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Social Query A Query Routing System for Twitter
Cleyton Souza Jonathas Magalhães, Evandro Costa, and Joseana Fechine
Laboratory of Artificial Intelligence – LIA
Federal University of Campina Grande - UFCG
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Introduction
• Query Routing (QR) is the process of directing questions to appropriate responders
– Community Question and Answering Services (CQA)
– Online Social Networks (OSN)
• We are proposing an Expertise Finding System to automatically routing questions on Social Networks
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Introduction
• Our goal is to present the Social Query System • How does it work?
• How does the usual Q&A process is affected?
• Talk about our preliminary results
• Talk about our planning for the future
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Agenda
• Introduction
• Related Work & Background
• Usual Q&A
• Social Query System: How it works
• Evaluation & Results
• Future Work
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Related Work & Background
• The differential of our research
– We are proposing a Query Routing to an OSN context
• Previous work usually focused on CQA context
• We are proposing a solution to a pre-existent and popular context: Twitter
• Most part of questions asked on Twitter are not answered (more than 80%) [Paul et al. 2012]
– We lead with the recommendation as multi-criteria decision making problem
• Previous work usually apply probabilistic or Information Retrieval-based models;
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Usual Q&A on OSN
• Sharing a public question
Fig. 1: Sharing a Public Question
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Q&A on OSN
• Directing the question
Fig. 2: Directing the Question
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Q&A on OSN
• Routing the question
Fig. 3: Routing the Question
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Social Query System
• Works outside Twitter
– Questioner’s Followers are Expert Candidates
– Questions and Answers without size limitations
Fig. 4: Social Query System’s Homepage Cleyton Souza - ICIW 2013 9
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“New Question” Page
• Three text fields, two mandatory
Fig. 5: “New Question” Page Cleyton Souza - ICIW 2013 10
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“Recommendation List” Page
• Questioner chooses who will “receive” the question
Fig. 6: “Recommendation List” Page
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Question’s Tweet
• Questioner tweets the following message
Fig. 7: Question’s Tweet
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“New Answer” Page
• Three options of answer
Fig. 8: “New Answer” Page Cleyton Souza - ICIW 2013 13
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“I don’t Know” & “I know Someone”
Fig. 9: “I don’t know” Tweet
Fig. 10: “I know someone” Tweet
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I want answer
• When the expert clicks on the “I want answer” button
Fig. 11: “I want answer” Page Cleyton Souza - ICIW 2013 15
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Tweeting about the Answer
Fig. 12: “I just answered” Tweet
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“New Evaluation” Page
Fig. 12: “New Evaluation” `Page
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How does it work?
• (1) The questioner accesses our System and (2) informs his question;
• (3) The System recommends potential responders and (4) the questioner chooses to whom direct the question;
• (5) Those chosen access our System, (6) answers the question, (7) and informs the questioner about his answer;
• (8) The questioner access our System, (9) see the answer, and (10) evaluates it.
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Evaluation
• Nine Volunteers evaluated ten recommendations for a couple of questions
a) Looking for a new band to listen during weekend, does anyone have an indication?
b) Going to the movie theater after years LOL. What is the best movie in theaters?
• Each recommendation was labeled as good (relevance 1), neutral (relevance 0) and bad (relevance 0).
• These labels reflect the opinion of the volunteers about the recommendation
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Results
Cases Amount of Followers
% of good %of bad nDCG
Best case for Question “a” 192 50% 10% 0.63
Worst case for Question “a” 129 30% 60% 0.25
Best case for Question “b” 121 60% 0% 0.74
Worst case for Question “b” 68 30% 0% 0.18
Average for Question “a” 110 41% 28% 0.41
Average for Question “b” 110 50% 20% 0.51
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Future Work
• Where are we?
• Mobile App
• Volunteer’s feedback
– Follow Back Filter
– Thesaurus
• Real case study
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Social Query System A System for Query Routing on Twitter
Cleyton Souza Jonathas Magalhães, Evandro Costa, and Joseana Fechine
Laboratory of Artificial Intelligence – LIA
Federal University of Campina Grande - UFCG
Thank You!
Lia TIPS Laboratory of Artificial
Intelligence Group of Intelligent Social and
Customizable Technologies