Name
Abstract | ||
|---|---|---|
Social media like Twitter have become globally popular in the past decade. Thanks to the high penetration of smartphones, social media users are increasingly going mobile. This trend has contributed to foster various location based services deployed on social media, the success of which heavily depends on the availability and accuracy of users’ location information. However, only a very small fraction of tweets in Twitter are geo-tagged. Therefore, it is necessary to infer locations for tweets in order to attain the purpose of those location based services. In this paper, we tackle this problem by scrutinizing Twitter user timelines in a novel fashion. First of all, we split each user's tweet timeline temporally into a number of clusters, each tending to imply a distinct location. Subsequently, we adapt two machine learning models to our setting and design classifiers that classify each tweet cluster into one of the pre-defined location classes at the city level. The Bayes based model focuses on the information gain of words with location implications in the user-generated contents. The convolutional LSTM model treats user-generated contents and their associated locations as sequences and employs bidirectional LSTM and convolution operation to make location inferences. The two models are evaluated on a large set of real Twitter data. The experimental results suggest that our models are effective at inferring locations for non-geotagged tweets and the models outperform the state-of-the-art and alternative approaches significantly in terms of inference accuracy. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/tkde.2018.2852764 | IEEE Transactions on Knowledge and Data Engineering |
Keywords | Field | DocType |
Twitter,Urban areas,Adaptation models,Feature extraction,Hidden Markov models,Location awareness | Social media,Computer science,Inference,Location-based service,Feature extraction,Timeline,Artificial intelligence,Hidden Markov model,Location awareness,Machine learning,Bayes' theorem | Journal |
Volume | Issue | ISSN |
31 | 6 | 1041-4347 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pengfei Li | 1 | 3 | 1.71 |
| Hua Lu | 2 | 1380 | 83.74 |
| Nattiya Kanhabua | 3 | 346 | 26.35 |
| Sha Zhao | 4 | 48 | 9.96 |
| Gang Pan | 5 | 1501 | 123.57 |