Repository logo
Communities & Collections
All of DSpace
  • English
  • العربية
  • বাংলা
  • Català
  • Čeština
  • Deutsch
  • Ελληνικά
  • Español
  • Suomi
  • Français
  • Gàidhlig
  • हिंदी
  • Magyar
  • Italiano
  • Қазақ
  • Latviešu
  • Nederlands
  • Polski
  • Português
  • Português do Brasil
  • Srpski (lat)
  • Српски
  • Svenska
  • Türkçe
  • Yкраї́нська
  • Tiếng Việt
Log In
Have you forgotten your password?
  1. Home
  2. Browse by Author

Browsing by Author "Sinha, T."

Filter results by typing the first few letters
Now showing 1 - 2 of 2
  • Results Per Page
  • Sort Options
  • No Thumbnail Available
    Item
    Impact of group norms in eliciting response in a goal driven virtual community
    (2013) Jain, S.; Sinha, T.; Shah, A.; Sharma, C.; Rose, C.
    With the proliferation of social media into our daily lives, online communities have become an important platform for collaborative learning and education. To connect users with varying knowledge levels and increase the net learning throughput, these communities often follow a question-answer based approach. Understanding what drives attention to help-seeking questions can reduce the amount of questions that go unnoticed or remain unanswered by the community. In this paper we discuss an important feature that affects the activity of the community, namely the community norms. We present a machine learning based trigger-driven feedback model that functions by (i) differentiating between help-seeking questions and follow-up posts - i.e. posts that are part of an ongoing discussion, and (ii) a dynamic intervention scheme to help improve question formulation. Our findings show that adhering to the community norms significantly increases the chance of eliciting a response.
  • No Thumbnail Available
    Item
    Impact of group norms in eliciting response in a goal driven virtual community
    (UHAMKA PRESS uhamkapress@yahoo.co.id, 2013) Jain, S.; Sinha, T.; Shah, A.; Sharma, C.; Rosé, C.
    With the proliferation of social media into our daily lives, online communities have become an important platform for collaborative learning and education. To connect users with varying knowledge levels and increase the net learning throughput, these communities often follow a question-answer based approach. Understanding what drives attention to help-seeking questions can reduce the amount of questions that go unnoticed or remain unanswered by the community. In this paper we discuss an important feature that affects the activity of the community, namely the community norms. We present a machine learning based trigger-driven feedback model that functions by (i) differentiating between help-seeking questions and follow-up posts - i.e. posts that are part of an ongoing discussion, and (ii) a dynamic intervention scheme to help improve question formulation. Our findings show that adhering to the community norms significantly increases the chance of eliciting a response.

Maintained by Central Library NITK | DSpace software copyright © 2002-2026 LYRASIS

  • Privacy policy
  • End User Agreement
  • Send Feedback
Repository logo COAR Notify