<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bidur Devkota</style></author><author><style face="normal" font="default" size="100%">Hiroyuki Miyazaki</style></author><author><style face="normal" font="default" size="100%">Niraj Pahari</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Utilizing User Generated Contents to describe Tourism Areas of Interest</style></title><secondary-title><style face="normal" font="default" size="100%">2019 First International Conference on Smart Technology &amp; Urban Development (STUD)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Flickr</style></keyword><keyword><style  face="normal" font="default" size="100%">TFIDF</style></keyword><keyword><style  face="normal" font="default" size="100%">Tourism Area of Interest</style></keyword><keyword><style  face="normal" font="default" size="100%">Twitter</style></keyword><keyword><style  face="normal" font="default" size="100%">User Generated Contents</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">12/2019</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://ieeexplore.ieee.org/document/9018810</style></url></web-urls></urls><pub-location><style face="normal" font="default" size="100%">Chiang Mai, Thailand</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">B. Devkota</style></author><author><style face="normal" font="default" size="100%">H. Miyazaki</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">An Exploratory Study on the Generation and Distribution of Geotagged Tweets in Nepal</style></title><secondary-title><style face="normal" font="default" size="100%">2018 IEEE 3rd International Conference on Computing, Communication and Security (ICCCS)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">active user locations</style></keyword><keyword><style  face="normal" font="default" size="100%">clustering</style></keyword><keyword><style  face="normal" font="default" size="100%">Conferences</style></keyword><keyword><style  face="normal" font="default" size="100%">data mining</style></keyword><keyword><style  face="normal" font="default" size="100%">geotagged tweets</style></keyword><keyword><style  face="normal" font="default" size="100%">hotspots</style></keyword><keyword><style  face="normal" font="default" size="100%">human information behaviors</style></keyword><keyword><style  face="normal" font="default" size="100%">Kernel</style></keyword><keyword><style  face="normal" font="default" size="100%">live human sensors</style></keyword><keyword><style  face="normal" font="default" size="100%">Media</style></keyword><keyword><style  face="normal" font="default" size="100%">microblogging platform</style></keyword><keyword><style  face="normal" font="default" size="100%">Nepal</style></keyword><keyword><style  face="normal" font="default" size="100%">pattern clustering</style></keyword><keyword><style  face="normal" font="default" size="100%">Security</style></keyword><keyword><style  face="normal" font="default" size="100%">social media</style></keyword><keyword><style  face="normal" font="default" size="100%">social media platforms</style></keyword><keyword><style  face="normal" font="default" size="100%">social networking (online)</style></keyword><keyword><style  face="normal" font="default" size="100%">spatial clustering</style></keyword><keyword><style  face="normal" font="default" size="100%">spatial distribution</style></keyword><keyword><style  face="normal" font="default" size="100%">spatial patterns</style></keyword><keyword><style  face="normal" font="default" size="100%">spatial penetration</style></keyword><keyword><style  face="normal" font="default" size="100%">spatiotemporal patterns</style></keyword><keyword><style  face="normal" font="default" size="100%">spatiotemporal public opinion</style></keyword><keyword><style  face="normal" font="default" size="100%">time data</style></keyword><keyword><style  face="normal" font="default" size="100%">travel industry</style></keyword><keyword><style  face="normal" font="default" size="100%">tweet clusters</style></keyword><keyword><style  face="normal" font="default" size="100%">Twitter</style></keyword><keyword><style  face="normal" font="default" size="100%">twitter activities</style></keyword><keyword><style  face="normal" font="default" size="100%">twitter data</style></keyword><keyword><style  face="normal" font="default" size="100%">Urban areas</style></keyword><keyword><style  face="normal" font="default" size="100%">world wide web today</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2018</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Oct</style></date></pub-dates></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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.</style></abstract></record></records></xml>