Twiage: a game for finding good advice on Twitter
Twiage: a game for finding good advice on Twitter
Millions of recommendations, opinions and experiences are shared across popular microblogging platforms and services each day. Yet much of this content becomes quickly lost in the stream shortly after being posted. This paper looks at the feasibility of identifying useful content in microblog streams so that it might be archived to facilitate wider access and reference. Towards this goal, we present an experiment with a game-with-a-purpose called Twiage that we designed to determine how well the deluge of content in “raw” microblog streams could be turned into filtered and ranked collections using ratings from players. Experiments with Twiage validate the feasibility of applying human-computation to this problem, finding strong agreement about what constitutes the “most useful” content in our test dataset. Second, we compare the effectiveness of various methods of eliciting such ratings, finding that a “choose-best” interface and Elo rating ranking scheme yield the greatest agreement in the fewest rounds. External validation of resulting top-rated twitter content with a domain expert found that while the top Twiageranked “tweets” were among the best of the set, there was a tendency for players to also select what we term “weak spam” – e.g., promotional content disguised as articles or reviews, indicating a need for more stringent content filtering.
Van Kleek, Max
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Smith, Daniel Alexander
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Stranders, Ruben
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schraefel, m.c.
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Van Kleek, Max
4d869656-cd47-4cdf-9a4f-697fa9ba4105
Smith, Daniel Alexander
8d05522d-e91e-4aa7-8972-e362e73f005c
Stranders, Ruben
cca79d07-0668-4231-a80f-5fae6617644c
schraefel, m.c.
ac304659-1692-47f6-b892-15113b8c929f
Van Kleek, Max, Smith, Daniel Alexander, Stranders, Ruben and schraefel, m.c.
(2012)
Twiage: a game for finding good advice on Twitter.
CHI2012 - Conference on Human Factors in Computing Systems, Austin, United States.
05 - 10 May 2012.
10 pp
.
(Submitted)
Record type:
Conference or Workshop Item
(Paper)
Abstract
Millions of recommendations, opinions and experiences are shared across popular microblogging platforms and services each day. Yet much of this content becomes quickly lost in the stream shortly after being posted. This paper looks at the feasibility of identifying useful content in microblog streams so that it might be archived to facilitate wider access and reference. Towards this goal, we present an experiment with a game-with-a-purpose called Twiage that we designed to determine how well the deluge of content in “raw” microblog streams could be turned into filtered and ranked collections using ratings from players. Experiments with Twiage validate the feasibility of applying human-computation to this problem, finding strong agreement about what constitutes the “most useful” content in our test dataset. Second, we compare the effectiveness of various methods of eliciting such ratings, finding that a “choose-best” interface and Elo rating ranking scheme yield the greatest agreement in the fewest rounds. External validation of resulting top-rated twitter content with a domain expert found that while the top Twiageranked “tweets” were among the best of the set, there was a tendency for players to also select what we term “weak spam” – e.g., promotional content disguised as articles or reviews, indicating a need for more stringent content filtering.
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Submitted date: May 2012
Venue - Dates:
CHI2012 - Conference on Human Factors in Computing Systems, Austin, United States, 2012-05-05 - 2012-05-10
Organisations:
Web & Internet Science
Identifiers
Local EPrints ID: 334518
URI: http://eprints.soton.ac.uk/id/eprint/334518
PURE UUID: 431f4603-6e88-4890-bdb9-f042b43ecd52
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Date deposited: 08 Mar 2012 11:48
Last modified: 15 Mar 2024 03:16
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Contributors
Author:
Max Van Kleek
Author:
Daniel Alexander Smith
Author:
Ruben Stranders
Author:
m.c. schraefel
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