Trivia quiz mining using probabilistic knowledge

Taesung Lee, Seungwon Hwang, Zhongyuan Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Recent work suggests that providing unexpected information is an important factor for drawing user traffic. Such examples can be easily found in the 'Did you know' section of the Wikipedia main page, the ESPN quiz, the Google Doodles, and the Bing main page. Inspired by these applications, we propose a novel trivia quiz mining asking unexpected questions for a given entity. We solve this problem by linking different types of social media as input and output, and mine unexpected properties based on prototype theory to mediate the input and the output media.

Original languageEnglish
Title of host publicationProceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016
EditorsRavi Kumar, James Caverlee, Hanghang Tong
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1392-1393
Number of pages2
ISBN (Electronic)9781509028467
DOIs
Publication statusPublished - 2016 Nov 21
Event2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016 - San Francisco, United States
Duration: 2016 Aug 182016 Aug 21

Publication series

NameProceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016

Other

Other2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016
CountryUnited States
CitySan Francisco
Period16/8/1816/8/21

Fingerprint

quiz
Wikipedia
social media
search engine
knowledge
traffic

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Sociology and Political Science
  • Communication

Cite this

Lee, T., Hwang, S., & Wang, Z. (2016). Trivia quiz mining using probabilistic knowledge. In R. Kumar, J. Caverlee, & H. Tong (Eds.), Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016 (pp. 1392-1393). [7752427] (Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ASONAM.2016.7752427
Lee, Taesung ; Hwang, Seungwon ; Wang, Zhongyuan. / Trivia quiz mining using probabilistic knowledge. Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016. editor / Ravi Kumar ; James Caverlee ; Hanghang Tong. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 1392-1393 (Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016).
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title = "Trivia quiz mining using probabilistic knowledge",
abstract = "Recent work suggests that providing unexpected information is an important factor for drawing user traffic. Such examples can be easily found in the 'Did you know' section of the Wikipedia main page, the ESPN quiz, the Google Doodles, and the Bing main page. Inspired by these applications, we propose a novel trivia quiz mining asking unexpected questions for a given entity. We solve this problem by linking different types of social media as input and output, and mine unexpected properties based on prototype theory to mediate the input and the output media.",
author = "Taesung Lee and Seungwon Hwang and Zhongyuan Wang",
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Lee, T, Hwang, S & Wang, Z 2016, Trivia quiz mining using probabilistic knowledge. in R Kumar, J Caverlee & H Tong (eds), Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016., 7752427, Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016, Institute of Electrical and Electronics Engineers Inc., pp. 1392-1393, 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016, San Francisco, United States, 16/8/18. https://doi.org/10.1109/ASONAM.2016.7752427

Trivia quiz mining using probabilistic knowledge. / Lee, Taesung; Hwang, Seungwon; Wang, Zhongyuan.

Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016. ed. / Ravi Kumar; James Caverlee; Hanghang Tong. Institute of Electrical and Electronics Engineers Inc., 2016. p. 1392-1393 7752427 (Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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N2 - Recent work suggests that providing unexpected information is an important factor for drawing user traffic. Such examples can be easily found in the 'Did you know' section of the Wikipedia main page, the ESPN quiz, the Google Doodles, and the Bing main page. Inspired by these applications, we propose a novel trivia quiz mining asking unexpected questions for a given entity. We solve this problem by linking different types of social media as input and output, and mine unexpected properties based on prototype theory to mediate the input and the output media.

AB - Recent work suggests that providing unexpected information is an important factor for drawing user traffic. Such examples can be easily found in the 'Did you know' section of the Wikipedia main page, the ESPN quiz, the Google Doodles, and the Bing main page. Inspired by these applications, we propose a novel trivia quiz mining asking unexpected questions for a given entity. We solve this problem by linking different types of social media as input and output, and mine unexpected properties based on prototype theory to mediate the input and the output media.

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BT - Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016

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PB - Institute of Electrical and Electronics Engineers Inc.

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Lee T, Hwang S, Wang Z. Trivia quiz mining using probabilistic knowledge. In Kumar R, Caverlee J, Tong H, editors, Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 1392-1393. 7752427. (Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016). https://doi.org/10.1109/ASONAM.2016.7752427