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Where might this be helpful? • Questions about an event that are best answered soon after the event • Questions for which there might be a diversity of opinion • More?
How feasible is this approach? • Will people answer questions from strangers? • Will use of an incentive increase responses? • What is the quality of the answers?
Concrete Prototype: TSA Tracker Crowdsourcing airport security wait times through TwitterStep #1. Watch for people http://tsatracker.org/tweeting about being @tsatracker , @tsatrackingin airportStep #2. Ask nicely if theywould share wait time tohelp othersStep #3. Collect responsesand share relevant dataon web siteStep #4. Say thank you! Key Question: Will people respond to questions from strangers? 8
QuestionsFrom @tsatracker (includes incentive) “If you went through security at <airport code>, can you reply with your wait time? Info will be used to help other travelers”From @tsatracking (no incentive) “If you went through security at <airport code>, can you reply with your wait time?”
Concrete Prototype: Product ReviewsStep 1. Identify owners of a product Key Questions:Step 2. Ask focused question about product Will people respond to questions in this different domain? • How is the image quality? • Does it take good low light pictures? Will people respond to follow-up • How quickly does it take a picture after pressing questions at the same rate? the shutter button? • How durable is it? Do responses contain useful & • What accessories are must haves? accurate information? • Etc…Step 3-4. Ask more questions if user respondsStep 5. Visualize results as structured product review (future work)
ResultsKey Question:Will people respond toquestions from strangers?Answer:42% response rate44% of answersreceived in 30 minsNo significant differencebetween any conditions(taking into account suspension)
Follow-up Question Results• Significant differences between all 4 questions (H=50.12, df=3, p < 0.0001, Kruskal-Wallis) and just the 3 follow-ups (H=25.46, df=2, p < 0.0001, Kruskal-Wallis)
Response Quality (Coding) Off-topicInfo per Relevant Answer Average Info per Response Count Multi- Message But Useful Info Wrong Answer Response Response ResponseOverallBreakdown Tablet 258 71% 19% 3% 1.82 0.48 Food Truck 111 82% 6% 6% 1.69 0.46 Thinks # Irrelevant No Didnt know wereIrrelevant Responses Experience or understand a botResponse Tablet 75 63% 11% 7%Breakdown Food Truck 20 25% 30% 0% For more, come see talk on Tuesday, 11am in Regency West 5
Qualitative Results • @tsatracker account picked up 16 followers • Many positive responses (“this will be great for travelers”) • Only one slightly negative response (“this is creepy”), but that person also gave an answer
What’s next? Can we build technology to support this process? • What do we need to know about potential answerers to better target questions? • Can we infer who will answer and who will not? Feasibility in other domains? • Influence, marketing, etc.?
Engagement Continuum qCrowd manual assisted automatic Send this: SendHumans do all the work Analytics streamline decisions: System-driven engagement “press button to engage”• Keyword filtering • Scenario-based filtering • Rule-based engagement• Unstructured engagement • Smart engagement recommendations • Exception identification• Domain-independent analytics (e.g., based on location inference) and notification • Customizable engagement scenarios • Intelligent transition to • Domain-specific analytics human-driven engagement as desired 18
User Modeling to Aid Engagement• Create an OmniProfile of each customer from social media Omni • Get to know each customer Profile as a unique individual• Employ OmniProfiles to external traits intrinsic traits better target messages Transaction history Personality • Try to ensure that only Web interaction Willingness Demographics Social relationships those who are willing are … … contacted
Modeling and Deriving Personality Map the use of words, frequency, & correlation with Big5 based on LIWC “Agreeableness” wonderful (0.28), together (0.26) … porn (-0.25), cost (-0.23) Openness Conscientiousness Extraversion Agreeableness Neuroticism [Tausczik&Pennebaker 2010, Yarkoni 2010]
To wrap up… • Interaction on social media enables a variety of applications • Data collection through real-time targeted question asking is a potentially useful application • Collecting information using this approach is feasible and produces quality information • We have built algorithms and technology to facilitate this approach
Thanks!For more information, contact:Jeffrey Nicholsjwnichols@us.ibm.com