Rethinking Location Sharing: Exploring the Implications of Social-Driven vs. Purpose-Driven Location Sharing, at Ubicomp2010
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Rethinking Location Sharing: Exploring the Implications of Social-Driven vs. Purpose-Driven Location Sharing, at Ubicomp2010

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The popularity of micro-blogging has made general-purpose information sharing a pervasive phenomenon. This trend is now impacting location sharing applications (LSAs) such that users are sharing their ...

The popularity of micro-blogging has made general-purpose information sharing a pervasive phenomenon. This trend is now impacting location sharing applications (LSAs) such that users are sharing their location data with a much wider and more diverse audience. In this paper, we describe this as social-driven sharing, distinguishing it from past examples of what we refer to as purpose-driven location sharing. We explore the differences between these two types of sharing by conducting a comparative two-week study with nine participants. We found significant differences in terms of users' decisions about what location information to share, their privacy concerns, and how privacy-preserving their disclosures were. Based on these results, we provide design implications for future LSAs.

Authors are Karen Tang, Jialiu Lin, Jason Hong, and Norman Sadeh

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    Rethinking Location Sharing: Exploring the Implications of Social-Driven vs. Purpose-Driven Location Sharing, at Ubicomp2010 Rethinking Location Sharing: Exploring the Implications of Social-Driven vs. Purpose-Driven Location Sharing, at Ubicomp2010 Presentation Transcript

    • Rethinking Location Sharing: Exploring the Implications of Social-Driven vs. Purpose-Driven Location Sharing Karen P. Tang Jialiu Lin, Jason Hong, Dan Siewiorek, Norman Sadeh Human-Computer Interaction Institute School of Computer Science Carnegie Mellon University
    • Location-Based Services Are Here 2
    • Types of Location-Based Services tracking personal trends (no sharing) doing local searches (sharing with a service provider) 3 [google latitude] [yelp]
    • Location Sharing Applications (LSAs) tracking personal trends (no sharing) doing local searches (sharing with a service provider) share locations with other people(a social network) 4
    • activecampus [griswold, ’03] lemming [hong, ’04] Past Research Examples of LSAs 5 2003 2004 2005 20082007 2009 esm study [consolvo, ’05] reno [smith, ’05] whereabouts [brown, ’07] watchme [marmasse, ’04] contextcontacts [raento, ’05] connecto [barkhuus, ’08] locaccino [sadeh, ’09] 1992 active badge [want, ’92]
    • activecampus [griswold, ’03] lemming [hong, ’04] Past Research Examples of LSAs 6 2003 2004 2005 20082007 2009 esm study [consolvo, ’05] reno [smith, ’05] whereabouts [brown, ’07] watchme [marmasse, ’04] contextcontacts [raento, ’05] connecto [barkhuus, ’08] locaccino [sadeh, ’09] 1992 active badge [want, ’92] The most common use of the system was by the receptionist who routinely used it when forwarding telephone calls from the main switchboard. Groups of people who regularly wanted to hold meetings could find each other easily with very little notice. “
    • activecampus [griswold, ’03] lemming [hong, ’04] Past Research Examples of LSAs 7 2003 2004 2005 20082007 2009 esm study [consolvo, ’05] reno [smith, ’05] whereabouts [brown, ’07] watchme [marmasse, ’04] contextcontacts [raento, ’05] connecto [barkhuus, ’08] locaccino [sadeh, ’09] 1992 active badge [want, ’92] Given mobile users’ fragmented attention, the time it takes to make a phone call must remain extremely short…These [context] cues [which include location] should facilitate decisions about whether to call, and if so, which communication channel to use. “
    • activecampus [griswold, ’03] lemming [hong, ’04] Past Research Examples of LSAs 8 2003 2004 2005 20082007 2009 esm study [consolvo, ’05] reno [smith, ’05] whereabouts [brown, ’07] watchme [marmasse, ’04] contextcontacts [raento, ’05] connecto [barkhuus, ’08] locaccino [sadeh, ’09] 1992 active badge [want, ’92] Phoebe wonders what she and her husband, Ross, will do for the evening, so she sends a location query to Ross. While he is waiting at the bus stop near his office, Ross sends a location update to Phoebe. Phoebe receives the message at home, eagerly anticipating Ross’ arrival home. When Ross gets off the bus, a location update is sent to Phoebe and she knows that he’s only 10 minutes away. She sets out dinner just in time for her husband’s arrival. “
    • Common Themes for Past LSAs driven by functional purposes: • coordination • collaboration • interruptibility • event planning one-to-one sharing or small group sharing 9
    • Industry Trends for Information Sharing integrated with online social networks (OSNs) • diverse networks, lots of weak links [wellman, ‘01] • very large networks [donah, ‘04] sharing is often not because one needs to share, but because one wants to share driven by a social reason for sharing 10
    • Commercial Examples of LSAs mostly aimed at social-driven sharing 11 2005 2006 2009 20102007 2008
    • Commercial Examples of LSAs mostly aimed at social-driven sharing 12 2005 2006 2009 20102007 2008 “I'm just down the street!” Never miss another chance to connect when you happen to be at the same place at the same time. [facebook places] Find out who’s around, what to do, and where to go. Introducing…the new Loopt so you can always stay connected… [loopt] Share your location and stay connected with your friends. [plazes] “ “ “
    • Reframing Location Sharing Purpose-Driven Social-Driven motivations coordination, collaboration, interruptibility, planning want (vs. need) to share, social awareness features one-to-one close-knit relationships one-to-many diverse relationship types 13
    • Understanding the Differences Q1: what are people sharing? will social-driven sharing lead to different sharing decisions? Q2: how are making their sharing decisions? what privacy strategies are used in social-driven sharing? Q3: are people making good choices? do people’s preferences result in privacy-preserving choices? 14
    • User Study: Participants 2-week user study 9 participants, 3 female 18-46 years old (μ=27.1, σ=8.3) ⅔ undergrad & grad students, ⅓ staff 15
    • User Study: Part 1 (in the field) participants given custom Nokia N95s • treated as primary phone collected continuous GPS traces extracted significant places • dwell time ≥ 5 mins 16
    • User Study: Part 2 (in the lab) 1. shown a map of each place 2. generate as many labels as possible 17 [sample labeling exercise given to everyone as training] Heinz Field Football field Steelers vs. Bengals downtown Pittsburgh Steelers’ home 100 Art Rooney Ave Near golden triangle
    • User Study: Part 2 (in the lab) purpose-driven scenario: social-driven scenario: 18
    • User Study: Part 2 (in the lab) purpose-driven scenario: social-driven scenario: 19
    • Analysis: Taxonomy coded each label: 20 Heinz Field Football field Steelers vs Bengals downtown Pittsburgh Steelers’ home 100 Art Rooney Ave Near golden triangle
    • Analysis: Taxonomy coded each label: 21 Heinz Field Football field Steelers vs Bengals downtown Pittsburgh Steelers’ home 100 Art Rooney Ave Near golden triangle type of description example geographic 100 Art Rooney Ave Near Golden Triangle Downtown Pittsburgh semantic Heinz Field Steelers vs. Bengals Steelers’ home Football field hybrid Heinz Field @ downtown
    • Q1: What Do Users Share? [semantic] social sharing preferences: • more semantic labels* • fewer hybrid labels** social sharing had different semantic labels** • prefer activity & personal labels (“home”, “work”) • purpose-driven sharing preferred type of place & business names (“coffee shop”, “Starbucks”) 22 *p<0.01 **p<0.005
    • Q2: How Do Users Decide? [blurring] insider knowledge “If I just say Giant Eagle [a regional grocery store chain], my friends will know which one I’m at.” sharing activity vs. location “I’d rather say what I am doing than that I’m at a certain place.” protecting friends’ locations “I’m uncomfortable sharing where I am at, since it’s someone else's place.” 23
    • Q2: How Do Users Share? [blurring intent] purpose-driven: used to convey unavailability social-driven: used to explicitly hide location 24
    • Q2: How Do Users Share? [blurring intent] purpose-driven: used to convey unavailability social-driven: used to explicitly hide location …but also considered: • social capital & image management • what would appear more interesting to others? 25
    • Q3: Do Users Make Good Choices? examine 3 techniques for reverse engineering • google maps • google search + google maps • routines + google search + google maps “bad” choice = physically locatable (stalker threat) 26
    • Result: Leaky Privacy Decisions purpose-driven: easily locatable social-driven: susceptible to being located 27 resource(s) purpose-driven social-driven map 50.0% 10.2% map + web 62.3% 19.4% map + web + routines 90.8% 51.0%
    • Summary & Conclusions reframing: purpose- vs. social-driven sharing significant differences for social sharing: • what: different types of disclosures [semantic] • how: different intentions for blurring [to hide] • how: considered social issues [impressions] • actual privacy: still susceptible to attacks 28
    • Summary & Conclusions reframing: purpose- vs. social-driven sharing significant differences for social sharing: • what: different types of disclosures [semantic] • how: different intentions for blurring [to hide] • how: considered social issues [impressions] • actual privacy: still susceptible to attacks  context for sharing is an important factor 29
    • Limitations & Future Work hypothetical disclosure scenarios small, homogenous participant pool • predominantly college students • already familiar social network users comparing two extremes of location sharing • many other types of possible location sharing • one-to-one vs. one-to-many purpose-driven • one-to-many vs. one-to-one social-driven 30
    • Questions? Karen P. Tang Human-Computer Interaction Institute School of Computer Science Carnegie Mellon University kptang@cs.cmu.edu This research has been supported in part by the National Science Foundation under grants CNS-0627513, IIS-0534406, and ITR-032535, by the CyLab at Carnegie Mellon University under grants DAAD19-02-1-0389 from the Army Research Office, by Nokia, by Portugal ICTI, and by a Microsoft Computational Thinking grant. 31