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Content Provider | ACM Digital Library |
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Author | Zafar, Muhammad Bilal Sharma, Naveen Ghosh, Saptarshi Gummadi, Krishna Bhattacharya, Parantapa Ganguly, Niloy |
Abstract | Several applications today rely upon content streams crowd-sourced from online social networks. Since real-time processing of large amounts of data generated on these sites is difficult, analytics companies and researchers are increasingly resorting to sampling. In this paper, we investigate the crucial question of how to sample the data generated by users in social networks. The traditional method is to randomly sample all the data. We analyze a different sampling methodology, where content is gathered only from a relatively small subset (< 1%) of the user population namely, the expert users. Over the duration of a month, we gathered tweets from over 500,000 Twitter users who are identified as experts on a diverse set of topics, and compared the resulting expert-sampled tweets with the 1% randomly sampled tweets provided publicly by Twitter. We compared the sampled datasets along several dimensions, including the diversity, timeliness, and trustworthiness of the information contained within them, and find important differences between the datasets. Our observations have major implications for applications such as topical search, trustworthy content recommendations, and breaking news detection. |
Starting Page | 1739 |
Ending Page | 1744 |
Page Count | 6 |
File Format | |
ISBN | 9781450322638 |
DOI | 10.1145/2505515.2505615 |
Language | English |
Publisher | Association for Computing Machinery (ACM) |
Publisher Date | 2013-10-27 |
Publisher Place | New York |
Access Restriction | Subscribed |
Subject Keyword | Sampling from experts Random sampling Twitter Sampling content streams Twitter lists |
Content Type | Text |
Resource Type | Article |
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