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Using Public Social Media to Find Answers to Questions

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Talk given at the CSCW 2013 Workshop on Social Media Question Asking, February 2013

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Using Public Social Media to Find Answers to Questions

  1. 1. Using Public Social Media toFind Answers to QuestionsJeffrey NicholsWorkshop on Social Media Question Asking - CSCW 2013jwnichols@us.ibm.com @jwnichls
  2. 2. Asking questions on social media… What’s a good digital camera?
  3. 3. Can we leverage the large amount of datacreated by public social networks?
  4. 4. The Information Iceberg Information revealed through status updates Use the information we have to get the information we don’tUseful information known to members of social network
  5. 5. Examples
  6. 6. 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?
  7. 7. 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?
  8. 8. 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
  9. 9. 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?”
  10. 10. 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)
  11. 11. Results…
  12. 12. Suspended! • @tsatracking account (no incentive condition) given 1 week suspension after asking 150 questions • Did not violate Twitter Terms of Use • Exceeded threshold for blocks or message marked as spam • Neither of our other accounts were suspended
  13. 13. 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)
  14. 14. 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)
  15. 15. 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
  16. 16. 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
  17. 17. 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.?
  18. 18. 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
  19. 19. 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
  20. 20. 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]
  21. 21. 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
  22. 22. Thanks!For more information, contact:Jeffrey Nicholsjwnichols@us.ibm.com

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