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Eddi: interactive topic-based browsing of social status streams

Published: 03 October 2010 Publication History

Abstract

Twitter streams are on overload: active users receive hundreds of items per day, and existing interfaces force us to march through a chronologically-ordered morass to find tweets of interest. We present an approach to organizing a user's own feed into coherently clustered trending topics for more directed exploration. Our Twitter client, called Eddi, groups tweets in a user's feed into topics mentioned explicitly or implicitly, which users can then browse for items of interest. To implement this topic clustering, we have developed a novel algorithm for discovering topics in short status updates powered by linguistic syntactic transformation and callouts to a search engine. An algorithm evaluation reveals that search engine callouts outperform other approaches when they employ simple syntactic transformation and backoff strategies. Active Twitter users evaluated Eddi and found it to be a more efficient and enjoyable way to browse an overwhelming status update feed than the standard chronological interface.

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    cover image ACM Conferences
    UIST '10: Proceedings of the 23nd annual ACM symposium on User interface software and technology
    October 2010
    476 pages
    ISBN:9781450302715
    DOI:10.1145/1866029
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    Published: 03 October 2010

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    1. social streams
    2. topic clustering
    3. twitter

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    • (2022)Query-specific subtopic clusteringProceedings of the 22nd ACM/IEEE Joint Conference on Digital Libraries10.1145/3529372.3530923(1-9)Online publication date: 20-Jun-2022
    • (2022)RumorLens: Interactive Analysis and Validation of Suspected Rumors on Social MediaExtended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491101.3519712(1-7)Online publication date: 27-Apr-2022
    • (2021)TwiCS: Lightweight Entity Mention Detection in Targeted Twitter StreamsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.3088716(1-1)Online publication date: 2021
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    • (2020)Topic representation model based on microblogging behavior analysisWorld Wide Web10.1007/s11280-020-00822-xOnline publication date: 15-Jun-2020
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    • (2019)Framework for Personalized Content Recommendations to Support Informal Learning in Massively Diverse Information WikisIEEE Access10.1109/ACCESS.2019.29562847(172752-172773)Online publication date: 2019
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