# Deep Learning for Information Triage on Twitter
## 5 Analysis of Information Change Over Time  
The preliminary study (see Section 3) classified the tweet data related to the Great East Japan Earthquake and proposed the following hypothesis regarding the changes in information appearing in tweets over time. Figure 11: Visualization of classification results for Heavy rains tweets. Figure 12: Visualization of classification results for Typhoon tweets. \begin{table}
\begin{tabular}{l l r r r r r r} \hline \hline  & \multicolumn{3}{c}{**Classified as:**} & \multicolumn{1}{c}{**Precision**} & \multicolumn{1}{c}{**Recall**} & \multicolumn{1}{c}{**F1-Score**} & \multicolumn{1}{c}{**Accuracy**} \\ \hline  & **Ontake eruption** & **primary** & **secondary** & **sesquiary** & & & \\ \hline \multirow{3}{*}{**event**} & **primary** & 195 & 5 & 9 & 0.894 & 0.933 & 0.913 & \\  & **secondary** & 6 & 213 & 52 & 0.835 & 0.786 & 0.810 & \\  & **sesquiary** & 17 & 37 & 340 & 0.848 & 0.863 & 0.855 & \\ \hline \multicolumn{3}{c}{**Weighted average**} & \multicolumn{1}{c}{0.855} & \multicolumn{1}{c}{0.856} & \multicolumn{1}{c}{0.855} & \multicolumn{1}{c}{0.856} \\ \hline  & **Heavy rains** & **primary** & **secondary** & **sesquiary** & & & \\ \hline \multirow{3}{*}{**event**} & **primary** & 360 & 6 & 5 & 0.970 & 0.970 & 0.970 & \\  & **secondary** & 1 & 345 & 0 & 0.975 & 0.997 & 0.986 & \\  & **sesquiary** & 10 & 3 & 320 & 0.985 & 0.961 & 0.973 & \\ \hline \multicolumn{3}{c}{**Weighted average**} & \multicolumn{1}{c}{0.976} & \multicolumn{1}{c}{0.976} & \multicolumn{1}{c}{0.976} & \multicolumn{1}{c}{0.976} \\ \hline  & **Typhoon** & **primary** & **secondary** & **sesquiary** & & & \\ \hline \multirow{3}{*}{**event**} & **primary** & 361 & 4 & 6 & 0.973 & 0.973 & 0.973 & \\  & **secondary** & 4 & 342 & 0 & 0.983 & 0.988 & 0.986 & \\  & **sesquiary** & 6 & 2 & 325 & 0.982 & 0.976 & 0.979 & \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Confusion matrices and results of validation on new data. 1. **Sesquiary information grows right after the event occurs:** not directly related to the event, or related to some extent, but not posted by users who directly experienced the event. 2. **Secondary information grows over time:** some time after the event occurs, various kinds of information get mixed and the secondary information is used in the general spread of information. In the present research, we verified this hypothesis by analyzing a new set of tweets from the emergency situation of an eruption of a volcano in Japan in October 2014. In this section, we describe the results of analysis of tweet logs with the proposed system. The overall dataset contained 3706 tweets collected from 27 September 2014 (the day of eruption of Mt. Ontake) to 6 October 2014. From this dataset, we randomly extracted up to 100 tweets from each day (a total of 874 tweets). On October 5 and 6, the overall number of tweets did not exceed 100. As for the result of the classification, primary information was represented by 129 tweets (14.76%), sesqutiary information was represented by 513 tweets (58.70%), and secondary information was represented by 232 tweets (26.54%) (Figure 13). Among the primary information tweets, many contained content such as:  
\"Volcano fumes are approaching the Kuzo Pass. Volcanic ash has fallen here as well. My throat is itching. I am evacuating further.\"  
The sesqutiary information contained tweets such as:  
\"I hope the climbers descent safely. I hope the injured will be rescued quickly.\"  
Among secondary information, there were tweets such as:  
\"I heard there were some injured people.\"  
In the result, similarly to the preliminary study, sesqutiary information covered the largest amount of data, reaching 58.7% of all analyzed tweets. Moreover, similarly to the analysis of the tweets from the time of the Great East Japan Earthquake, sesqutiary information contained numerous tweets by people located at a further distance from the event, but interested in what was happening, such as \"I wonder what happened?\", or \"I hope everyone is alright\".