Introduction - Twitter Data Analytics - page 3

To stay in agreement with Twitter's data sharing policies, some fields have been
removed from this dataset, and others have been modified. When collecting data
from the Twitter APIs in Chap. 2 , you will get raw data with unaltered values for all
of the fields.

1.3 Applying Twitter Data

Twitter's popularity as an information source has led to the development of
applications and research in various domains. Humanitarian Assistance and Disaster
Relief is one domain where information from Twitter is used to provide situational
awareness to a crisis situation. Researchers have used Twitter to predict the
occurrence of earthquakes [ 5 ] and identify relevant users to follow to obtain disaster
related information [ 1 ]. Studies of Twitter's use in disasters include regions such as
China [ 4 ], and Chile [ 2 ].
While a sampled view of Twitter is easily obtained through the APIs discussed
in this topic, the full view is difficult to obtain. The APIs only grant us access to
a 1 % sample of the Twitter data, and concerns about the sampling strategy and the
quality of Twitter data obtained via the API have been raised recently in [ 3 ]. This
study indicates that care must be taken while constructing the queries used to collect
data from the Streaming API.

References

1. S. Kumar, F. Morstatter, R. Zafarani, and H. Liu. Whom Should I Follow? Identifying Relevant
Users During Crises. In Proceedings of the 24th ACM conference on Hypertext and social media .
ACM, 2013.
2. M. Mendoza, B. Poblete, and C. Castillo. Twitter Under Crisis: Can we Trust What We RT? In
Proceedings of the First Workshop on Social Media Analytics , 2010.
3. F. Morstatter, J. Pfeffer, H. Liu, and K. Carley. Is the Sample Good Enough? Comparing Data
from Twitter's Streaming API with Twitter's Firehose. In International AAAI Conference on
Weblogs and Social Media , 2013.
4. Y. Qu, C. Huang, P. Zhang, and J. Zhang. Microblogging After a Major Disaster in China:
A Case Study of the 2010 Yushu Earthquake. In Computer Supported Cooperative Work and
Social Computing , pages 25-34, 2011.
5. T. Sakaki, M. Okazaki, and Y. Matsuo. Earthquake Shakes Twitter Users: Real-Time Event
Detection by Social Sensors. In Proceedings of the 19th international conference on World wide
web , pages 851-860. ACM, 2010.