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.

1. 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

2. 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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3. 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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4. Agenda
•
•
•
•
•
•
Introduction
Related Work & Background
Usual Q&A
Social Query System: How it works
Evaluation & Results
Future Work
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5. 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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6. Usual Q&A on OSN
• Sharing a public question
Fig. 1: Sharing a Public Question
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7. Q&A on OSN
• Directing the question
Fig. 2: Directing the Question
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8. Q&A on OSN
• Routing the question
Fig. 3: Routing the Question
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9. Social Query System
• Works outside Twitter
– Questioner’s Followers are Expert Candidates
– Questions and Answers without size limitations
Fig. 4: SocialCleyton Souza - ICIW 2013
Query System’s Homepage
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10. “New Question” Page
• Three text fields, two mandatory
Fig. 5: “New Question” Page
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11. “Recommendation List” Page
• Questioner chooses who will “receive” the
question
Fig. 6: “Recommendation List” Page
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12. Question’s Tweet
• Questioner tweets the following message
Fig. 7: Question’s Tweet
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13. “New Answer” Page
• Three options of answer
Fig. 8: “New Answer” Page
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14. “I don’t Know” & “I know Someone”
Fig. 9: “I don’t know” Tweet
Fig. 10: “I know someone” Tweet
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15. I want answer
• When the expert clicks on the “I want answer”
button
Fig. 11: “I want answer” Page
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16. Tweeting about the Answer
Fig. 12: “I just answered” Tweet
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17. “New Evaluation” Page
Fig. 12: “New Evaluation” `Page
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18. 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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19. Evaluation
• Nine Volunteers evaluated ten recommendations
for a couple of questions
a)
b)
Looking for a new band to listen during weekend, does anyone
have an indication?
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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20. Results
Cases
Best case for Question “a”
Amount of
Followers
192
% of good
%of bad
nDCG
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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21. Future Work
• Where are we?
• Mobile App
• Volunteer’s feedback
– Follow Back Filter
– Thesaurus
• Real case study
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22. Social Query System
A System for Query Routing on Twitter
Thank You!
Lia TIPS
Laboratory of Artificial
Intelligence
Group of Intelligent Social and
Customizable Technologies
Cleyton Souza
Jonathas Magalhães, Evandro Costa, and Joseana Fechine
Laboratory of Artificial Intelligence – LIA
Federal University of Campina Grande - UFCG