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Understanding experts' and novices' expertise judgment of twitter users

Published: 05 May 2012 Publication History

Abstract

Judging topical expertise of micro-blogger is one of the key challenges for information seekers when deciding which information sources to follow. However, it is unclear how useful different types of information are for people to make expertise judgments and to what extent their background knowledge influences their judgments. This study explored differences between experts and novices in inferring expertise of Twitter users. In three conditions, participants rated the level of expertise of users after seeing (1) only the tweets, (2) only the contextual information including short biographical and user list information, and (3) both tweets and contextual information. Results indicated that, in general, contextual information provides more useful information for making expertise judgment of Twitter users than tweets. While the addition of tweets seems to make little difference, or even add nuances to novices' expertise judgment, experts' judgments were improved when both content and contextual information were presented.

References

[1]
Canini, K. R., Suh, B., & Pirolli, P. L. (2011). Finding credible sources in Twitter based on relevance and social structure. In Proc. SocialCom 2011 (In Press).
[2]
Ericsson, K. A., Charness, N. Hoffman, R. & Fltovich. P., The Cambridge handbook of expertise and expert performance. New York: Cambridge University Press.
[3]
Pal, A., Counts, S. Identifying Topical Authorities in Microblogs. In Proc. WSDM 2011, ACM Press (2011).
[4]
Petty, R. E., Cacioppo, J. T, The Elaboration Likelyihood Model. Advances in Experimental Social Psychology 19. 123--205 (1986).
[5]
Weng, J., Lim, E., Jiang, J., He, Q., TwitterRank: finding topic-sensitive influential twitterers, In Proc. WSDM 2010, ACM Press. 261--270 (2010).

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    cover image ACM Conferences
    CHI '12: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
    May 2012
    3276 pages
    ISBN:9781450310154
    DOI:10.1145/2207676
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 05 May 2012

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    1. expertise judgment
    2. recommendation system
    3. twitter

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    • (2019)Opinion Formation in Online Social Networks: Exploiting Predisposition, Interaction, and CredibilityIEEE Transactions on Computational Social Systems10.1109/TCSS.2019.29142646:3(554-566)Online publication date: Jun-2019
    • (2018)Modelling majority and expert influences on opinion formation in online social networksWorld Wide Web10.1007/s11280-017-0484-721:3(663-685)Online publication date: 1-May-2018
    • (2017)Value and Misinformation in Collaborative Investing PlatformsACM Transactions on the Web10.1145/302748711:2(1-32)Online publication date: 4-May-2017
    • (2016)Inferring Your Expertise from Twitter: Integrating Sentiment and Topic Relatedness2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)10.1109/WI.2016.0027(121-128)Online publication date: Oct-2016
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    • (2015)Identification of Microblogs Prominent Users during Events by Learning Temporal Sequences of FeaturesProceedings of the 24th ACM International on Conference on Information and Knowledge Management10.1145/2806416.2806612(1715-1718)Online publication date: 17-Oct-2015
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