@proceedings {113,
	title = {Utilizing User Generated Contents to describe Tourism Areas of Interest},
	year = {2019},
	month = {12/2019},
	address = {Chiang Mai, Thailand},
	abstract = {The use of available place databases (like GeoNamesand traditional maps) to obtain descriptive keywords of a user defined place is not possible because such data sources mainlymaintain location definitions of the well-known places only.Traditional sources may not be updated dynamically and maynot ensure diverse information. Additionally, they do not give anyinformation on the popularity, e.g., which is more popular amongthe places indexed by the same keyword. A bottom-up approach,based on real user attention, can address these problems. Wepropose a method to describe tourism area of interest (TAOI) byaggregating user generated social media text. We match the cooccurrence of important keywords in a particular location andselect such words to describe TAOIs. We applied the proposedmethod to data on micro blogging service Twitter and photosharing service Flickr and confirmed that our method made itpossible to extract TAOI description. The recommended bottomup approach enables the extraction of valuable information thatis not possible by using traditional top-down approaches.},
	keywords = {Flickr, TFIDF, Tourism Area of Interest, Twitter, User Generated Contents},
	url = {https://ieeexplore.ieee.org/document/9018810},
	author = {Bidur Devkota and Hiroyuki Miyazaki and Niraj Pahari}
}
@proceedings {103,
	title = {An Exploratory Study on the Generation and Distribution of Geotagged Tweets in Nepal},
	year = {2018},
	month = {Oct},
	abstract = {Social media platforms contribute a huge part of the content available on the world wide web today. These platforms act as a rich source of real time data from live human sensors. These media disseminate spatiotemporal public opinion regarding a range of events, activities and human information behaviors. This paper explores the active user locations and spatial penetration of popular microblogging platform, Twitter, in Nepal. A heatmap visualization is used to show the intensity and distribution of the spatial patterns of Twitter activities in different parts of Nepal. Clustering is a popular technique for knowledge discovery, so spatial clustering is applied to groups tweets spatially into different classes. Such spatial clustering helps in the identification of areas of similar twitter activities and shows the distribution of the spatial patterns in different parts of Nepal. Tweet clusters are observed mainly in the main cities and the tourism centers. Further, an examination of the twitter data shared by the local Nepalese people and the foreigners are shown. This study contributes the research line by providing insights to better understand the spatiotemporal patterns and hotspots of tweets in Nepal. Such patterns and hotspots have an immense practical value that can be attributable to a place in order to derive meaningful insights related to various domains like a disease, crime, tourism, etc.},
	keywords = {active user locations, clustering, Conferences, data mining, geotagged tweets, hotspots, human information behaviors, Kernel, live human sensors, Media, microblogging platform, Nepal, pattern clustering, Security, social media, social media platforms, social networking (online), spatial clustering, spatial distribution, spatial patterns, spatial penetration, spatiotemporal patterns, spatiotemporal public opinion, time data, travel industry, tweet clusters, Twitter, twitter activities, twitter data, Urban areas, world wide web today},
	doi = {10.1109/CCCS.2018.8586827},
	author = {B. Devkota and H. Miyazaki}
}
