# A Method of Supplementing Reviews to Less-Known Tourist Spots Using Geotagged Tweets
## 1 Introduction
Primarily, there is a number of online review sites for tourism related activities, such as TripAdvisor ([[https://tripadvisor.com/](https://tripadvisor.com/)]([https://tripadvisor.com/](https://tripadvisor.com/)), accessed on 15 January 2022), Booking.com, or Expedia ([[https://www.expedia.com](https://www.expedia.com)]([https://www.expedia.com](https://www.expedia.com)), accessed on 15 January 2022). Unfortunately, less-known and rarely visited sightspots often do not accumulate sufficient number of valuable opinions. Therefore, to address this, we introduce the concept of using on-spot reviews (on-spot tweets with contents verified to contain visitor opinions). These are Internet opinions about the target spot extracted from geotagged tweets. To prove the adequateness of the extracted information we propose our classification method that uses a fine-tuned BERT model. Previously, [PERSON] et al. [6] introduced a method to identify on-site likelihood of tweets using a two-stage method, a rule based and contextual approach. Unlike them, in our proposed method we prove adequateness using a fine-tuned BERT model. Approved geotagged tweets are mapped as on-spot reviews in the designed system (PSRS). This is realized as efforts to cultivate newly Point Of Interest (POI) and to supplement additional information to the less-known places in the target spot (Serengeti and Ngorongoro) National Park (NP), which are famous and largest NP in northern Tanzania. Serengeti's annual great wildebeest migration is an iconic feature of the park which is happening around the end of year. The two parks are in the list of UNESCO World Heritage Sites with Serengeti NP property changing seamlessly to Ngorongoro Conservation Unit (see Figure 1 for details). The plains of Serengeti NP, comprising 1.5 million hectares of savanna, while the annual migration of two million wildebeests, with thousands of other ungulates in search of pasture and water, engage in a 1000 km long annual circular trespanning the two adjacent countries of Kenya and Tanzania. It is known to be one of the nature's most impressive spectacles ([[https://whc.unesco.org/en/list/156/](https://whc.unesco.org/en/list/156/)]([https://whc.unesco.org/en/list/156/](https://whc.unesco.org/en/list/156/)), accessed on 15 January 2022). The two spots together cover the area of more than twenty thousand square kilometers with many sightspots scattered around the area. Because of its wide area, some spots are less-known among sightseers than others and therefore rarely visited, thus accumulating few reviews. Additionally, the wildebeest migration is a famous but seasonal scenery across the target spot. Precise timing is entirely dependent upon the rainfall patterns each year. Hence, POI also differ periodically. Despite the fact that the migration and animal spot can be predicted, in this study, we take extra efforts to cultivate new POI pointed out in tweets by tourists. This is an important task as it can improve tourism activities of those target spots. Moreover, if the method is verified as effective, it can be applied also to other such attractive, yet not often visited sightseeing spots, all around the globe, in any country. Therefore, in this study, we propose a method of obtaining tourist on-spot reviews from the Internet to complement the least reviewed sightspots by extracting information directly from geotagged tweets. Tweets are considered geotagged if they include geolocation information assigned to it. We treat tweets that include the name of the target spot as potential tourist on-spot reviews. Results published in this paper represent an effort to complement reviews information for less-known places and rarely visited sightspots areas. Therefore, this article, by presenting a method to support less-known, yet valuable tourist attractions by cultivating on-spot reviews automatically with automatically collected and analyzed geotagged tweets, presents an important contribution for Tourism Informatics in general.