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  1. Proceedings of the 3rd international workshop on Search and mining user-generated contents (SMUC '11)
  2. ThemeCrowds: multiresolution summaries of twitter usage
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The challenge of understanding the flow of sentiments in social media documents
Trend-based and reputation-versed personalized news network
Improved answer ranking in social question-answering portals
Analysis of communities in social media
Predicting age and gender in online social networks
"I'm eating a sandwich in Glasgow": modeling locations with tweets
A comparative evaluation of personality estimation algorithms for the twin recommender system
Detection of near-duplicate user generated contents: the SMS spam collection
Characterizing Wikipedia pages using edit network motif profiles
Mining tweets for tag recommendation on social media
Mining tag similarity in folksonomies
ThemeCrowds: multiresolution summaries of twitter usage
On the generation of rich content metadata from social media

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ThemeCrowds: multiresolution summaries of twitter usage

Content Provider ACM Digital Library
Author Greene, Derek Cunningham, Pádraig Hurley, Neil Archambault, Daniel
Abstract Users of social media sites, such as Twitter, rapidly generate large volumes of text content on a daily basis. Visual summaries are needed to understand what groups of people are saying collectively in this unstructured text data. Users will typically discuss a wide variety of topics, where the number of authors talking about a specific topic can quickly grow or diminish over time, and what the collective is saying about the subject can shift as a situation develops. In this paper, we present a technique that summarises what collections of Twitter users are saying about certain topics over time. As the correct resolution for inspecting the data is unknown in advance, the users are clustered hierarchically over a fixed time interval based on the similarity of their posts. The visualisation technique takes this data structure as its input. Given a topic, it finds the correct resolution of users at each time interval and provides tags to summarise what the collective is discussing. The technique is tested on a large microblogging corpus, consisting of millions of tweets and over a million users.
Starting Page 77
Ending Page 84
Page Count 8
File Format PDF
ISBN 9781450309493
DOI 10.1145/2065023.2065041
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2011-10-28
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Cluster analysis Visualization Twitter
Content Type Text
Resource Type Article
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