Abstract
Social Media (SM) has become a valuable information source to many in diverse situations. In IR, research has focused on real-time aspects and as such little is known about how long SM content is of value to users, if and how often it is re-accessed, the strategies people employ to re-access and if difficulties are experienced while doing so. We present results from a 5 month-long naturalistic, log-based study of user interaction with Twitter, which suggest re-finding to be a regular activity and that Tweets can offer utility for longer than one might think. We shed light on re-finding strategies revealing that remembered people are used as a stepping stone to Tweets rather than searching for content directly. Bookmarking strategies reported in the literature are used infrequently as a means to re-access. Finally, we show that by using statistical modelling it is possible to predict if a Tweet has future utility and is likely to be re-found. Our findings have implications for the design of social media search systems and interfaces, in particular for Twitter, to better support users re-find previously seen content.
Originalsprog | Udefineret/Ukendt |
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Titel | SIGIR '16 : Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information |
Antal sider | 10 |
Udgivelsessted | New York, NY, USA |
Forlag | Association for Computing Machinery (ACM) |
Publikationsdato | 2016 |
Sider | 355-364 |
ISBN (Trykt) | 978-1-4503-4069-4 |
DOI | |
Status | Udgivet - 2016 |
Begivenhed | Neu-IR ’16 SIGIR Workshop on Neural Information Retrieval - Pisa, Italien Varighed: 21 jul. 2016 → 21 jul. 2016 https://www.microsoft.com/en-us/research/event/neuir2016/ |
Workshop
Workshop | Neu-IR ’16 SIGIR Workshop on Neural Information Retrieval |
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Land/Område | Italien |
By | Pisa |
Periode | 21/07/2016 → 21/07/2016 |
Internetadresse |