Do your friends make you smarter? Exploring social interactions in search

Photo Credit: Mikey Ottawa Do your friends make you smarter? Exploring social interactions in search Brynn M. Evans Supervised by: David Kirsh

i. Search as an activity ii. Related work iii. My previous work iv. Theoretical orientation v. Conclusion

Search as an activity

Search Question What hummingbird species is this?

Photo Credit: OpenThreads

Apr 13 Apr 16 Apr 20 Apr 30 May 9 Icon Credit: fasticon.com & iconaholic.com

Apr 13 Apr 16 Apr 20 Apr 30 May 9 GOOGLE Icon Credit: fasticon.com & iconaholic.com

Apr 13 Apr 16 Apr 20 Apr 30 May 9 GOOGLE BOOK Icon Credit: fasticon.com & iconaholic.com

Apr 13 Apr 16 Apr 20 Apr 30 May 9 GOOGLE BOOK FRIENDS Icon Credit: fasticon.com & iconaholic.com

Apr 13 Apr 16 Apr 20 Apr 30 May 9 GOOGLE BOOK FRIENDS SOCIAL NETWORK (http://watch.birds.cornell.edu) Icon Credit: fasticon.com & iconaholic.com

Apr 13 Apr 16 Apr 20 Apr 30 May 9 Anna’s ✔ GOOGLE BOOK FRIENDS SOCIAL NETWORK Hummingbird! Icon Credit: fasticon.com & iconaholic.com

search ≠ database lookup search ≠ keyword queries Photo Credit: B1gJ4k3

search ≠ database lookup search ≠ keyword queries search = an activity Photo Credit: B1gJ4k3

Even smaller searches are embedded in rich activities

Even smaller searches are embedded in rich activities Photo Source: Peter Voerman examples from E VANS & C HI 2008

A reaction to the classic view of search

Photo Credit: David Wild Photo Credit: Thomas Hawk classic view of search revised view of search activity can involve other people (remotely or single‐user activity co‐located) query‐response mechanism highly dynamic, fluid process few keywords, short sessions search can last over an extended period search takes place in a rich ecology of social and log files from single search engine information resources

With a revised notion of search... ...comes a revised method of study Searches are composed of: Activities Goals Operators

With a revised notion of search... ...comes a revised method of study Searches are composed of: Log files reveal operators Activities Goals Operators

With a revised notion of search... ...comes a revised method of study Searches are composed of: Observations reveal activities Activities Goals Operators

Related work

SEARCH LOCATION SEARCH GOAL

Information seeking in physical contexts Photo Credit: Rachael Lovinger A LLEN 1977; C ROSS ET AL 2001; C ROSS & S PROULL 2004; B ORGATTI & C ROSS 2003

Information seeking in physical contexts Photo Credit: Rachael Lovinger HIERARCHY (STATUS) PROXIMITY SOCIAL OBLIGATIONS A LLEN 1977; C ROSS ET AL 2001; C ROSS & S PROULL 2004; B ORGATTI & C ROSS 2003

(Joint) collaborative search online M ORRIS 2008; P ICKENS ET AL . 2008; P AUL & M ORRIS 2009; S HAH 2008. SEARCH TOGETHER CO-SENSE

Research Questions Empirical question: How do social interacTons help with individual search tasks? Design question: How can we improve search with social networking technologies?

My previous work

study one survey: everyday searches study two survey: diﬃcult or failed searches study three observaTons: cogniTve beneﬁts of social interacTons during search

Characterization studies of social search (studies one and two) E VANS & C HI 2008; E VANS & C HI 2009

Large scale characterization studies SEARCH STUDY N= generic or “everyday” 150 difficult or failed 150 Mechanical Turk Photo Credit: egoldviet (USED WITHOUT PERMISSION)

Large scale characterization studies SEARCH STUDY N= generic or “everyday” 150 difficult or failed 150 INFORMATIONAL searching for informaTon assumed to be present, but otherwise unknown

Large scale characterization studies SEARCH STUDY N= INFORMATIONAL generic or “everyday” 150 59% difficult or failed 150 87% INFORMATIONAL searching for informaTon assumed to be present, but otherwise unknown

Large scale characterization studies SEARCH STUDY N= INFORMATIONAL generic or “everyday” 150 59% difficult or failed 150 87% BEFORE DURING AFTER • reﬂecTon, synthesis • search preparaTon • search execuTon • feedback, iteraTon • problem formulaTon • lookup, foraging, re‐ﬁnding • sensemaking

Large scale characterization studies SEARCH STUDY N= INFORMATIONAL SOCIAL INTERACTIONS generic or “everyday” 150 59% 40% difficult or failed 150 87% 61% BEFORE DURING AFTER • reﬂecTon, synthesis • search preparaTon • search execuTon • feedback, iteraTon • problem formulaTon • lookup, foraging, re‐ﬁnding • sensemaking

Cognitive benefits of social searching (study three) E VANS , K AIRAM , P IROLLI 2009; E VANS , K AIRAM , P IROLLI 2009

Recruiting subjects PRE-TEST SURVEY PARTICIPANTS (N=8) • knowledge of energy policies • computer and internet use • search experTse • social acTviTes Icon Credit: Iconaholic.com, dryicon.com E VANS , K AIRAM , P IROLLI 2009; E VANS , K AIRAM , P IROLLI 2009

Two task questions Icon Credit: iconfactory.com, http://ecotechdaily.com/wp-content/uploads/2008/05/oil_drums_450.jpg

Two task questions 55 mph “If we lowered the speed limit nationally to 55 mph, how many fewer barrels of oil would the U.S. consume every year?” Icon Credit: iconfactory.com, http://ecotechdaily.com/wp-content/uploads/2008/05/oil_drums_450.jpg

Two task questions 55 mph Pyrolytic oil “If we lowered the speed limit “What role does pyrolytic oil (or nationally to 55 mph, how many pyrolysis) play in the debate over fewer barrels of oil would the carbon emissions?” U.S. consume every year?” Icon Credit: iconfactory.com, http://ecotechdaily.com/wp-content/uploads/2008/05/oil_drums_450.jpg

Two search conditions SOCIAL NON-SOCIAL Talk-aloud protocol • friends • search engines (email, phone, IM, etc.) (Google, Yahoo) • social networks • Wikipedia • blogs • QuesTon‐Answer sites Icon Credit: fasticon.com, deleket.com, sykonist.deviantart.com

Protocol SOCIAL INTERVIEW NON-SOCIAL INTERVIEW Block duration 12:00 - 35:00 5:00 - 40:00 5:00 - 20:00 5:00 - 18:00 Icon Credit: mugenb16.deviantart.com, bombiadesign.com, dryicon.com

Three social tactics SEARCHING NETWORK ASKING TARGETED ASKING Icon Credit: fasticon.com, deleket.com, walrick.deviantart.com

Coding of activities SS03 !,!!,!!quot; !,!),#$quot; !,#&,$" !,$#,%(quot; !,$*,&*quot; !,%(,!!quot; !quot; !,!!,!!quot; !,!),#$quot; !,#&,$" !,$#,%(quot; !,$*,&*quot; !,%(,!!quot; #quot; TIME $quot; %quot; " 'quot; LEGEND (quot; SEARCHING NETW. ASKING TARGETED ASKING CHECKING THINKING OFF-TASK )quot;

Coding of activities SEARCHING SS03 !quot; [ !,!!,!!quot; ] !,!),#$quot; !,#&,$" !,$#,%(quot; !,$*,&*quot; !,%(,!!quot; !,!!,!!quot; !,!),#$quot; !,#&,$" !,$#,%(quot; !,$*,&*quot; !,%(,!!quot; #quot; TIME $quot; %quot; " 'quot; LEGEND (quot; SEARCHING NETW. ASKING TARGETED ASKING CHECKING THINKING OFF-TASK )quot;

Coding of activities SEARCHING NETWORK ASKING SS03 !,!!,!!quot; !quot; !,!),#$quot; [ !,#&,$" ] !,$#,%(quot; !,$*,&*quot; !,%(,!!quot; !,!!,!!quot; !,!),#$quot; !,#&,$" !,$#,%(quot; !,$*,&*quot; !,%(,!!quot; #quot; TIME $quot; %quot; " 'quot; LEGEND (quot; SEARCHING NETW. ASKING TARGETED ASKING CHECKING THINKING OFF-TASK )quot;

Coding of activities SEARCHING NETWORK ASKING TARGETED ASKING SS03 !,!!,!!quot; !quot; !,!),#$quot; !,#&,$" [ !,$#,%(quot; ] !,$*,&*quot; !,%(,!!quot; !,!!,!!quot; !,!),#$quot; !,#&,$" !,$#,%(quot; !,$*,&*quot; !,%(,!!quot; #quot; TIME $quot; %quot; " 'quot; LEGEND (quot; SEARCHING NETW. ASKING TARGETED ASKING CHECKING THINKING OFF-TASK )quot;

Coding of activities SEARCHING NETWORK ASKING TARGETED ASKING SS03 !,!!,!!quot; !,!),#$quot; !,#&,$" !,$#,%(quot; !,$*,&*quot; !,%(,!!quot; !quot; !,!!,!!quot; !,!),#$quot; !,#&,$" !,$#,%(quot; !,$*,&*quot; !,%(,!!quot; #quot; TIME $quot; %quot; CHECKING THINKING " OFF-TASK FOR REPLIES 'quot; LEGEND (quot; SEARCHING NETW. ASKING TARGETED ASKING CHECKING THINKING OFF-TASK )quot;

Depth of processing SCORE DESCRIPTION 1 identified or perceives facts, data, or info understands the meaning of info; 2 presents a translaTon of info 3 integrates and synthesizes learned info TIME Ex. of learning of one fact over time SCORE 1 2 3

Depth of processing SCORE DESCRIPTION 1 identified or perceives facts, data, or info understands the meaning of info; 2 presents a translaTon of info 3 integrates and synthesizes learned info TIME Ex. of learning of one fact over time (FINAL) SCORE 1 2 3 SCORE FOR FACT #1

Coding of activities !,!!,!!quot; !,!),#$quot; !,#&,$" !,$#,%(quot; !,$*,&*quot; !,%(,!!quot; SS03 !quot; !,!!,!!quot; !,!),#$quot; !,#&,$" !,$#,%(quot; !,$*,&*quot; !,%(,!!quot; !,!!,!!quot; #quot; !,!),#$quot; !,#&,$" !,$#,%(quot; !,$*,&*quot; !,%(,!!quot; !quot; !,!!,!!quot; !,!),#$quot; !,#&,$" !,$#,%(quot; !,$*,&*quot; !,%(,!!quot; $quot; #quot; %quot; $quot; " each line = one fact %quot; 'quot; " (quot; 'quot; )quot; (quot; *quot; )quot; +quot; *quot; #!quot; +quot; ##quot; #!quot; #$quot; ##quot; #%quot; #$quot; TIME -./012quot;345quot; 607/18quot;9:;<=quot; 607/18quot;9:/.>=quot; ?8.@5.@Aquot; #%quot; -./012quot;345quot; 607/18quot;9:;<=quot; 607/18quot;9:/.>=quot; ?8.@5.@Aquot;

Coding of activities !,!!,!!quot; !,!),#$quot; !,#&,$" !,$#,%(quot; !,$*,&*quot; !,%(,!!quot; SS03 !quot; !,!!,!!quot; !,!),#$quot; !,#&,$" !,$#,%(quot; !,$*,&*quot; !,%(,!!quot; !,!!,!!quot; #quot; !,!),#$quot; !,#&,$" !,$#,%(quot; !,$*,&*quot; !,%(,!!quot; !quot; !,!!,!!quot; !,!),#$quot; !,#&,$" !,$#,%(quot; !,$*,&*quot; !,%(,!!quot; $quot; #quot; %quot; $quot; " each line = one fact %quot; 'quot; " (quot; 'quot; )quot; (quot; *quot; )quot; +quot; *quot; the U.S. enacted a 55mph speed limit in 1974 #!quot; +quot; ##quot; #!quot; #$quot; ##quot; #%quot; #$quot; TIME -./012quot;345quot; 607/18quot;9:;<=quot; 607/18quot;9:/.>=quot; ?8.@5.@Aquot; #%quot; -./012quot;345quot; 607/18quot;9:;<=quot; 607/18quot;9:/.>=quot; ?8.@5.@Aquot;

Coding of activities !,!!,!!quot; !,!),#$quot; !,#&,$" !,$#,%(quot; !,$*,&*quot; !,%(,!!quot; SS03 !quot; !,!!,!!quot; !,!),#$quot; !,#&,$" !,$#,%(quot; !,$*,&*quot; !,%(,!!quot; !,!!,!!quot; #quot; !,!),#$quot; !,#&,$" !,$#,%(quot; !,$*,&*quot; !,%(,!!quot; !quot; !,!!,!!quot; !,!),#$quot; !,#&,$" !,$#,%(quot; !,$*,&*quot; !,%(,!!quot; $quot; #quot; %quot; $quot; " each line = one fact %quot; 'quot; " (quot; 'quot; )quot; (quot; *quot; )quot; +quot; *quot; #!quot; +quot; ##quot; #!quot; #$quot; ##quot; #%quot; #$quot; TIME -./012quot;345quot; 607/18quot;9:;<=quot; 607/18quot;9:/.>=quot; ?8.@5.@Aquot; #%quot; -./012quot;345quot; 607/18quot;9:;<=quot; 607/18quot;9:/.>=quot; ?8.@5.@Aquot;

Coding of activities !,!!,!!quot; !,!),#$quot; !,#&,$" !,$#,%(quot; !,$*,&*quot; !,%(,!!quot; SS03 !quot; !,!!,!!quot; !,!),#$quot; !,#&,$" !,$#,%(quot; !,$*,&*quot; !,%(,!!quot; !,!!,!!quot; #quot; !quot; !,!),#$quot; !,#&,$" !,$#,%(quot; !,$*,&*quot; !,%(,!!quot; 1 2 !,!!,!!quot; !,!),#$quot; !,#&,$" !,$#,%(quot; !,$*,&*quot; !,%(,!!quot; $quot; #quot; %quot; $quot; 3 " 3 each line = one fact %quot; 'quot; " 2 (quot; 'quot; 3 )quot; (quot; *quot; )quot; +quot; *quot; #!quot; +quot; ##quot; #!quot; 2 #$quot; ##quot; 1 #%quot; + #$quot; TIME -./012quot;345quot; 607/18quot;9:;<=quot; 607/18quot;9:/.>=quot; ?8.@5.@Aquot; #%quot; PERFORMANCE SCORE 17 -./012quot;345quot; 607/18quot;9:;<=quot; 607/18quot;9:/.>=quot; ?8.@5.@Aquot;

More social tactics leads to better performance #social Performance ID tactics score S04 1 1 S07 1 3 S02 1 6 S06 2 2 S05 2 9 S08 3 7 S01 3 9 S03 3 17 Spearman R: 0.77

More social tactics leads to better performance #social Performance ID tactics score SOCIAL TACTICS S04 1 1 S07 1 3 S02 1 6 SEARCHING S06 2 2 S05 2 9 S08 3 7 NETWORK ASKING S01 3 9 S03 3 17 Spearman R: 0.77 TARGETED ASKING

Number of social tactics is more predictive of task performance than...

Number of social tactics is more predictive of task performance than... ...social network size

Number of social tactics is more predictive of task performance than... ...social network size ...how much knowledge is in the network

Number of social tactics is more predictive of task performance than... ...social network size ...how much knowledge is in the network ...the user’s: • background knowledge • and interest in energy policy

Are there complimentary benefits to different social tactics?

Results NETWORK ASKING TARGETED ASKING

Results Thinking before Thinking a'er posTng quesTon receiving replies NETWORK ASKING TARGETED ASKING

Results Thinking before Thinking a'er posTng quesTon receiving replies 60% OF USERS 0% OF USERS NETWORK ASKING TIME 29% OF USERS 100% OF USERS TARGETED ASKING TIME

Results Thinking before Thinking a'er posTng quesTon receiving replies 60% OF USERS 0% OF USERS NETWORK ASKING TIME 29% OF USERS 100% OF USERS TARGETED ASKING TIME More thinking before posting questions on social networking sites

Results Thinking before Thinking a'er posTng quesTon receiving replies 60% OF USERS 0% OF USERS NETWORK ASKING TIME 29% OF USERS 100% OF USERS TARGETED ASKING TIME More thinking after receiving replies sent from targeted friends

Social networking sites: Thinking before posting to many

Social networking sites: Thinking before posting to many “Now, what could I say?” “Is this better phrased as two questions?” “Let’s see...what do I really want to be asking?”

Targeting friends: Thinking after getting replies from few

Targeting friends: Thinking after getting replies from few Pyro means... instant messenger Long reply... email

Targeting friends: Thinking after getting replies from few “What are people’s average driving speeds anyway?” “Given that, then I need to also know...” “If ‘pyro’ means fire, then this might be a process to...” Pyro means... instant messenger Long reply... email

Nature of Replies NETWORK ASKING TARGETED ASKING • short, conversa2onal • long, detailed • funny, not relevant • focused, relevant

Nature of Replies NETWORK ASKING TARGETED ASKING • short, conversa2onal • long, detailed • funny, not relevant • focused, relevant “Because no one drives the speed limit” “Lots more waste sitting idly in traffic” “Isn’t that something I rub on my [body]? Are you still in San Francisco?”

Nature of Replies NETWORK ASKING TARGETED ASKING • short, conversa2onal • long, detailed • funny, not relevant • focused, relevant “Because no one drives the “15-25% savings. But if speed limit” those cars were electric, we’d have all those barrels left to use for something else.” “Lots more waste sitting idly in traffic” “There’s no one national speed limit, there are two: 55 miles “Isn’t that something I rub per hour in general, 65 miles on my [body]? Are you still per hour for certain roads.” in San Francisco?”

Network prediction

Network prediction PREDICTED 100 80 probability of 60 at least one (relevant) reply 40 20 0 1 ... ... 450 ... ... 700 ... ... 1000 NETWORK SIZE

Network prediction OBSERVED PREDICTED 100 80 probability of 60 at least one (relevant) reply 40 20 0 1 ... ... 450 ... ... 700 ... ... 1000 NETWORK SIZE

Network prediction OBSERVED PREDICTED 100 80 probability of 60 at least one (relevant) reply 40 20 0 1 ... ... 450 ... ... 700 ... ... 1000 NETWORK SIZE TARGETED ASKING NETWORK ASKING

Research Question Design question: How can we improve search with social networking technologies?

Theoretical orientation

“Inhabitedness”

“Inhabitedness” Photo Credit: Niall Kennedy

Social Presence Theory “A communicator’s sense of awareness of the presence of an interaction partner” Short, Williams, & Christie 1976 Photo Credit: Carlo Nicora; George Duncan

?? R OBERT & D ENNIS 2005

Inhabitedness Photo Credit: Carlo Nicora; George Duncan, Sebastian Tauchmann,Guennadi Ivanov-Kuhn

Inhabitedness Social Presence Photo Credit: Carlo Nicora; George Duncan, Sebastian Tauchmann,Guennadi Ivanov-Kuhn

Inhabitedness Social Presence Structure of the Space + Photo Credit: Carlo Nicora; George Duncan, Sebastian Tauchmann,Guennadi Ivanov-Kuhn

Structure of the space Banks of the Seine, Paris A street cafe in Manchester, UK C ARMONA , H EATH , O C , & T IESDELL 2003

Structure of the space Banks of the Seine, Paris

Structure of the space A street cafe in Manchester, UK

Inhabitedness Social Presence + Structure of the Space nature of the relaTonship features of the channel !e strength (e.g., strong !es, weak !es) mul!media content relaTve group membership the acTviTes supported social network size “social objects” apparent idenTty pseudonym vs. real name visibility frequency of updates

Next Steps... Analytical • operaTonalize the model • develop hypotheses about how inhabitedness predicts behaviors Methodological • test our predicTons experimentally

Conclusion

Photo Credit: David Wild

NETWORK ASKING TARGETED ASKING

SEARCH STUDY SOCIAL INTERACTIONS generic or “everyday” 40% difficult or failed 61%

Observed Predicted 100 80 60 old models ?? 40 20 0 1 ... ... 450 ... ... 700 ... ... 1000

“Inhabitedness” Photo Credit: Niall Kennedy

Thank you!! Third year class Supervisor Third year advisor Kaya de Barbaro David Kirsh Andrea Chiba Matthew Leonard Josh Lewis Anne Marie Piper Outside Collaborators Friend Helpers! Ed H. Chi PARC Chris Messina Peter Pirolli PARC Sharoda Paul Sanjay Kairam PARC Michael Bernstein Michael Muller IBM Research Elizabeth Churchill Yahoo! Research

Discussion Search as an ac2vity: • Searches are embedded in rich acTviTes that beneﬁt from social interacTons with others, and from social communiTes online Research Findings: • More thinking before asking quesTons to large social networks; • More thinking amer genng replies from targeted friends Towards a theory: • Social presence theory alone doesn’t explain most social search behaviors • Inhabitedness may explain some of these behaviors • This is a rich area for future design work Photo Credit: Peter Lee

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Do your friends make you smarter? Exploring social interactions in search

by Brynn Evans

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