# Deep Learning for Information Triage on Twitter
## 6 System for Information Triage on Twitter  
In this section, we describe the structure of the proposed system for information triaging on Twitter.  
To create the system, we firstly developed classification criteria for the types of information appearing on Twitter, explained in previous sections. The classification criteria were based on the definitions of primary and secondary information and, as well as the additional type, namely, sesqutiary information (Table 4), which includes the user's opinions, sentiments, feelings, and so forth.  
These criteria were then used to collect the initial datasets (see Section 3), containing tweets representing each type of information. The datasets were used to train and test a classifier for the optimal performance in distinguishing between the three types of information.  
In the experimental phase, we used seven different classifiers for comparison with additional parameter modifications to chose the best-performing classifier. Moreover, we tested 11 methods of data preprocessing to further optimize the classifier performance. The final best-performing model was implemented in the proposed method.  
Next, we investigated the transition of information each day (Figure 14).  
Previous research [19] has hypothesized about the possible transition of information with time. We verified that in practice on the available data.  
Immediately after an event occurs (here: September 27), the primary information tweets start appearing and the number grows each time there is a movement or change in the event. On the 28 th, after most people have safely evacuated, the primary information decreases and the secondary information tweets gain in number.  
Later, around September 30 th, again, primary information gains in number. This is due to the fact that the official search for survivors has started, during which difficulties occurred in getting through the ruins and volcano fumes have fallen on the ground. In addition, there occurred a social and political fuss regarding the possible prediction of volcanic eruption reported in the news, which caused a growth in sesqutiary information, mostly consisting of opinions.  
The sesqutiary information grows largely immediately after the event occurs. This was most probably due to the fact that except the people who actually saw the event, many people could not understand what had happened and got confused.  
When it comes to secondary information, it is different by the event; however, it usually occurs in large quantities after some time has passed from the beginning of the event. Immediately after the event, there is not enough information sources for the secondary information to occur. Moreover, the general interest in the event itself is fading with time;  
Figure 14: Change in number of tweets each day.  
analyze classification errors. For this, we plan to apply the methodology of [36], who also reevaluated their system to reveal a major performance drop after two years. Fortunately, we have shown that the method works well on data from different periods of time; thus, this limitation is not expected to be extensive or sudden.  
Another limitation is related to the Twitter API used to collect the data, as well as for actual system application. Our reliance on official Twitter API enforces all limitations the the API possesses, such as a limited number of tweets allowed to collect per hour/day. The more strict the limitations are in the future, the more limited the developed system will become. Such limitations can be an inconvenience in general, but can also potentially mean that a rescue team can face a situation where no more tweets are allowed for extraction at a current day, and thus it is not possible to reach people in distress. Therefore, if the official Twitter API limitations become too severe to make the system reasonably applicable, we plan to either apply a third-party Twitter API, or in the worst case, change the SNS platform to a different venue.