A city-wide examination of fine-grained human emotions through social media analysis.

The proliferation of Social Media and Open Web data has provided researchers with a unique opportunity to better understand human behavior at different levels. In this paper, we show how data from Open Street Map and Twitter could be analyzed and used to portray detailed Human Emotions at a city wide level in two cities, San Francisco and London. Neural Network classifiers for fine-grained emotions were developed, tested and used to detect emotions from tweets in the two cites.

Annotated dataset of history-related tweets.

In this article, we present a dataset containing history-related content obtained from social media. It contains hashtags and tweets that include these hashtags, as well as the results of third party tools applied to the tweets that include extracted entities, years, and url categories, and the categories for the history-related hashtags we used to crawl the tweets. We collected the tweets from Twitter official API using hashtag-based crawling.

Context-aware heatstroke relief station placement and route optimization for large outdoor events.

Background: Heatstroke is becoming an increasingly serious threat to outdoor activities, especially, at the time of large events organized during summer, including the Olympic Games or various types of happenings in amusement parks like Disneyland or other popular venues. The risk of heatstroke is naturally affected by a high temperature, but it is also dependent on various other contextual factors such as the presence of shaded areas along traveling routes or the distribution of relief stations. The purpose of the study is to develop a method to reduce the heatstroke risk of pedestrians for large outdoor events by optimizing relief station placement, volume scheduling and route.