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| Content Provider | ACM Digital Library |
|---|---|
| Author | Saravanan, M. |
| Abstract | Immigration is a driver of demographic change and finding present location information of the immigrant population is a challenging task for anyone looking to move to a new place. People tend to settle in a new place within their own community and actively seek out areas where they can be home with their own. Census data taken on once in several years is often unreliable to help the travelers due to the sheer gap between available details and the incessant movement of people in a short span of time. The proposed method leverages the power of social media to attain up-to-date information to understand the migration patterns of the regular travelers. Using the real time data provided via the public tweets on Twitter, deep learning algorithms are applied to classify the user's race and ethnicity. For this paper, we considered Twitter users of three different communities from certain states in the United States of America. Through the analysis of the large volume of tweets initially we have built a lexical knowledge base in the training phase. In our experiment, we have witnessed a deeper understanding of the users based on their race and ethnicity and we hope that the tool can help those looking to travel or settle abroad. It makes the task little bit easier for them by finding more people of their own community in a specific location to book the hotel nearby and enjoy their stay. |
| Starting Page | 1 |
| Ending Page | 10 |
| Page Count | 10 |
| File Format | |
| ISBN | 9781450348812 |
| DOI | 10.1145/3092090.3092100 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2017-07-17 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Ethnicity Social network analysis Convolutional neural network Twitter Immigrants |
| Content Type | Text |
| Resource Type | Article |
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