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Who will retweet this?: Automatically Identifying and Engaging Strangers on Twitter to Spread Information

Lee, Kyumin ; Mahmud, Jalal ; Chen, Jilin ; Zhou, Michelle ; Nichols, Jeffrey

Proceedings of the 19th international conference on intelligent user interfaces, 24 February 2014, pp.247-256

ISBN: 9781450321846 ; ISBN: 1450321844 ; DOI: 10.1145/2557500.2557502

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  • Nhan đề:
    Who will retweet this?: Automatically Identifying and Engaging Strangers on Twitter to Spread Information
  • Tác giả: Lee, Kyumin ; Mahmud, Jalal ; Chen, Jilin ; Zhou, Michelle ; Nichols, Jeffrey
  • Chủ đề: Personality ; Retweet ; Social Media ; Twitter ; Willingness ; Computer Science
  • Là 1 phần của: Proceedings of the 19th international conference on intelligent user interfaces, 24 February 2014, pp.247-256
  • Mô tả: There has been much effort on studying how social media sites, such as Twitter, help propagate information in different situations, including spreading alerts and SOS messages in an emergency. However, existing work has not addressed how to actively identify and engage the right strangers at the right time on social media to help effectively propagate intended information within a desired time frame. To ad-dress this problem, we have developed two models: (i) a feature-based model that leverages peoplesfi exhibited social behavior, including the content of their tweets and social interactions, to characterize their willingness and readiness to propagate information on Twitter via the act of retweeting; and (ii) a wait-time model based on a user's previous retweeting wait times to predict her next retweeting time when asked. Based on these two models, we build a recommender system that predicts the likelihood of a stranger to retweet information when asked, within a specific time window, and recommends the top-N qualified strangers to engage with. Our experiments, including live studies in the real world, demonstrate the effectiveness of our work.
  • Ngôn ngữ: English
  • Số nhận dạng: ISBN: 9781450321846 ; ISBN: 1450321844 ; DOI: 10.1145/2557500.2557502

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