are still important sources of information, but that the Internet supplements and facilitates communication and information seeking. I expect the same with social search—that its power lies in its ability to supplement and augment our current search practices.

The drawbacks to Google search

However, there are limitations with our current search tools. The most notable drawback is that Google can’t always satisfy our information needs. This may occur for several reasons, perhaps because some inquiries aren’t well suited for a Google approach (Google deals best with facts, historical references, etc.) or because human search terms don’t match Google’s index terms (the human-system “vocabulary problem” [7]). Searchers who are unable to properly describe their search problem or whose query is not neatly addressed in the first page of hits may experience failures. It’s unclear at this point how frequently search failures occur. (Errors in accounting for this may always exist since people with difficult queries may be disinclined from even trying Google). On the other hand, Ed Chi & I have found that 87% of failed search reports were from informational queries, where the question was open-ended or the information need poorly defined. These searches are not likely to be addressed by simple facts present on the first page of search results, and would be expected to produce (occasional) failures with current search solutions.

Furthermore, “real world” searches extend across physical and digital media, include friends, family, and coworkers for support, and may even span several sessions, days, or weeks [2]. This has been observed in libraries and other physical settings [13, 15] and more recently in web search studies [5, 11]. A major drawback with current search tools is that they rarely capture (and address) the context and complexity of these types of searches. Simply put, Google is pretty dumb. It does what it does really well—indexing and retrieving data from large databases. But it does not know what room we’re in or what our surroundings are like; it does not know our recent activities; and it does not know who our friends are. I’m not advocating that we’d ever expect (or want) a search engine to become our personal assistant in this manner. Instead, I see Google’s drawbacks as an opportunity for social search.

Social search as a potential solution

The benefits of social search (on the web) are mostly theoretical at this point, although there is mounting evidence that social interactions provide critical support during search and problem solving. For example, people can provide solutions (advice, guidance, assistance) [1, 3, 8], pointers to databases or other people [3, 6], validation and legitimation of ideas [3, 5], and can serve as memory aids [9].

Plus, social interactions are useful for more than conveying knowledge or supplying certain facts. People can help with problem reformulation [3] and brainstorming [10]. “Guided participation” [12] is a process in which people co-construct knowledge in concert with peers in their community [4]. Social psychologists have recently found that social discussions facilitate subsequent cognitive performance [16]. These findings suggest that social interactions could naturally aid users in web search tasks, although we cannot predict how these interactions will play out in practice.

Several “flavors” of social search solutions already exist on the web, each with their own strengths and weaknesses. Some services are collective harvesters, presenting content based on popularity or emerging topics (Digg, OneRiot), or re-ranking search results based on user annotations (WikiaSearch, Google’s SearchWiki). These solutions, although available instantaneously, may lack relevance or validity since they are pooled from anonymous users across the web. Others try to connect searchers to experts (Yahoo! Answers, MetaFiler, Aardvark), providing personalized answers but at a serious cost (a time delay). Searching through social networks is another solution that has the potential to be both personalized and expedient. For example, a search in FriendFeed first returns content that your friends have shared with the system. Delver presents data shared by your social graph (elsewhere) intermixed with their search result listings. Undoubtedly Facebook and Google (who arguably have social graph data through Gmail contacts) are also considering solutions in this direction. While these solutions must require a lot of computing power (among other social smarts), I believe that a personalized social graph solution, if integrated with Google, will be the most successful in addressing real search problems.

Limitations of social search

One concern with these existing social search tools (and others) is that they aren’t quite the solution to the search problem yet. Do these services address real user needs? Is this the type of social support that will be most useful during difficult queries or failed search attempts? As this is yet to be determined, it’s worthwhile reviewing the current limitations of social search.

An obvious limitation is that information from social resources may not be as complete or thorough as information stored in large search engine databases. This is an issue of scale and scope. A social search only solution will lack either the scale or the scope that a Google search can provide. This criticism is not entirely a fair, however, if social inputs are used primarily to augment regular result listings, instead of replace them.

A narrower scale and scope may actually be advantageous by helping searchers in one of two ways. First, since we naturally seek information from other people directly, receiving suggestions of experts or friends to help with a certain problem may be quite useful. Second, socially-filtered data may be more trustworthy, especially if the filtered information comes from people you know.

On the other hand, a searcher may not have knowledgeable or available contacts for a given query. In these circumstances, passively receiving social recommendations may be a good solution. My current work is looking at the tradeoffs between asking friends directly for help and receiving somewhat anonymous replies from large social networks (a la social recommendations) during search and sensemaking tasks. These strategies provide complimentary support for searchers: While the latter may present a diverse set of views on a topic (establishing breadth), the former may help develop depth on individual topics.

Finally, social inputs won’t be the right solution for every user query. Certain questions are likely to be improved by social search (experience-, opinion-based, and tacit information), but others may be hindered. Open-ended, exploratory queries may benefit from social inputs, simple navigational ones may not [5]. To avoid cluttering the interface or cognitively overloading the user, an optimal solution would deliver social solutions appropriate to the target circumstance. Will this type of inference and personalization be possible with search tools?

Concluding remarks

On the whole, social search is still in its infancy. Even Google, which has been around for 10 years, continually tweaks its search algorithms. It’s naive to think that we will have truly viable, integrated social solutions to the search problem over night.

Unfortunately, current social search tools are fairly crude, presenting solutions to (more simple) algorithmic problems instead of to user problems. This is not to say that the work of social search engines is a easy, but rather that they may be working on the wrong questions. Very few current social tools integrate with Google (with a few exceptions, whose value has yet to be demonstrated: CloudLet and Qitera). Since it’s unlikely that users will rush to adopt new search tools at this point, an integrated, combined approach (Google + social) seems more practical.

Beyond this, we need to know more about how social inputs, interactions, and passive social recommendations can address user needs during search, to help focus targeted solutions to real problems. I believe that Google search will continue to be a part of this solution; I also believe that social search has the potential to supplement Google in powerful ways.

References

[1] Bandura, A. Social Cognitive Theory. In R. Vasta (Ed.), Annals of child development. Vol. 6. Six theories of child development (1989), 1-60.
[2] Churchill, E. Of candied herbs and happy babies: Seeking and searching on your own terms. Interactions 15, 6 (2008), 46-49.
[3] Cross, R., Rice, R.E., and Parker, A. Information seeking in social context: Structural influences and receipt of information benefits. IEEE Transactions on Systems, Man and Cybernetics — Part C 31, 4 (2001), 438-448.
[4] Edelson, D., Pea, R., & Gomez, L. Constructivism in the Collaboratory. In B. G. Wilson (Ed.), Constructivist learning environments: Case studies in instructional design. Englewood Cliffs, NJ: Educational Technology Publications (1996), 151-164.
[5] Evans, B.M. and Chi, E.H. Towards a model of understanding social search. Proc. CSCW’08, ACM Press (2008), 485-494.
[6] Fox, E.A., Hix, D., Nowell, L.T., Brueni, D.J., Wake, D.C., Heath, L.S., and Rao, D. Users, user interfaces, and objects–envison, a digital library. JASIS 44, 8 (1993), 480–491.
[7] Furnas, G.W., Landauer, T.K., Gomez, L.M., and Dumais, S.T. The Vocabulary-Problem in Human-System Communication. Comm. of the ACM 30, 11 (1987), 964-971.
[8] Hatch, T., and Gardner, H. Finding cognition in the classroom: An expanded view of human intelligence. In G. Salomon (Ed.), Distributed cognitions: Psychological and educational considerations. NY: Cambridge University Press (1993), 164-187.
[9] Karasavvidis, I. Distributed Cognition and Educational Practice. Journal of Iterative Learning Research 13, 1/2 (2002), 11-29.
[10] Kuhlthau, C.C. Inside the search process: Information seeking from the user’s perspective. JASIS 42, 5 (1991), 361–371.
[11] Morris, M.R. A survey of collaborative web search practices. Proc. CHI’08, ACM Press (2008), 1657-1660.
[12] Rogoff, B. Apprenticeship in thinking: Cognitive development in social context. New York: Oxford University Press (1990).
[13] Twidale, M.B., Nichols, D.M., and Paice, C.D. Browsing is a collaborative process. Information Processing and Management 33, 6 (1997), 761-783.
[14] Wells, A.T. and Rainie, L. The Internet as social ally. First Monday 13, 11 (2008), retrieved from: http://firstmonday.org/htbin/cgiwrap/bin/ojs/index.php/fm/article/view/2198/2051.
[15] Wilson, T. On user studies and information needs. Journal of Documentation 37, 1 (1981), 3-15.
[16] Ybarra, O., Burnstein, E., Winkielman, P., Keller, M., Manis, M., Chan, E., and Rodrigues, J. Mental exercising through simple socializing: Social interaction promotes general cognitive functioning. Personality and Social Psychology Bulletin 34, 2 (2008), 248-259.