Recently, geolocalisation of tweets has become important for a wide range of real-time applications, including real-time event detection, topic detection or disaster and emergency analysis. However, the number of relevant geotagged tweets available to enable such tasks remains insufficient. To overcome this limitation, predicting the location of non-geotagged tweets, while challenging, can increase the sample of geotagged data and has consequences for a wide range of applications. In this paper, we propose a location inference method that utilises a ranking approach combined with a majority voting of tweets, where each vote is weighted based on evidence gathered from the ranking. Using geotagged tweets from two cities, Chicago and New York (USA), our experimental results demonstrate that our method (statistically) significantly outperforms state-ofthe-art baselines in terms of accuracy and error distance, in both cities, with the cost of decreased coverage. Finally, we investigated the applicability of our method in a real-time scenario by means of a traffic incident detection task. Our analysis shows that our fine-grained geolocalisation method can overcome the limitations of geotagged tweets and precisely map incident-related tweets at the real location of the incident.

Recently, the geolocalisation of tweets has become an important feature for a wide range of tasks in Information Retrieval and other domains, such as real-time event detection, topic detection or disaster and emergency analysis. However, the number of relevant geo-tagged tweets available remains insu cient to reliably perform such tasks. us, predicting the location of non-geotagged tweets is an important yet challenging task, which can increase the sample of geo-tagged data and help to a wide range of tasks. In this paper, we propose a location inference method that utilises a ranking approach combined with a majority voting of tweets weighted based on the credibility of its source (Twi er user). Using geo-tagged tweets from two cities, Chicago and New York (USA), our experimental results demonstrate that our method (statistically) signi cantly outperforms our baselines in terms of accuracy, and error distance, in both cities, with the cost of decrease in recall.

Background: With popular location-based services on smart phones, users are willing to leave comments on the business venues (e.g., restaurants, shops, bars, etc.) that they visited. Reviews of users on Yelp venues somewhat indicate satisfaction of customers with services of those venues. Those reviews could be used to reflect service quality of business venues. Geo-localized venues could tell researchers where and how good a business venue is. Methods: In terms of a spatial analysis of venues' ratings, this paper explored geographic patterns of ratings of Yelp business venues in a city-wide region. Specifically, we identified clusters of high and low ratings and explored spatial patterns of clusters of high ratings for different venue categories (i.e., restaurants, fast foods and bars). Results: In this study, we undertook an analysis of Yelp ratings in Phoenix, USA. The empirical results indicate that spatial clusters of high ratings tend to be differently distributed between different categories of Yelp venues. More specifically, bars within or near the city centre are likely to get high ratings. Moreover, although hot spots and cold spots of restaurants and fast foods both tend to be randomly distributed over space, spatial distribution of restaurants' ratings tends to be more similar to that of bars' ratings. Conclusion: Mapping Yelp's business venues with ratings provides a new way to understand spatial patterns of service quality of business or public venues at a large spatial scale.

Fine-grained geolocation of tweets has become an important feature for reliably performing a wide range of tasks such as real-time event detection, topic detection or disaster and emergency analysis. Recent work adopted a ranking approach to return a predicted location based on content-based similarity to already available individual geotagged tweets. However, this work made use of the IDF weighting model to compute the ranking, which can diminish the quality of the Top-N retrieved tweets. In this work, we adopt a learning to rank approach towards improving the effectiveness of the ranking and increasing the accuracy of fine-grained geolocalisation. To this end, we propose a set of features extracted from pairs of geotagged tweets generated within the same fine-grained geographical area (squared areas of size 1 km). Using geotagged tweets from two cities (Chicago and New York, USA), our experimental results show that our learning to rank approach significantly outperforms previous work based on IDF ranking, and improves the accuracy of tweet geolocalisation at a fine-grained level.