@article{oai:kitami-it.repo.nii.ac.jp:02000449,
 author = {Michal Ptaszynski and Fumito Masui and Yuuto Fukushima and Yuuto Oikawa and Hiroshi Hayakawa and Yasunori Miyamori and Kiyoshi Takahashi and Shunzo Kawajiri},
 issue = {14},
 journal = {Applied Sciences},
 month = {2023-07-25},
 note = {In this paper, we present a Deep Learning-based system for the support of information
triaging on Twitter during emergency situations, such as disasters, or other influential events, such
as political elections. The system is based on the assumption that a different type of information
is required right after the event and some time after the event occurs. In a preliminary study, we
analyze the language behavior of Twitter users during two kinds of influential events, namely,
natural disasters and political elections. In the study, we analyze the credibility of information
included by users in tweets in the above-mentioned situations, by classifying the information into
two kinds: Primary Information (first-hand reports) and Secondary Information (second-hand reports,
retweets, etc.). We also perform sentiment analysis of the data to check user attitudes toward the
occurring events. Next, we present the structure of the system and compare a number of classifiers,
including the proposed one based on Convolutional Neural Networks. Finally, we validate the
system by performing an in-depth analysis of information obtained after a number of additional
events, including an eruption of a Japanese volcano Ontake on 27 September 2014, as well as heavy
rains and typhoons that occurred in 2020. We confirm that the methods works sufficiently well
even when trained on data from nearly 10 years ago, which strongly suggests that the model is
well-generalized and sufficiently grasps important aspects of each type of classified information.},
 pages = {6340--6340},
 title = {Deep Learning for Information Triage on Twitter},
 volume = {11},
 year = {}
}