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Many social media researchers and data scientists collected geo-tagged tweetsto conduct spatial analysis or identify spatiotemporal patterns of filteredmessages for specific topics or events. This paper provides a systematic viewto illustrate the characteristics (data noises, user biases, and system errors)of geo-tagged tweets from the Twitter Streaming API. First, we found that asmall percentage (1%) of active Twitter users can create a large portion (16%)of geo-tagged tweets. Second, there is a significant amount (57.3%) ofgeo-tagged tweets located outside the Twitter Streaming API's bounding box inSan Diego. Third, we can detect spam, bot, cyborg tweets (data noises) byexamining the "source" metadata field. The portion of data noises in geo-taggedtweets is significant (29.42% in San Diego, CA and 53.47% in Columbus, OH) inour case study. Finally, the majority of geo-tagged tweets are not created bythe generic Twitter apps in Android or iPhone devices, but by other platforms,such as Instagram and Foursquare. We recommend a multi-step procedure to removethese noises for the future research projects utilizing geo-tagged tweets.

Identifying Data Noises, User Biases, and System Errors in Geo-tagged Twitter Messages (Tweets)