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Social Query is a new and efficient way to get answers on the social networks. However, the popular method of sharing public questions could be optimized by directing the question to an expert, a process called query routing. In this work, we propose a Social Query System for query routing on Twitter, currently, one of the most popular social networks. The Social Query Systems analyzes the information about the questioner’s followers and recommends the most suitable users to answer the questions. The use of the system changes the usual process, working apart of Twitter and allowing questioner and responder exceed the limit of 140 characters. Through a qualitative evaluation, we showed promising results and ideas for improving the system and the recommendation algorithm.
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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
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
Cleyton Souza - ICIW 2013 2
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
Cleyton Souza - ICIW 2013 3
Agenda
• Introduction
• Related Work & Background
• Usual Q&A
• Social Query System: How it works
• Evaluation & Results
• Future Work
Cleyton Souza - ICIW 2013 4
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;
Cleyton Souza - ICIW 2013 5
Usual Q&A on OSN
• Sharing a public question
Fig. 1: Sharing a Public Question
Cleyton Souza - ICIW 2013 6
Q&A on OSN
• Directing the question
Fig. 2: Directing the Question
Cleyton Souza - ICIW 2013 7
Q&A on OSN
• Routing the question
Fig. 3: Routing the Question
Cleyton Souza - ICIW 2013 8
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
“New Question” Page
• Three text fields, two mandatory
Fig. 5: “New Question” Page Cleyton Souza - ICIW 2013 10
“Recommendation List” Page
• Questioner chooses who will “receive” the question
Fig. 6: “Recommendation List” Page
Cleyton Souza - ICIW 2013 11
Question’s Tweet
• Questioner tweets the following message
Fig. 7: Question’s Tweet
Cleyton Souza - ICIW 2013 12
“New Answer” Page
• Three options of answer
Fig. 8: “New Answer” Page Cleyton Souza - ICIW 2013 13
“I don’t Know” & “I know Someone”
Fig. 9: “I don’t know” Tweet
Fig. 10: “I know someone” Tweet
Cleyton Souza - ICIW 2013 14
I want answer
• When the expert clicks on the “I want answer” button
Fig. 11: “I want answer” Page Cleyton Souza - ICIW 2013 15
Tweeting about the Answer
Fig. 12: “I just answered” Tweet
Cleyton Souza - ICIW 2013 16
“New Evaluation” Page
Fig. 12: “New Evaluation” `Page
Cleyton Souza - ICIW 2013 17
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.
Cleyton Souza - ICIW 2013 18
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
Cleyton Souza - ICIW 2013 19
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
Cleyton Souza - ICIW 2013 20
Future Work
• Where are we?
• Mobile App
• Volunteer’s feedback
– Follow Back Filter
– Thesaurus
• Real case study
Cleyton Souza - ICIW 2013 21
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