# Predicting Location of Tweets Using Machine Learning Approaches
## 2 Related Works
### Geo-Location Prediction Approaches  
There are two main approaches to predict the location of a tweet based on its content, which are based on either using the content of the tweet alone or considering the geographical context. The first approach clusters data based on the density of geo-tagged tweets in various areas. For instance, a study applied a Gaussian Mixture Model (GMM) to produce geographic density evaluations for all n-grams contained in the tweet, and the predicted position is provided by the weighted sum of all the density estimates [24; 25]. Another study used an iterative GMM to predict the coordinate points of the geo-tagged tweets and found that an n-gram is geo-specific [27]. The center of the longest geo-specific n-gram is adopted as the predicted location if an ellipse can be built using the GMM that covers a predefined maximum area and contains a specific ratio of total tweets. The second approach transforms the geographical space into a grid of predefined regions. Some studies have built a language model for each area to predict the location [28; 29; 30; 31; 32]. The likelihood of a tweet being generated in an area is determined based on its relevance to the geo-tagged tweets in the area. The closest distance is returned as the predicted location. For example, a study divided the earth's geographical area into cells of about 111 km [32]. The researchers used multinomial naive Bayes (MNB) and Kullback-Leibler divergence functions to build the language model, including word counts as features. Another study used a Gaussian kernel instead of word counts to generate features [32]. This section provides a comprehensive overview of the related works in the field of tweet geo-location prediction. The importance of geographical information in tweets and the applications it enables is first explained. The main approaches for geo-location prediction section then describes the two main approaches used for geo-location prediction, based on either the content of the tweet or the geographical context. The strengths and limitations of each approach are discussed. [PERSON] et al. 's [29] use of a Recurrent Neural Network (RNN) for their language model is a strength of their approach. RNNs are well suited to capture temporal information, which is important in this context because the geographical context of a tweet is dependent on the time it was posted. This temporal information can help to improve the accuracy of location predictions. However, this approach also has limitations. As with the second approach in general, the accuracy of the predictions may be impacted by the size of the regions and the choice of the language model. Choosing a model that is well suited to the task at hand is crucial in achieving high accuracy. Recent research by [PERSON] et al. [33] and [PERSON] and [PERSON] [34] have also contributed to the field of tweet geo-location prediction. [PERSON] et al. (2022) used a combination of geographical and text-based features to create their predictions, which helped to improve the accuracy of their results. In their study, the authors found that incorporating both geographical and text-based features improved the accuracy of location predictions compared to when only text-based features were used. [PERSON] and [PERSON] (2021) used a deep learning approach to create their predictions. They found that their approach outperformed other existing methods, including those based on traditional machine learning algorithms. The authors concluded that deep learning approaches have the potential to significantly improve the accuracy of tweet geo-location predictions. In comparison to these recent research works, our current approach takes into account the geographical context of a tweet and its content to create predictions. This multi-faceted approach helps to improve the accuracy of our predictions, as well as to better understand the underlying factors that contribute to the geographical information of a tweet.