# Deep Learning for Information Triage on Twitter
## 8 Conclusions  
In this paper, we firstly presented our study of user linguistic behaviors in tweets of two kinds: disaster tweets and election tweets. In particular, we focused on the time of the Great East Japan Earthquake and the Lower and the Upper House elections. As a basic idea, we assumed that people use SNS in decision-making or present status determination. Since it is important in such situations to automatically extract valid primary information, we firstly analyzed the earthquake tweets and defined rules for our classification criteria. Next, we classified election tweets based on these criteria and found out that even in primary information tweets there could be other information which causes cognitive bias in readers, and thus it is necessary to further divide factual information from opinionated information. We named this kind of information, neither being primary nor secondary information, though still preserving the structure of its own, \"sesquiary information\". This becomes especially important during the time of elections when people look for opinions about the candidates. However, it is also useful in the time of a disaster. Right after the disaster occurs, it is most important to quickly obtain only primary information. However, after the first emergency phase passes and people begin to look for appropriate information to support their further planning and decision-making, sesqutiary information gains significant importance as well. Therefore, we redefined the classification rules based on the analysis of the Great East Japan Earthquake tweets and the tweets for the General Elections to the House of Representatives. We used the new classification rules to reanalyze both types of data. Moreover, we gathered new data from the time of later election to the House of Councilors. We collected all tweets from 4 to 21 July appearing under the hashtag \"# Elections\" and accordingly analyzed a randomly selected sample of 2000 tweets. We were able to confirm the effectiveness of the re-defined classification rules. In a further investigation into the opinionated contents of primary information tweets, we found out that all those tweets were neutral. However, since some of them contained sesqutiary information, which could cause cognitive bias in readers, further post-processing of such cases is necessary. After estimating the potential of using Twitter as a source of information for decision-making and status determination, we developed a system using Twitter for information triage during major events, such as disasters. To build the system, we compared a number of classifiers, including the proposed one based on Deep Convolutional Neural Networks. We also validated 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 confirmed that the methods worked sufficiently well even when trained on data from nearly 10 years ago, which strongly suggests that the proposed model is well-generalized and sufficiently grasps important aspects of each type of classified information. On the basis of the proposed method for automatic extraction of information of high accuracy and uniformity, we used Twitter hashtags to collect messages related to the eruption of the Mt. Ontake volcano. We found that sesqutiary information appeared in large amounts. Moreover, after examining the change of occurrence of information in time, we confirmed that the ratio of information types does change with time. Thus, the classification criteria proposed in the hypothesis were valid. In the future, we plan to undertake further study of changes in time and changes according to situation (when users are in need of different kinds of information). We also plan to analyze expressions and patterns which indicate changes in user behavior. Finally, we plan to implement those improvements and test the method in practice. Conceptualization, M.P. and F.M.